Databases

Experience Actian Vector 7.0: A Faster, More Powerful Analytics Database

Dee Radh

October 22, 2024

Slow BI reporting and analytics speed indicate that your database is not performing at the high level needed to support modern analytics tools, applications, and real-time insights. This is not only a drag for data and IT teams that slows down their productivity, it can cripple your business growth. Slow time to insights has a ripple effect, delaying business decisions, missing opportunities, and losing a competitive advantage. It also undermines confidence in the data.

If you’re using an outdated database, you’re at a distinct business disadvantage. Legacy databases can’t keep up with:

Integrating diverse data sources and rapidly growing data volumes.
Processing workloads for real-time data analysis use cases.
Providing flexible and secure data deployments on-premises and in the cloud.

Actian Vector 7.0 Raises the Bar for Analytics

Real-time data analytics is a powerful differentiator for businesses seeking a competitive edge. Actian Vector delivers exactly that—and more. Now with the Vector 7.0 launch, querying large–even extremely large–data sets for analytics can be done in milliseconds, allowing database admins to maintain blazing-fast ingest rates for real-time analytics.

“Because Actian Vector can deliver extraordinary performance using only a small number of commodity compute nodes, the solution has exceeded the performance and functionality benchmarks of Netezza while lowering overall cost of ownership. By replacing its legacy technology, the bank estimates it will save $20 million over five years.”–Global Bank

What’s New in Vector 7.0

At Actian, our goal is simple: Make the Vector analytics database even better. We’ve empowered organizations across industries—including healthcare, transportation, retail, manufacturing, financial services, and more—to use the Vector analytics database for their most critical analytic workloads.

Organizations like KNMP (The Royal Dutch Pharmacists Association) rely on Actian Vector to unify data for pharmacies across the Netherlands, Sabre technology uses it to update transactions in 10-20ms, and IsCool Entertainment uses Vector to tailor offers and recommendations to their customers.

Like many of your peers, these companies demand more than a traditional database—they need a modern technology that can handle high-performance workloads to meet the demands of real-time analytics.

With 7.0, Actian Vector Delivers a Host of Upgrades

Our new release enables you to:

Drive greater performance and scalability to improve the speed and efficiency of data processing and reduce query response times. The database does this with:
- Auto Partitioning improves the efficiency of data processing. Optimized partitioning leads to faster query execution and better resource management, allowing users to focus on analysis rather than database tuning.
Increase developer productivity by speeding up developer cycles with tools to quickly test scenarios, create more responsive applications, and handle complex queries. You can benefit from:
- Developer SDK provides developers with the tools needed to create more responsive and scalable applications, catering to both large-scale enterprise requirements and real-time, low-latency environments. This ultimately speeds up development cycles and enhances product quality.
- Table Cloning enables users to quickly test scenarios, restore data to a prior state, and reduce storage costs. There is no additional storage cost incurred for the cloned tables.
- Advanced External Tables enhances the flexibility and scalability of the External Tables feature by allowing users to perform complex and customized data operations directly within their analytics workflows.
- Spark UDFs allow for complex computations while enabling advanced data transformations and analytics.
- REGEX Pattern Matching allows for more advanced search functionalities, enabling users to efficiently handle complex queries and improve data retrieval accuracy.
Power machine learning (ML) workloads to perform model inferencing for real-time workloads with a “bring your own pre-trained model” approach.
- ML Inference using TensorFlow streamlines the ML inference workflow, reducing data transfer time and enabling real-time analysis, leading to more timely and actionable insights.

Analyze Data No Matter Where it Resides

Vector can be deployed as an on-premises solution on Windows and Linux and as a private or managed cloud on Google Cloud, Amazon Web Services (AWS), and Microsoft Azure. You can also choose a hybrid approach. Organizations with sensitive workloads can realize the true potential of hybrid cloud by bringing compute power to the place where their data resides–both on-prem and in the cloud. You can leverage the same database engine, physical data model, ETL/ELT tools, and BI tools across clouds.

Want to experience Vector 7.0 today? Click here to request a personalized demo.

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.

Databases

Imagine New Possibilities With HCL Informix®

Nick Johnson

October 16, 2024

two people discussing the possibilities of hcl informix

Driving Business Success With HCL Informix®

HCL Informix delivers fast, reliable, and scalable transactions that drive mission-critical operations for small businesses and large enterprises, reducing friction and increasing business productivity. Thousands of forward-thinking organizations around the globe trust the HCL Informix brand to help them solve their toughest data challenges and transform how they power their businesses with data.

The HCL Informix Use Case Selection Guide is designed for developers, database administrators, and application product leaders looking to use HCL Informix to address a wide range of powerful business use cases, or modernize their existing ones. These examples feature challenges from real-world customer experiences to serve as a guide for understanding what is possible with HCL Informix:

Retail & Supply Chain Management

Rapid changes in product supply and demand make it difficult to understand how much product to make or keep on hand at any point in time. Additionally, external factors such as weather, global health crises, and natural disasters can create sudden shifts in the supply chain that can be difficult to manage.

Factory Maintenance

Factories rely on complex machinery and equipment to reach optimal productivity. Regular maintenance, including preventative maintenance, is crucial to prevent unexpected breakdowns, minimize downtime, and ensure optimal production.

Gaming & Gambling

Online gaming and gambling operators must handle millions of transactions, particularly during peak events like races and tournaments. To ensure an accurate ledger of these transactions, the database must efficiently manage high volumes of data while maintaining performance and accuracy.

Explore More Use Cases

Download The HCL Informix Use Case Selection Guide to explore more of the challenges businesses face and how HCL Informix can help resolve them.

> Get the eBook

For additional best practices, or to customize a strategy for your organization, connect with one of our Actian partners or specialists.

Informix is a trademark of IBM Corporation in at least one jurisdiction and are used under license.

About Nick Johnson

Nick Johnson is a Senior Product Marketing Manager at Actian, driving the go-to-market success for HCL Informix and Actian Zen. With a career dedicated to shaping compelling messages and strategies for databases, Nick brings a wealth of experience from his impactful work at leading technology companies, including Neo4j, Microsoft, and SAS.

Data Management

Get to Know the Value of the Actian Data Intelligence Platform

Ashley Knoble

October 4, 2024

The Actian Data Intelligence Platform is a cloud-native SaaS data discovery and metadata management solution that democratizes data access and accelerates your data-driven business initiatives. It is designed to help you efficiently find, understand, and trust enterprise data assets. As businesses like yours look to create and connect massive amounts of data from diverse sources, you need the ability to consolidate, govern, and make sense of that data to ensure confident decision-making and drive innovation.

The Actian platform is unique in the marketplace. It leverages a knowledge graph and automated processes to simplify the management of data and metadata while enhancing the overall user experience. At its core, the Actian Data Intelligence Platform functions as a smart data catalog to deliver a sophisticated solution that goes beyond basic data inventory. By utilizing a dynamic metamodel and advanced search capabilities, the platform lets you effectively explore, curate, and manage data assets across the organization.

5 Key Capabilities of the Actian Data Intelligence Platform

The game-changing data intelligence platform solves challenges such as managing the ever-increasing volume of data assets, meeting the needs of a growing number of data producers and data consumers, and closing the knowledge gap caused by a lack of data literacy in many organizations. It can connect to all of your data sources in seconds, less time than it took you to read this.

The platform offers capabilities that include:

Automated Metadata Management and Inventory

One of the platform’s standout features is its ability to automatically gather and manage metadata from different data sources. By leveraging built-in scanners, the platform runs through various databases, applications, and data storage systems to build an accurate inventory of data assets. This approach eliminates the need for manual input, reducing the likelihood of errors and ensuring that data inventories are always up to date.

For instance, the platform can automatically connect, consolidate, and link metadata from systems such as relational databases, file systems, cloud solutions, and APIs. This approach also allows the platform to generate valuable metadata insights such as data profiling, which helps identify patterns, top values, and distributions of null values within datasets.

Metamodeling for Flexibility and Scalability

Actian’s metamodel is the backbone of its flexibility. Unlike static data catalogs, the Actian Data Intelligence Platform allows you to create and evolve your metamodel based on your specific use cases. This means you can define new object classes or attributes as your data management needs grow.

As the platform scales, so does the metamodel, allowing for continuous adaptation and expansion of the data catalog. This flexibility is critical for businesses operating in fast-paced environments with ever-evolving data governance requirements.

Knowledge Graph-Driven Search and Discovery

The knowledge graph architecture is one of the most powerful features of the platform. It underpins the platform’s search engine, which allows you to navigate through complex datasets easily. Unlike traditional flat-index search engines, Actian’s search engine integrates natural language processing (NLP) and semantic analysis to provide more relevant and meaningful results.

This means you can quickly find the most relevant datasets, even when you aren’t exactly sure what you’re looking for. For instance, business analysts looking for customer data might not know the exact technical terms they need, but with Actian’s intuitive search, they can use everyday language to find the appropriate datasets.

Role-Based Interfaces: Actian Studio and Actian Explorer

These applications cater to different user needs. Actian offers two distinct interfaces:

- Actian Studio is designed for data stewards and administrators responsible for managing and curating data. The tool helps ensure the accuracy, completeness, and governance of the data within the catalog.
- Actian Explorer is a user-friendly interface tailored for business users or data consumers. It allows them to search, filter, and explore data assets with ease, without requiring deep technical knowledge.

This dual-interface approach ensures that each user type can interact with the platform in a way that suits their needs and role within your organization.

Security and Compliance

The platform is SOC 2 Type II certified and ISO 27001 compliant, meaning it meets the highest security standards required by industries such as banking, healthcare, and government. This makes the platform a trusted solution to manage sensitive data and for those doing business in heavily regulated sectors.

Sample Use Cases for the Actian Data Intelligence Platform

Organizations across industries can benefit from the data discovery capabilities offered by the Actian platform. Use cases include:

Data Governance for Financial Services

In the financial services sector, data governance is critical to ensure regulatory compliance and maintain operational efficiency. The Actian Data Intellligence Platform can be used to automate the documentation of data lineage, classify sensitive data, and ensure proper access controls are in place. Financial institutions can use the Actian Data Intelligence Platform’s metadata management to track the flow of data across various systems, ensuring full compliance with regulations such as GDPR.

Customer 360 Insights for Retailers

Retail businesses generate vast amounts of customer data across various channels, such as in-store purchases, online transactions, or marketing interactions. With the Actian Data Intelligence Platform, retailers can consolidate this data into a single source of truth, ensuring that business teams have the accurate, up-to-date data they need for customer analytics and to personalize marketing campaigns. The platform’s search and discovery capabilities allow marketing teams to easily find datasets related to customer behavior, preferences, and trends.

Improving Operational Efficiency for Healthcare

In healthcare, maintaining high data quality is essential for improving patient outcomes and complying with regulations. Hospitals and other healthcare organizations can use the Actian Data Intelligence Platform to govern and manage patient data, ensure data accuracy, and streamline reporting processes. Actian’s role-based interfaces make it easy for healthcare administrators to navigate complex datasets while ensuring sensitive information remains secure.

Scaling Data Discovery for Telecommunications

Telcos manage complex data ecosystems with data sources ranging from IoT devices to customer management systems. The platform’s ability to automate metadata management and its scalable metamodel gives telcos the ability to effectively track, manage, and discover data across their vast infrastructure. This ensures that data teams can quickly find operational data to improve services and identify areas for innovation.

The Value of Actian for Modern Businesses

Your business demands a holistic view of data assets to facilitate their effective use. This requires the data lineage and metadata management capabilities enabled by the Actian Data Intelligence Platform. The platform enables you to gain more value from your data by:

Enhancing Decision-Making

By providing a comprehensive overview of your data landscape, the Actian Data Intelligence Platform helps you make more informed decisions. The ability to quickly find and trust data means you can act faster and with greater confidence.

Improving Data Governance

Actian Data Intelligence Platform facilitates strong data governance by enabling you to automatically track data lineage, classify assets, and manage compliance requirements. This is particularly valuable in industries like finance and healthcare where regulations demand high levels of oversight and transparency.

Increasing Operational Efficiency

The platform’s automation capabilities free up valuable time for data stewards and administrators, allowing them to focus on higher-value tasks instead of manual data cataloging. This, in turn, reduces operational bottlenecks and improves the overall efficiency of data teams.

Future-Proofing Data Management

As you grow and your data needs evolve, Actian’s flexible architecture ensures that you can continue to scale your data catalog without running into limitations. The dynamic metamodel allows you to adapt to new use cases, technologies, and governance requirements as they emerge.

Build Trust in Your Data Assets

The Actian Data Intelligence Platform provides modern businesses like yours with a smart, scalable, and secure solution for data management and discovery. Its robust features, including automated metadata management, role-based interfaces, and advanced search capabilities, can give you confidence in data governance and discovery as well as your ability to fully optimize your data assets.

If you’re looking to improve operational efficiency, enhance decision-making, and ensure strong data governance, the Actian Data Intelligence Platform offers a modern platform to achieve these goals. Experience it for yourself with a personalized demo.

About Ashley Knoble

Ashley Knoble is Director of Strategic Alliances for Actian's East and Canadian regions, bringing 10+ years of expertise in business development and partner management. Ashley has excelled at SaaS solution growth, cyber security engagements, and partner ecosystems. She has a reputation for forging strong relationships that drive competitive growth. A frequent speaker at regional tech forums, Ashley also contributes to partner strategy Whitepapers. In her blog contributions, Ashley shares best practices for alliances in data management, network connectivity, and modern technologies. Check out her articles to learn how to cultivate strategic partnerships.

Data Management

Why Confidence in Data is Important for Business Growth

Actian Corporation

October 2, 2024

It’s no surprise to any of today’s business leaders that data technologies are experiencing unprecedented and rapid change. The rise of Artificial Intelligence (AI), its subset Generative AI (GenAI), machine learning, and other advanced technologies has enabled new and emerging opportunities at a pace never experienced before.

Yet with these opportunities comes a series of challenges such as navigating data privacy regulations, ensuring data quality and governance, and managing the increasing complexity of data integration across multiple systems. For modern organizations, staying ahead of these challenges hinges on one critical asset—data.

Data has become the lifeblood of innovation, strategy, and decision-making for forward-looking organizations. Companies that leverage data effectively can identify trends faster, make smarter decisions, and maintain a competitive edge. However, data in itself is not enough. To truly capitalize on its potential, organizations must have confidence in their data—which requires having data that’s trusted and easy to use.

What Does Data Confidence Mean?

At its core, confidence in data means trusting that the data informing decision-making is accurate, reliable, and timely. Without this assurance, data-driven insights can be flawed, leading to poor decision-making, missed opportunities, and distrust in the data.

Confidence in data comes from three key factors:

Data Quality

Poor data quality can lead to disastrous results. Whether it’s incomplete data, outdated or duplicated information, or inconsistent data values, low-quality data reduces the accuracy of insights and predictions. Ensuring decisions are based on accurate information requires data to be cleansed, validated, and maintained regularly. It should also be integrated organization-wide to avoid the pervasive problem of data silos.

Data Accessibility

Even if an organization has high-quality data, it’s of little use if it’s fragmented or difficult to access. For businesses to function effectively, they need a seamless flow of data across departments, systems, and processes. Ensuring data is accessible to all relevant stakeholders, applications, and systems is crucial for achieving operational efficiency and becoming a truly data-driven organization.

Data Integration

Today’s businesses manage an ever-growing volume of data from numerous sources, including customer data, transaction data, and third-party data. Without technology and processes in place to integrate all these data sets into a cohesive, single source of information, businesses face a disjointed view of their operations. A well-integrated data platform provides a unified view, enabling more strategic, insightful, and confident decision-making.

An Ever-Evolving Data Management Environment

As the business landscape shifts, the environment in which data is managed, stored, and analyzed also evolves. Traditional data management systems are no longer sufficient for handling the large volume, variety, and velocity of data bombarding modern organizations. That’s why today’s business environment demands modern, high-performance, scalable data solutions that can grow with them and meet their future needs.

The rise of cloud computing, AI, and edge computing has introduced new possibilities for businesses, but they have also added layers of complexity. To navigate this increasingly intricate ecosystem, businesses must be agile, capable of strategically adapting to new technologies while maintaining confidence in their data.

With the rapid pace of innovation, implementing new tools is not enough. Companies must also establish a strong foundation of trust in their data. This is where a modern data management solution becomes invaluable, enabling organizations to optimize the full power of their data with confidence.

Confidence in Technology: The Backbone of Innovation

Confidence isn’t just about the data—it extends to the various technologies that businesses rely on to process, analyze, and store that data. Businesses require scalable, flexible technology stacks that can handle growing workloads, perform a range of use cases, and adapt to changing demands.

Many organizations are transitioning to hybrid or multi-cloud environments to better support their data needs. These environments offer flexibility, enabling businesses to deploy data solutions that align with their unique requirements while providing the freedom to choose where data is stored and processed for various use cases.

Not surprisingly, managing these sophisticated ecosystems requires a high level of confidence in the underlying technology infrastructure. If the technology fails, data flow is disrupted, decisions are delayed, and business operations suffer. To prevent this, organizations require reliable systems that ensure seamless data management, minimize downtime, and maintain operational efficiency to keep the business running smoothly.

Confidence in technology also means investing in future-proof systems that can scale alongside the organization. As data volumes continue to grow, the ability to scale without sacrificing performance is critical for long-term success. Whether companies are processing operational data in real time or running complex analytical workloads, the technology must be robust enough to deliver consistent, high-quality results.

5 Steps to Build Confidence in Data

Ultimately, the goal of any data strategy is to drive better business outcomes. Data-driven decision-making has the power to transform how businesses operate, from improving customer experiences to optimizing supply chains to improving financial performance. Achieving these outcomes requires having confidence in the decisions themselves.

This is where analytics and real-time insights come into play. Organizations that can harness data for real-time analysis and predictions are better equipped to respond to market changes, customer needs, and internal challenges. The ability to make data-driven decisions with confidence allows businesses to innovate faster, streamline operations, and accelerate growth.

For organizations to trust their data and the systems that manage it, they need to implement a strategy focused on reliability, usability, and flexibility. Here are five ways businesses can build confidence in their data:

Invest in Data Quality Tools

Implementing data governance policies and investing in tools to clean and maintain data help ensure that information is accurate and reliable. Performing regular audits and monitoring can prevent data integrity issues before they impact decision-making.

Ensure Seamless Data Integration

Data from various sources must be integrated into a single, unified platform while maintaining quality. By breaking down silos and enabling smooth data flows, businesses can gain a holistic view of their operations, leading to more informed decisions.

Leverage Scalable Technology

Modern data platforms offer the flexibility to handle both current and future workloads. As business needs evolve, having a scalable system allows organizations to expand capacity without disrupting operations or sacrificing performance.

Empower All Departments With Data Accessibility

Data should be easily accessible to all teams and individuals who need it, not just data scientists or those with advanced IT skills. When everyone in the organization can leverage data without barriers, it fosters a culture of collaboration and innovation.

Adapt to Emerging Technologies

Staying ahead of technological advancements is key to maintaining a competitive edge. Businesses should evaluate new technologies like GenAI, machine learning, and edge computing to understand how they can enhance their data strategies.

Why Choose Actian for Your Data Needs?

For businesses navigating an era of exponential change, having confidence in their data and technology is essential for success. Actian can foster that confidence. As an industry leader with more than 50 years of experience, Actian is committed to delivering trusted, easy-to-use, and flexible solutions that meet the data management needs of modern organizations in any industry.

For example, the Actian Data Platform enables businesses to connect, govern, and analyze their data with confidence, ensuring they can make informed decisions that drive growth. With a unified, high-performance data platform and a commitment to innovation, Actian helps organizations turn challenges into opportunities and confidently embrace whatever is next.

Explore how Actian can help your business achieve data-driven success today.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

AI & ML

Exploring the Fundamental Truths of Generative AI

Steven B. Becker

October 1, 2024

fundamental truths of generative ai blog

In recent years, Generative AI has emerged as a revolutionary force in artificial intelligence, providing businesses and individuals with groundbreaking tools to create new data and content.

So, what exactly is GenAI? The concept refers to a type of artificial intelligence that is designed to generate new content rather than simply analyze or classify existing data. It leverages complex machine learning models to create outputs such as text, images, music, code, and even video by learning patterns from vast datasets.

Generative AI systems, like large language models (LLMs), use sophisticated algorithms to understand context, style, and structure. They can then apply this understanding to craft human-like responses, create art, or solve complex problems. These models are trained on enormous amounts of data, allowing them to capture nuanced patterns and relationships. As a result, they can produce outputs that are often indistinguishable from human-created content–and do it in a fraction of the time as humans.

The following survey conducted by TDWI shows that utilizing Generative AI is a major priority for companies in 2024. It ranks alongside other top initiatives like machine learning and upskilling business analysts, indicating that businesses are keen to explore and implement Generative AI technologies to enhance their analytics capabilities.

tdwi graph for analytics

Given that high level of priority, understanding five core truths around Generative AI helps to demystify its capabilities and limitations while showcasing its transformative potential:

Generative AI Uses Predictions to Generate Data

At its core, Generative AI leverages predictions made by deep learning algorithms to generate new data, as opposed to traditional AI models that use data to make predictions. This inversion of function makes Generative AI unique and powerful, capable of producing realistic images, coherent text, audio, or even entire datasets that have never existed before.

Example: Consider Generative Pre-trained Transformer, better known as GPT, models that predict the next word in a sentence based on the preceding words. With each prediction, these models generate fluid, human-like text, enabling applications like chatbots, content creation, and even creative writing. This capability is a radical shift from how traditional AI models simply analyze existing data to make decisions or classifications.

Why it Matters: The ability to generate data through predictive modeling opens the door to creative applications, simulation environments, and even artistic endeavors that were previously unimaginable in the AI world.

Generative AI is Built on Deep Learning Foundations

Generative AI stands on the shoulders of well-established deep learning algorithms such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models like GPT. These frameworks power the generation of realistic images, text, and other forms of content.

- GANs: Used extensively for creating high-quality images, GANs pit two networks against each other—a generator and a discriminator. The generator creates images, while the discriminator judges their quality, gradually improving the output.
- VAEs: These models enable the creation of entirely new data points by understanding the distribution of the data itself, often used in generative tasks involving audio and text.
- Transformers (GPT): The backbone of LLMs, transformers utilize self-attention mechanisms to handle large-scale text generation with impressive accuracy and fluency.

Why it Matters: These deep learning foundations provide the generative power to these models, enabling them to create diverse types of outputs. Understanding these algorithms also helps developers and AI enthusiasts choose the right architecture for their Generative AI tasks, whether for generating art, music, text, or something entirely different.

Generative AI Stands Out in Conversational Use Cases

A key strength of Generative AI is in applications where humans interact conversationally with AI systems. This differs from traditional AI and machine learning applications, which typically stand out in scenarios where the system is making decisions on behalf of humans. In Generative AI, dialogue-driven interactions come to the forefront.

Example: Chatbots powered by GPT models can converse with users in natural language, answering questions, providing recommendations, or even assisting in customer service. These models shine in areas where continuous interaction with users is essential for delivering valuable outputs.

Why it Matters: The conversational capability of Generative AI redefines user experiences. Instead of using structured, predefined outputs, users can ask open-ended questions and get context-aware responses, which makes interactions with machines feel more fluid and human-like. This represents a monumental leap in fields like customer service, education, and entertainment, where AI needs to respond dynamically to human inputs.

Generative AI Fosters “Conversations With Data”

One of the most exciting developments in Generative AI is its ability to let users have “conversations with data.” Through Generative AI, even non-technical users can interact with complex datasets and receive natural-language responses based on the data.

Example: Imagine a business analyst querying a vast dataset: Instead of writing SQL queries, the analyst simply asks questions in plain language (e.g., “What were the sales in Q3 last year?”). The generative model processes the query and produces accurate, data-driven answers—making analytics more accessible and democratized.

Why it Matters: By lowering the barrier to entry for data analysis, Generative AI makes it easier for non-technical users to extract insights from data. This democratization is a huge leap forward in industries like finance, healthcare, and logistics, where data-driven decisions are crucial, but data skills may be limited.

Generative AI Facilitates “Conversations With Documents”

Another pivotal truth about Generative AI is its capacity to facilitate “conversations with documents,” allowing users to access knowledge stored in vast repositories of text. Generative AI systems can summarize documents, answer questions, and even pull relevant sections from large bodies of text in response to specific queries.

Example: In a legal setting, a lawyer could use a Generative AI system to analyze large case files. Instead of manually combing through hundreds of pages, the lawyer could ask Generative AI to summarize key rulings, precedents, or legal interpretations, greatly speeding up research and decision-making.

Why it Matters: In industries where professionals deal with large amounts of documentation—such as law, medicine, or academia—the ability to have a “conversation” with documents saves valuable time and resources. By providing context-aware insights from documents, Generative AI helps users find specific information without wading through reams of text.

Changing How We Interact With Technology

These truths about Generative AI shed some light on the capabilities and potential of this groundbreaking technology. By generating data through predictions, leveraging deep learning foundations, and enabling conversational interactions with both data and documents, Generative AI is reshaping how businesses and individuals interact with technology.

As we continue to push the boundaries of Generative AI, it is crucial to understand how these truths will shape future applications, driving innovation across industries. Whether organizations are building chatbots, analyzing data, or interacting with complex documents, Generative AI stands as a versatile and powerful tool in the modern AI toolbox. To make sure an organization’s data is ready for Generative AI, get our checklist.

About Steven B. Becker

Steven B. Becker is Global Vice President of Solution Engineering at Actian, with over 20 years of technology experience. He has a history of helping Fortune 10 companies modernize apps, data, analytics, AI, and GenAI initiatives. Steven prioritizes bridging technology, people, and business. Steven has led successful transformations for both large enterprises and startups. His Actian blog posts explore modern app architectures, AI-driven insights, and enterprise data challenges. Dive into his articles for proven strategies on leveraging technology for growth.

Databases

The Essential Guide to Modernizing HCL Informix Applications

Nick Johnson

September 30, 2024

Organizations like yours face increasing pressure to modernize their legacy applications to remain competitive and meet customer needs. HCL Informix, a robust and reliable database platform, has been a cornerstone of many businesses for decades. Now, as technology advances and business needs change, HCL Informix can play a new role—helping you to reevaluate and modernize your applications.

In the HCL Informix Modernization Checklist, I outline four steps to planning your modernization journey:

Start building your business strategy.
Evaluate your existing Informix database environment.
Kick off your modernization project.
Learn, optimize, and innovate.

Throughout this modernization series, we will dedicate a blog to each of these steps, delving into the strategic considerations, technical approaches, and best practices so you can get your project started on the right track.

Start Building Your Business Strategy

Establish Your Application Modernization Objectives

The initial step in any application migration and modernization project is to clearly define the business problems you are trying to solve and optimize your project planning to best serve those needs. For example, you may be facing challenges with:

Security and compliance
Stability and reliability
Performance bottlenecks and scalability
Web and modern APIs
Technological obsolescence
Cost inefficiencies

By defining these parameters, you can set a clear objective for your migration and modernization efforts. This will guide your decision-making process and help in selecting the right strategies and technologies for a successful transformation.

Envision the End Result

Understanding the problem you want to address is crucial, but it’s equally important to develop a solution. Start by envisioning an ideal scenario. For instance, consider goals like:

Real-time responses
Scale to meet user demand
Update applications with zero downtime
Zero security incidents
100% connectivity with other applications
Deliver the project on time and on budget
Complete business continuity

Track Progress With Key Performance Indicators

Set key performance indicators (KPIs) to track progress toward your goals and objectives. This keeps leadership informed and motivates the team. Some sample KPIs might look like:

kpis for hcl informix

Identify the Capabilities You Want to Incorporate into Your Applications

With your vision in place, identify capabilities you wish to incorporate into your applications to help you meet your KPIs. Consider incorporating capabilities like:

Cloud computing.
Third-party solutions and microservices.
Orchestration and automation.
DevOps practices.
APIs for better integration.

Evaluate each capability and sketch an architecture diagram to determine if existing tools meet your needs. If not, identify new services required for your modernization project.

Get Your Modernization Checklist

For more best-practice approaches to modernizing your Informix applications, download the HCL Informix Modernization Checklist.

Get the Checklist >

Informix® is a trademark of IBM Corporation in at least one jurisdiction and is used under license.

About Nick Johnson

Data Management

Table Cloning: Create Instant Snapshots Without Data Duplication

Actian Corporation

September 27, 2024

What is Table Cloning?

Table Cloning is a database operation that makes a copy of an X100 table without the performance penalty of copying the underlying data. If you arrived here looking for the SQL syntax to clone a table in Actian Vector, it works like this:

CREATE TABLE newtable CLONE existingtable
[, newtable2 CLONE existingtable2, ...]
            [ WITH <option, option, ...> ];

The WITH options are briefly listed here. We’ll explain them in more detail later on.

WITH <option>
NODATA	Clone only the table structure, not its contents.
GRANTS	Also copy privileges from existing tables to new tables.
REFERENCES= NONE \| RESTRICTED \| EXTENDED	Disable creation of references between new tables (NONE), create references between new tables to match those between existing tables (RESTRICTED, the default), or additionally enable creation of references from new tables to existing tables not being cloned (EXTENDED).

The new table – the “clone” – has the same contents the existing table did at the point of cloning. The main thing to remember is that the clone you’ve created is just a table. No more, no less. It looks exactly like a copy. The new table may subsequently be inserted into, updated, deleted from, and even dropped, without affecting the original table, and vice versa.

In developing this feature, it became common to field questions like “Can you create a view on a clone?” or “Can you update a clone?” and “Can you grant privileges on a clone?” The answer, in all cases, is yes. It’s a table. If it helps, after you clone a table, you can simply forget that the table was created with the CLONE syntax. That’s what Vector does.

What Isn’t Table Cloning?

It’s just as important to recognize what Table Cloning is not. You can only clone an X100 table, all its contents or none of it, within the same database. You can’t clone only part of a table, or clone a table between two databases.

What’s it For?

With Table Cloning, you can make inexpensive copies of an existing X100 table. This can be useful to create and persist daily snapshots of a table that changes gradually over time, for example. These snapshots can be queried like any other table.

Users can also make experimental copies of sets of tables and try out changes on them, before applying those changes to the original tables. This makes it faster for users to experiment with tables safely.

How Table Cloning Works

In X100’s storage model, when a block of table data is written to storage, that block is never modified, except to be deleted when no longer required. If the table’s contents are modified, a new block is written with the new data, and the table’s list of storage blocks is updated to include the new block and exclude the old one.

table cloning block diagram

X100 catalog and storage for a one-column table MYTABLE, with two storage blocks.

There’s nothing to stop X100 creating a table that references another table’s storage blocks, as long as we know which storage blocks are still referenced by at least one table. So that’s what we do to clone a table. This allows X100 to create what looks like a copy of the table, without having to copy the underlying data.

In the image below, mytableclone references the same storage blocks as mytable does.

table cloning block diagram

X100 catalog and storage after MYTABLECLONE is created as a clone of MYTABLE.

Note that every table column, including the column in the new table, “owns” a storage file, which is the destination file for any new storage blocks for that column. So if new rows are added to mytableclone in the diagram above, the new block will be added to its own storage file:

table cloning block diagram

X100 catalog and storage after another storage block is added to MYTABLECLONE.

X100 tables can also have in-memory updates, which are applied on top of the storage blocks when the table is scanned. These in-memory updates are not cloned, but copied. This means a table which has recently had a large number of updates might not clone instantly.

My First Clone: A Simple Example

Create a table (note that on Actian Ingres, WITH STRUCTURE=X100 is needed to ensure you get an X100 table):

CREATE TABLE mytable (c1 INT, c2 VARCHAR(10)) WITH STRUCTURE=X100;

Insert some rows into it:

INSERT INTO mytable VALUES (1, 'one'), (2, 'two'), (3, 'three'), (4, 'four'), (5, 'five');

Create a clone of this table called myclone:

CREATE TABLE myclone CLONE mytable;

The tables now have the same contents:

SELECT * FROM mytable;

c1	c2
1	one
2	two
3	three
4	four
5	five

SELECT * FROM myclone;

c1	c2
1	one
2	two
3	three
4	four
5	five

Note that there is no further relationship between the table and its clone. The two tables can be modified independently, as if you’d created the new table with CREATE TABLE … AS SELECT …

UPDATE mytable SET c2 = 'trois' WHERE c1 = 3;
INSERT INTO mytable VALUES (6, 'six');
DELETE FROM myclone WHERE c1 = 1;

SELECT * FROM mytable;

c1	c2
1	one
2	two
3	trois
4	four
5	five
6	six

SELECT * FROM myclone;

c1	c2
2	two
3	three
4	four
5	five

You can even drop the original table, and the clone is unaffected:

DROP TABLE mytable;

SELECT * FROM myclone;

c1	c2
2	two
3	three
4	four
5	five

Security and Permissions

You can clone any table you have the privilege to SELECT from, even if you don’t own it.

When you create a table, whether by cloning or otherwise, you own it. That means you have all privileges on it, including the privilege to drop it.

By default, the privileges other people have on your newly-created clone are the same as if you created a table the normal way. If you want all the privileges other users were GRANTed on the existing table to be granted to the clone, use WITH GRANTS.

Metadata-Only Clone

The option WITH NODATA will create an empty copy of the existing table(s), but not the contents. If you do this, you’re not doing anything you couldn’t do with existing SQL, of course, but it may be easier to use the CLONE syntax to make a metadata copy of a group of tables with complicated referential relationships between them.

The WITH NODATA option is also useful on Actian Ingres 12.0. The clone functionality only works with X100 tables, but Actian Ingres 12.0 allows you to create metadata-only clones of non-X100 Ingres tables, such as heap tables.

Cloning Multiple Tables at Once

If you have a set of tables connected by foreign key relationships, you can clone them to create a set of tables connected by the same relationships, as long as you clone them all in the same statement.

For example, suppose we have the SUPPLIER, PART and PART_SUPP, defined like this:

CREATE TABLE supplier (
supplier_id INT PRIMARY KEY,
supplier_name VARCHAR(40),
supplier_address VARCHAR(200)
);

CREATE TABLE part (
part_id INT PRIMARY KEY,
part_name VARCHAR(40)
);

CREATE TABLE part_supp (
supplier_id INT REFERENCES supplier(supplier_id),
part_id INT REFERENCES part(part_id),
cost DECIMAL(6, 2)
);

If we want to clone these three tables at once, we can supply multiple pairs of tables to the clone statement:

CREATE TABLE
supplier_clone CLONE supplier,
part_clone CLONE part,
part_supp_clone CLONE part_supp;

We now have clones of the three tables. PART_SUPP_CLONE references the new tables SUPPLIER_CLONE and PART_CLONE – it does not reference the old tables PART and SUPPLIER.

Without Table Cloning, we’d have to create the new tables ourselves with the same definitions as the existing tables, then copy the data into the new tables, which would be further slowed by the necessary referential integrity checks. With Table Cloning, the database management system doesn’t have to perform an expensive referential integrity check on the new tables because their contents are the same as the existing tables, which have the same constraints.

WITH REFERENCES=NONE

Don’t want your clones to have references to each other? Then use WITH REFERENCES=NONE:

CREATE TABLE
supplier_clone CLONE supplier,
part_clone CLONE part,
part_supp_clone CLONE part_supp
WITH REFERENCES=NONE;

WITH REFERENCES=EXTENDED

Normally, the CLONE statement will only create references between the newly-created clones.

For example, if you only cloned PART and PART_SUPP:

CREATE TABLE
part_clone CLONE part,
part_supp_clone CLONE part_supp;

PART_SUPP_CLONE would have a foreign key reference to PART_CLONE, but not to SUPPLIER.

But what if you want all the clones you create in a statement to retain their foreign keys, even if that means referencing the original tables? You can do that if you want, using WITH REFERENCES=EXTENDED:

CREATE TABLE
part_clone CLONE part,
part_supp_clone CLONE part_supp
WITH REFERENCES=EXTENDED;

After the above SQL, PART_SUPP_CLONE would reference PART_CLONE and SUPPLIER.

Table Cloning Use Case and Real-World Benefits

The ability to clone tables opens up new use cases. For example, a large eCommerce company can use table cloning to replicate its production order database. This allows easier reporting and analytics without impacting the performance of the live system. Benefits include:

Reduced reporting latency. Previously, reports were generated overnight using batch ETL processes. Table cloning can create reports in near real-time, enabling faster decision-making. It can also be used to create a low-cost daily or weekly snapshot of a table which receives gradual changes.
Improved analyst productivity. Analysts no longer have to make a full copy of a table in order to try out modifications. They can clone the table and work on the clone instead, without having to wait for a large table copy or modifying the original.
Cost savings. A clone takes up no additional storage initially, because it only refers to the original table’s storage blocks. New storage blocks are written only as needed when the table is modified. Table cloning would therefore reduce storage costs compared to maintaining a separate data warehouse for reporting.

This hypothetical example illustrates the potential benefits of table cloning in a real-world scenario. By implementing table cloning effectively, you can achieve significant improvements in development speed, performance, cost savings, and operational efficiency.

Create Snapshot Copies of X100 Tables

Table Cloning allows the inexpensive creation of snapshot copies of existing X100 tables. These new tables are tables in their own right, which may be modified independently of the originals.

Actian Vector 7.0, available this fall, will offer Table Cloning. You’ll be able to easily create snapshots of table data at any moment, while having the ability to revert to previous states without duplicating storage. With this Table Cloning capability, you’ll be able to quickly test scenarios, restore data to a prior state, and reduce storage costs. Find out more.

About Actian Corporation

Data Management

Fundamentals of Edge-to-Cloud Data Management

Kunal Shah

September 26, 2024

Zen Edge Data Management 101 ebook cover

Over the last few years edge computing has progressed significantly, both in capability and availability, continuing a progressive trend of data management at the edge. According to a recent report, the number of Internet of Things (IoT) devices worldwide is forecast to almost double from 15.9 billion in 2023 to more than 32.1 billion IoT devices in 2030. However, during that time one thing has remained constant. There has been a need for good Edge-to-Cloud data management foundations and practices.

In this blog post, we will provide an overview of edge-to-cloud data management. We will explore the main concepts, benefits, and practical applications that can help you make the most of your data.

The Edge: Where Data Meets Innovation

At the heart of edge-to-cloud data management lies the edge – the physical location where data is generated. From sensors and IoT devices to wearable technology and industrial machinery, the edge is a treasure trove of real-time insights. By processing and analyzing data closer to its source, you can reduce latency, improve efficiency, and unlock new opportunities for innovation.

The Power of Real-Time Insights

Imagine the possibilities when you can access and analyze data in real-time. Whether you’re optimizing manufacturing processes, improving customer experiences, or making critical business decisions, real-time insights provide a competitive edge.

Predictive Maintenance: Prevent equipment failures and minimize downtime by analyzing sensor data to detect anomalies and predict potential issues.
Enhanced Customer Experiences: Personalize recommendations, optimize inventory, and provide exceptional service by leveraging real-time customer data.
Intelligent Operations: Optimize fleet management, streamline supply chains, and improve energy efficiency with real-time data-driven insights.

The Benefits of Edge-to-Cloud Data Management

By implementing an effective edge-to-cloud data management strategy, you can:

Reduce Latency and Improve Response Times: Process data closer to its source to make faster decisions.
Enhance Operational Efficiency: Optimize processes, reduce costs, and improve productivity.
Gain a Competitive Advantage: Unlock new opportunities for innovation and growth.
Improve Decision-Making: Make data-driven decisions based on real-time insights.
Ensure Data Privacy and Security: Protect sensitive data from unauthorized access and breaches.

Want to Learn More?

This blog post has only scratched the surface of the exciting world of edge-to-cloud data management. To dive deeper into the concepts, techniques, and best practices, be sure to download our comprehensive ebook – Edge Data Management 101.

Our eBook will cover:

The fundamentals of edge computing.
Best practices for edge data management.
Real-world use cases and success stories.
Security considerations and best practices.
The future of edge data management.

Don’t miss out on this opportunity to stay ahead of the curve. Download your free copy of our eBook today and unlock the power of real-time data at the edge.

About Kunal Shah

Kunal Shah is a product marketer with 15+ years in data and digital growth, leading marketing for Actian Zen Edge and NoSQL products. He has consulted on data modernization for global enterprises, drawing on past roles at SAS. Kunal holds an MBA from Duke University. Kunal regularly shares market insights at data and tech conferences, focusing on embedded database innovations. On the Actian blog, Kunal covers product growth strategy, go-to-market motions, and real-world commercial execution. Explore his latest posts to discover how edge data solutions can transform your business.

Databases

Build an IoT Smart Farm Using Raspberry Pi and Actian Zen

Johnson Varughese

September 26, 2024

Technology is changing every industry, and agriculture is no exception. The Internet of Things (IoT) and edge computing provide powerful tools to make traditional farming practices more efficient, sustainable, and data-driven. One affordable and versatile platform that can form the basis for such a smart agriculture system is the Raspberry Pi.

In this blog post, we will build a smart agriculture system using IoT devices to monitor soil moisture, temperature, and humidity levels across a farm. The goal is to optimize irrigation and ensure optimal growing conditions for crops. We’ll use a Raspberry Pi running Raspbian OS, Actian Zen Edge for database management, Zen Enterprise to handle the detected anomalies on the remote server database, and Python with the Zen ODBC interface for data handling. Additionally, we’ll leverage AWS SNS (Simple Notification Service) to send alerts for detected anomalies in real-time for immediate action.

Prerequisites

Before we start, ensure you have the following:

A Raspberry Pi running Raspbian OS.
Python installed on your Raspberry Pi.
Actian Zen Edge database installed.
PyODBC library installed.
AWS SNS set up with an appropriate topic and access credentials.

Step 1: Setting Up the Raspberry Pi

First, update your Raspberry Pi and install the necessary libraries:

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python3-pip
pip3 install pyodbc boto3

Step 2: Install Actian Zen Edge

Follow the instructions on the Actian Zen Edge download page to download and install Actian Zen Edge on your Raspberry Pi.

Step 3: Create Tables in the Database

We need to create tables to store sensor data and anomalies. Connect to your Actian Zen Edge database and create the following table:

CREATE TABLE sensor_data (
    id identity PRIMARY KEY,
    timestamp DATETIME,
    soil_moisture FLOAT,
    temperature FLOAT,
    humidity FLOAT
);

Install Zen Enterprise, connect to the central database, and create the following table:

 CREATE TABLE anomalies (
    id identity PRIMARY KEY ,
    timestamp DATETIME,
    soil_moisture FLOAT,
    temperature FLOAT,
    humidity FLOAT,
    description longvarchar
);

Step 4: Define the Python Script

Now, let’s write the Python script to handle sensor data insertion, anomaly detection, and alerting via AWS SNS.

Anomaly Detection Logic

Define a function to check for anomalies based on predefined thresholds:

def check_for_anomalies(data):
    threshold = {'soil_moisture': 30.0, 'temperature': 35.0, 'humidity': 70.0}
    anomalies = []
    if data['soil_moisture'] < threshold['soil_moisture']:
        anomalies.append('Low soil moisture detected')
    if data['temperature'] > threshold['temperature']:
        anomalies.append('High temperature detected')
    if data['humidity'] > threshold['humidity']:
        anomalies.append('High humidity detected')
    return anomalies

Insert Sensor Data

Define a function to insert sensor data into the database:

import pyodbc

def insert_sensor_data(data):
    conn = pyodbc.connect('Driver={Pervasive ODBC 
Interface};servername=localhost;Port=1583;serverdsn=demodata;')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO sensor_data (timestamp, soil_moisture, temperature, humidity) VALUES (?, ?, ?, ?)",
                   (data['timestamp'], data['soil_moisture'], data['temperature'], data['humidity']))
    conn.commit()
    cursor.close()
    conn.close()

Send Anomalies to the Remote Database

Define a function to send detected anomalies to the database:

def send_anomalies_to_server(anomaly_data):
    conn = pyodbc.connect('Driver={Pervasive ODBC Interface};servername=<remote server>;Port=1583;serverdsn=demodata;')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO anomalies (timestamp, soil_moisture, temperature, humidity, description) VALUES (?, ?, ?, ?, ?)",
                   (anomaly_data['timestamp'], anomaly_data['soil_moisture'], anomaly_data['temperature'], anomaly_data['humidity'], anomaly_data['description']))
    conn.commit()
    cursor.close()
    conn.close()

Send Alerts Using AWS SNS

Define a function to send alerts using AWS SNS:

def send_alert(message):
    sns_client = boto3.client('sns', aws_access_key_id='Your key ID',
    aws_secret_access_key ='Your Access key’, region_name='your-region')
    topic_arn = 'arn:aws:sns:your-region:your-account-id:your-topic-name'
    response = sns_client.publish(
        TopicArn=topic_arn,
        Message=message,
        Subject='Anomaly Alert'
    )
    return response

Replace your-region, your-account-id, and your-topic-name with your actual AWS SNS topic details.

Step 5: Generate Sensor Data

Define a function to simulate real-world sensor data:

import random
import datetime

def generate_sensor_data():
    return {
        'timestamp': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
        'soil_moisture': random.uniform(20.0, 40.0),
        'temperature': random.uniform(15.0, 45.0),
        'humidity': random.uniform(30.0, 80.0)
    }

Step 6: Main Function to Simulate Data Collection and Processing

Finally, put everything together in a main function:

def main():
    for _ in range(100):
        sensor_data = generate_sensor_data()
        insert_sensor_data(sensor_data)
        anomalies = check_for_anomalies(sensor_data)
        if anomalies:
            anomaly_data = {
                'timestamp': sensor_data['timestamp'],
                'soil_moisture': sensor_data['soil_moisture'],
                'temperature': sensor_data['temperature'],
                'humidity': sensor_data['humidity'],
                'description': ', '.join(anomalies)
            }
            send_anomalies_to_server(anomaly_data)
            send_alert(anomaly_data['description'])
if __name__ == "__main__":
    main()

Conclusion

And there you have it! By following these steps, you’ve successfully set up a basic smart agriculture system on a Raspberry Pi using Actian Zen Edge and Python. This system, which monitors soil moisture, temperature, and humidity levels, detects anomalies, stores data in databases, and sends notifications via AWS SNS, is a scalable solution for optimizing irrigation and ensuring optimal growing conditions for crops. Now, it’s your turn to apply this knowledge and contribute to the future of smart agriculture.

Remember to replace placeholders with your actual AWS SNS topic details and database connection details. Happy farming!

About Johnson Varughese

Johnson Varughese manages Support Engineering at Actian, assisting developers leveraging ZEN interfaces (Btrieve, ODBC, JDBC, ADO.NET, etc.). He provides technical guidance and troubleshooting expertise to ensure robust application performance across different programming environments. Johnson's wealth of knowledge in data access interfaces has streamlined numerous development projects. His Actian blog entries detail best practices for integrating Btrieve and other interfaces. Explore his articles to optimize your database-driven applications.

Data Architecture

Data Warehousing Demystified: From Basics to Advanced

Fenil Dedhia

September 24, 2024

data warehouse 101 blog image blue cubes

Table of Contents

Understanding the Basics of Data Warehousing

What is a Data Warehouse?

The Business Imperative of Data Warehousing

The Technical Role of Data Warehousing

Understanding the Differences: Databases, Data Warehouses, and Analytics Databases

The Human Side of Data: Key User Personas and Their Pain Points

Data Warehouse Use Cases For Modern Organizations

6 Common Business Use Cases

9 Technical Use Cases

Understanding the Basics of Data Warehousing

Welcome to data warehousing 101. For those of you who remember when “cloud” only meant rain and “big data” was just a database that ate too much, buckle up—we’ve come a long way. Here’s an overview:

What is a Data Warehouse?

Data warehouses are large storage systems where data from various sources is collected, integrated, and stored for later analysis. Data warehouses are typically used in business intelligence (BI) and reporting scenarios where you need to analyze large amounts of historical and real-time data. They can be deployed on-premises, on a cloud (private or public), or in a hybrid manner.

Think of a data warehouse as the Swiss Army knife of the data world – it’s got everything you need, but unlike that dusty tool in your drawer, you’ll actually use it every day!

Prominent examples include Actian Data Platform, Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure Synapse Analytics, and IBM Db2 Warehouse, among others.

Proper data consolidation, integration, and seamless connectivity with BI tools are crucial for a data strategy and visibility into the business. A data warehouse without this holistic view provides an incomplete narrative, limiting the potential insights that can be drawn from the data.

“Proper data consolidation, integration, and seamless connectivity with BI tools are crucial aspects of a data strategy. A data warehouse without this holistic view provides an incomplete narrative, limiting the potential insights that can be drawn from the data.”

The Business Imperative of Data Warehousing

Data warehouses are instrumental in enabling organizations to make informed decisions quickly and efficiently. The primary value of a data warehouse lies in its ability to facilitate a comprehensive view of an organization’s data landscape, supporting strategic business functions such as real-time decision-making, customer behavior analysis, and long-term planning.

But why is a data warehouse so crucial for modern businesses? Let’s dive in.

A data warehouse is a strategic layer that is essential for any organization looking to maintain competitiveness in a data-driven world. The ability to act quickly on analyzed data translates to improved operational efficiencies, better customer relationships, and enhanced profitability.

The Technical Role of Data Warehousing

The primary function of a data warehouse is to facilitate analytics, not to perform analytics itself. The BI team configures the data warehouse to align with its analytical needs. Essentially, a data warehouse acts as a structured repository, comprising tables of rows and columns of carefully curated and frequently updated data assets. These assets feed BI applications that drive analytics.

“The primary function of a data warehouse is to facilitate analytics, not to perform analytics itself.”

Achieving the business imperatives of data warehousing relies heavily on these four key technical capabilities:

1. Real-Time Data Processing: This is critical for applications that require immediate action, such as fraud detection systems, real-time customer interaction management, and dynamic pricing strategies. Real-time data processing in a data warehouse is like a barista making your coffee to order–it happens right when you need it, tailored to your specific requirements.

2. Scalability and Performance: Modern data warehouses must handle large datasets and support complex queries efficiently. This capability is particularly vital in industries such as retail, finance, and telecommunications, where the ability to scale according to demand is necessary for maintaining operational efficiency and customer satisfaction.

3. Data Quality and Accessibility: The quality of insights directly correlates with the quality of data ingested and stored in the data warehouse. Ensuring data is accurate, clean, and easily accessible is paramount for effective analysis and reporting. Therefore, it’s crucial to consider the entire data chain when crafting a data strategy, rather than viewing the warehouse in isolation.

4. Advanced Capabilities: Modern data warehouses are evolving to meet new challenges and opportunities:

- - Data Virtualization: Allowing queries across multiple data sources without physical data movement.
  - Integration With Data Lakes: Enabling analysis of both structured and unstructured data.
  - In-Warehouse Machine Learning: Supporting the entire ML lifecycle, from model training to deployment, directly within the warehouse environment.

“In the world of data warehousing, scalability isn’t just about handling more data—it’s about adapting to the ever-changing landscape of business needs.”

Understanding the Differences: Databases, Data Warehouses, and Analytics Databases

Databases, data warehouses, and analytics databases serve distinct purposes in the realm of data management, with each optimized for specific use cases and functionalities.

A database is a software system designed to efficiently store, manage, and retrieve structured data. It is optimized for Online Transaction Processing (OLTP), excelling at handling numerous small, discrete transactions that support day-to-day operations. Examples include MySQL, PostgreSQL, and MongoDB. While databases are adept at storing and retrieving data, they are not specifically designed for complex analytical querying and reporting.

Data warehouses, on the other hand, are specialized databases designed to store and manage large volumes of structured, historical data from multiple sources. They are optimized for analytical processing, supporting complex queries, aggregations, and reporting. Data warehouses are designed for Online Analytical Processing (OLAP), using techniques like dimensional modeling and star schemas to facilitate complex queries across large datasets. Data warehouses transform and integrate data from various operational systems into a unified, consistent format for analysis. Examples include Actian Data Platform, Amazon Redshift, Snowflake, and Google BigQuery.

Analytics databases, also known as analytical databases, are a subset of databases optimized specifically for analytical processing. They offer advanced features and capabilities for querying and analyzing large datasets, making them well-suited for business intelligence, data mining, and decision support. Analytics databases bridge the gap between traditional databases and data warehouses, offering features like columnar storage to accelerate analytical queries while maintaining some transactional capabilities. Examples include Actian Vector, Exasol, and Vertica. While analytics databases share similarities with traditional databases, they are specialized for analytical workloads and may incorporate features commonly associated with data warehouses, such as columnar storage and parallel processing.

“In the data management spectrum, databases, data warehouses, and analytics databases each play distinct roles. While all data warehouses are databases, not all databases are data warehouses. Data warehouses are specifically tailored for analytical use cases. Analytics databases bridge the gap, but aren’t necessarily full-fledged data warehouses, which often encompass additional components and functionalities beyond pure analytical processing.”

The Human Side of Data: Key User Personas and Their Pain Points

Welcome to Data Warehouse Personalities 101. No Myers-Briggs here—just SQL, Python, and a dash of data-induced delirium. Let’s see who’s who in this digital zoo.

Note: While these roles are presented distinctly, in practice they often overlap or merge, especially in organizations of varying sizes and across different industries. The following personas are illustrative, designed to highlight the diverse perspectives and challenges related to data warehousing across common roles.

DBAs are responsible for the technical maintenance, security, performance, and reliability of data warehouses. “As a DBA, I need to ensure our data warehouse operates efficiently and securely, with minimal downtime, so that it consistently supports high-volume data transactions and accessibility for authorized users.”
Data analysts specialize in processing and analyzing data to extract insights, supporting decision-making and strategic planning. “As a data analyst, I need robust data extraction and query capabilities from our data warehouse, so I can analyze large datasets accurately and swiftly to provide timely insights to our decision-makers.”
BI analysts focus on creating visualizations, reports, and dashboards from data to directly support business intelligence activities. “As a BI analyst, I need a data warehouse that integrates seamlessly with BI tools to facilitate real-time reporting and actionable business insights.”
Data engineers manage the technical infrastructure and architecture that supports the flow of data into and out of the data warehouse. “As a data engineer, I need to build and maintain a scalable and efficient pipeline that ensures clean, well-structured data is consistently available for analysis and reporting.”
Data scientists use advanced analytics techniques, such as machine learning and predictive modeling, to create algorithms that predict future trends and behaviors. “As a data scientist, I need the data warehouse to handle complex data workloads and provide the computational power necessary to develop, train, and deploy sophisticated models.”
Compliance officers ensure that data management practices comply with regulatory requirements and company policies. “As a compliance officer, I need the data warehouse to enforce data governance practices that secure sensitive information and maintain audit trails for compliance reporting.”
IT managers oversee the IT infrastructure and ensure that technological resources meet the strategic needs of the organization. “As an IT manager, I need a data warehouse that can scale resources efficiently to meet fluctuating demands without overspending on infrastructure.”
Risk managers focus on identifying, managing, and mitigating risks related to data security and operational continuity. “As a risk manager, I need robust disaster recovery capabilities in the data warehouse to protect critical data and ensure it is recoverable in the event of a disaster.”

Data Warehouse Use Cases For Modern Organizations

In this section, we’ll feature common use cases for both the business and IT sides of the organization.

6 Common Business Use Cases

This section highlights how data warehouses directly support critical business objectives and strategies.

1. Supply Chain and Inventory Management: Enhances supply chain visibility and inventory control by analyzing procurement, storage, and distribution data. Think of it as giving your supply chain a pair of X-ray glasses—suddenly, you can see through all the noise and spot exactly where that missing shipment of left-handed widgets went.

Examples:

- - - Retail: Optimizing stock levels and reorder points based on sales forecasts and seasonal trends to minimize stockouts and overstock situations.
    - Manufacturing: Tracking component supplies and production schedules to ensure timely order fulfillment and reduce manufacturing delays.
    - Pharmaceuticals: Ensuring drug safety and availability by monitoring supply chains for potential disruptions and managing inventory efficiently.

2. Customer 360 Analytics: Enables a comprehensive view of customer interactions across multiple touchpoints, providing insights into customer behavior, preferences, and loyalty.

Examples:

- - - Retail: Analyzing purchase history, online and in-store interactions, and customer service records to tailor marketing strategies and enhance customer experience (CX).
    - Banking: Integrating data from branches, online banking, and mobile apps to create personalized banking services and improve customer retention.
    - Telecommunications: Leveraging usage data, service interaction history, and customer feedback to optimize service offerings and improve customer satisfaction.

3. Operational Efficiency: Improves the efficiency of operations by analyzing workflows, resource allocations, and production outputs to identify bottlenecks and optimize processes. It’s the business equivalent of finding the perfect traffic route to work—except instead of avoiding road construction, you’re sidestepping inefficiencies and roadblocks to productivity.

Examples:

- - - Manufacturing: Monitoring production lines and supply chain data to reduce downtime and improve production rates.
    - Healthcare: Streamlining patient flow from registration to discharge to enhance patient care and optimize resource utilization.
    - Logistics: Analyzing route efficiency and warehouse operations to reduce delivery times and lower operational costs.

4. Financial Performance Analysis: Offers insights into financial health through revenue, expense, and profitability analysis, helping companies make informed financial decisions.

Examples:

- - - Finance: Tracking and analyzing investment performance across different portfolios to adjust strategies according to market conditions.
    - Real Estate: Evaluating property investment returns and operating costs to guide future investments and development strategies.
    - Retail: Assessing the profitability of different store locations and product lines to optimize inventory and pricing strategies.

5. Risk Management and Compliance: Helps organizations manage risk and ensure compliance with regulations by analyzing transaction data and audit trails. It’s like having a super-powered compliance officer who can spot a regulatory red flag faster than you can say “GDPR.”

Examples:

- - - Banking: Detecting patterns indicative of fraudulent activity and ensuring compliance with anti-money laundering laws.
    - Healthcare: Monitoring for compliance with healthcare standards and regulations, such as HIPAA, by analyzing patient data handling and privacy measures.
    - Energy: Assessing and managing risks related to energy production and distribution, including compliance with environmental and safety regulations.

6. Market and Sales Analysis: Analyzes market trends and sales data to inform strategic decisions about product development, marketing, and sales strategies.

Examples:

- - - eCommerce: Tracking online customer behavior and sales trends to adjust marketing campaigns and product offerings in real time.
    - Automotive: Analyzing regional sales data and customer preferences to inform marketing efforts and align production with demand.
    - Entertainment: Evaluating the performance of media content across different platforms to guide future production and marketing investments.

These use cases demonstrate how data warehouses have become the backbone of data-driven decision making for organizations. They’ve evolved from mere data repositories into critical business tools.

In an era where data is often called “the new oil,” data warehouses serve as the refineries, turning that raw resource into high-octane business fuel. The real power of data warehouses lies in their ability to transform vast amounts of data into actionable insights, driving strategic decisions across all levels of an organization.

9 Technical Use Cases

Ever wonder how boardroom strategies transform into digital reality? This section pulls back the curtain on the technical wizardry of data warehousing. We’ll explore nine use cases that showcase how data warehouse technologies turn business visions into actionable insights and competitive advantages. From powering machine learning models to ensuring regulatory compliance, let’s dive into the engine room of modern data-driven decision making.

1. Data Science and Machine Learning: Data warehouses can store and process large datasets used for machine learning models and statistical analysis, providing the computational power needed for data scientists to train and deploy models.

Key features:

1. 1. 1. Built-in support for machine learning algorithms and libraries (like TensorFlow).
    2. High-performance data processing capabilities for handling large datasets (like Apache Spark).
    3. Tools for deploying and monitoring machine learning models (like MLflow).

2. Data as a Service (DaaS): Companies can use cloud data warehouses to offer cleaned and curated data to external clients or internal departments, supporting various use cases across industries.

Key features:

1. 1. 1. Robust data integration and transformation capabilities that ensure data accuracy and usability (using tools like Actian DataConnect, Actian Data Platform for data integration, and Talend).
    2. Multi-tenancy and secure data isolation to manage data access (features like those in Amazon Redshift).
    3. APIs for seamless data access and integration with other applications (such as RESTful APIs).
    4. Built-in data sharing tools (features like those in Snowflake).

3. Regulatory Compliance and Reporting: Many organizations use cloud data warehouses to meet compliance requirements by storing and managing access to sensitive data in a secure, auditable manner. It’s like having a digital paper trail that would make even the most meticulous auditor smile. No more drowning in file cabinets!

Key features:

1. 1. 1. Encryption of data at rest and in transit (technologies like AES encryption).
    2. Comprehensive audit trails and role-based access control (features like those available in Oracle Autonomous Data Warehouse).
    3. Adherence to global compliance standards like GDPR and HIPAA (using compliance frameworks such as those provided by Microsoft Azure).

4. Administration and Observability: Facilitates the management of data warehouse platforms and enhances visibility into system operations and performance. Consider it your data warehouse’s health monitor—keeping tabs on its vital signs so you can diagnose issues before they become critical.

Key features:

1. 1. 1. A platform observability dashboard to monitor and manage resources, performance, and costs (as seen in Actian Data Platform, or Google Cloud’s operations suite).
    2. Comprehensive user access controls to ensure data security and appropriate access (features seen in Microsoft SQL Server).
    3. Real-time monitoring dashboards for live tracking of system performance (like Grafana).
    4. Log aggregation and analysis tools to streamline troubleshooting and maintenance (implemented with tools like ELK Stack).

5. Seasonal Demand Scaling: The ability to scale resources up or down based on demand makes cloud data warehouses ideal for industries with seasonal fluctuations, allowing them to handle peak data loads without permanent investments in hardware. It’s like having a magical warehouse that expands during the holiday rush and shrinks during the slow season. No more paying for empty shelf space!

Key features:

1. 1. 1. Semi-automatic or fully automatic resource allocation for handling variable workloads (like Actian Data Platform’s scaling and Schedules feature, or Google BigQuery’s automatic scaling).
    2. Cloud-based scalability options that provide elasticity and cost efficiency (as seen in AWS Redshift).
    3. Distributed architecture that allows horizontal scaling (such as Apache Hadoop).

6. Enhanced Performance and Lower Costs: Modern data warehouses are engineered to provide superior performance in data processing and analytics, while simultaneously reducing the costs associated with data management and operations. Imagine a race car that not only goes faster but also uses less fuel. That’s what we’re talking about here—speed and efficiency in perfect harmony.

Key features:

1. 1. 1. Advanced query optimizers that adjust query execution strategies based on data size and complexity (like Oracle’s Query Optimizer).
    2. In-memory processing to accelerate data access and analysis (such as SAP HANA).
    3. Caching mechanisms to reduce load times for frequently accessed data (implemented in systems like Redis).
    4. Data compression mechanisms to reduce the storage footprint of data, which not only saves on storage costs but also improves query performance by minimizing the amount of data that needs to be read from disk (like the advanced compression techniques in Amazon Redshift).

7. Disaster Recovery: Cloud data warehouses often feature built-in redundancy and backup capabilities, ensuring data is secure and recoverable in the event of a disaster. Think of it as your data’s insurance policy—when disaster strikes, you’re not left empty-handed.

Key features:

1. 1. 1. Redundancy and data replication across geographically dispersed data centers (like those offered by IBM Db2 Warehouse).
    2. Automated backup processes and quick data restoration capabilities (like the features in Snowflake).
    3. High availability configurations to minimize downtime (such as VMware’s HA solutions).

Note: The following use cases are typically driven by separate solutions, but are core to an organization’s warehousing strategy.

8. (Depends on) Data Consolidation and Integration: By consolidating data from diverse sources like CRM and ERP systems into a unified repository, data warehouses facilitate a comprehensive view of business operations, enhancing analysis and strategic planning.

Key features:

1. 1. 1. 1. ETL and ELT capabilities to process and integrate diverse data (using platforms like Actian Data Platform or Informatica).
      2. Support for multiple data formats and sources, enhancing data accessibility (capabilities seen in Actian Data Platform or SAP Data Warehouse Cloud).
      3. Data quality tools that clean and validate data (like tools provided by Dataiku).

9. (Facilitates) Business Intelligence: Data warehouses support complex data queries and are integral in generating insightful reports and dashboards, which are crucial for making informed business decisions. Consider this the grand finale where all your data prep work pays off—transforming raw numbers into visual stories that even the most data-phobic executive can understand.

Key features:

1. 1. 1. 1. Integration with leading BI tools for real-time analytics and reporting (like Tableau).
      2. Data visualization tools and dashboard capabilities to present actionable insights (such as those in Snowflake and Power BI).
      3. Advanced query optimization for fast and efficient data retrieval (using technologies like SQL Server Analysis Services).

The technical capabilities we’ve discussed showcase how modern data warehouses are breaking down silos and bridging gaps across organizations. They’re not just tech tools; they’re catalysts for business transformation. In a world where data is the new currency, a well-implemented data warehouse can be your organization’s most valuable investment.

However, as data warehouses grow in power and complexity, many organizations find themselves grappling with a new challenge: managing an increasingly intricate data ecosystem. Multiple vendors, disparate systems, and complex data pipelines can turn what should be a transformative asset into a resource-draining headache.

“In today’s data-driven world, companies need a unified solution that simplifies their data operations. Actian Data Platform offers an all-in-one approach, combining data integration, data quality, and data warehousing, eliminating the need for multiple vendors and complex data pipelines.”

This is where Actian Data Platform shines, offering an all-in-one solution that combines data integration, data quality, and data warehousing capabilities. By unifying these core data processes into a single, cohesive platform, Actian eliminates the need for multiple vendors and simplifies data operations. Organizations can now focus on what truly matters—leveraging data for strategic insights and decision-making, rather than getting bogged down in managing complex data infrastructure.

As we look to the future, the organizations that will thrive are those that can most effectively turn data into actionable insights. With solutions like Actian Data Platform, businesses can truly capitalize on their data warehouse investment, driving meaningful transformation without the traditional complexities of data management.

Experience the data platform for yourself with a custom demo.

About Fenil Dedhia

Fenil Dedhia leads Product Management for Actian's Cloud Portfolio. He has previously guided two startups to success as a PM, excelling at transforming ideas into flagship products that solve complex business challenges. His user-centric, first-principles approach drives innovation across AI and data platform products. Through his Actian blog posts, Fenil explores AI, data governance, and data management topics. Check out his latest insights on how modern data platforms drive business value.

Data Quality

Key Insights From the ISG Buyers Guide for Data Intelligence 2024

Actian Corporation

September 23, 2024

ISG Buyers Guide: Navigating the Data Management Landscape

Modern data management requires a variety of technologies and tools to support the people responsible for ensuring that data is trustworthy and secure. Conquering the data challenge has led to a massive number of vendors offering solutions that promise to solve data issues.

With the evolving vendor landscape, it can be difficult to know where to start. It can also be difficult to understand how to determine the best way to evaluate vendors to be sure you’re seeing a true representation of their capabilities, not just sales speak. When it comes to data intelligence, it can be difficult to even define what that means to your business.

With budgets continuously stretched even thinner and new demands placed on data, you need data technologies that meet your needs for performance, reliability, manageability, and validation. Likewise, you want to know that the product has a strong roadmap for your future and a reputation for service you can count on, giving you the confidence to meet current and future needs.

Independent Assessments are Key to Informing Buying Decisions

Independent analyst reports and buying guides can help you make informed decisions when evaluating and ultimately purchasing software that aligns with your workloads and use cases. The reports offer unbiased, critical insights into the advantages and drawbacks of vendors’ products. The information cuts through marketing jargon to help you understand how technologies truly perform, helping you choose a solution with confidence.

These reports are typically based on thorough research and analysis, considering various factors such as product capabilities, customer satisfaction, and market performance. This objectivity helps you avoid the pitfalls of biased or incomplete information.

For example, the 2024 Buyers Guide for Data Intelligence by ISG Research, which provides authoritative market research and coverage on the business and IT aspects of the software industry, offers insights into several vendors’ products. The guide offers overall scoring of software providers across key categories, such as product experience, capabilities, usability, ROI, and more.

In addition to the overall guide, ISG Research offers multiple buyers guides that focus on specific areas of data intelligence, including data quality and data integration.

ISG Research Market View on Data Intelligence

Data intelligence is a comprehensive approach to managing and leveraging data across your organization. It combines several key components working seamlessly together to provide a holistic view of data assets and facilitate their effective use.

The goal of data intelligence is to empower all users to access and make use of organizational data while ensuring its quality. As ISG Research noted in its Data Quality Buyers Guide, the data quality product category has traditionally been dominated by standalone products focused on assessing quality.

“However, data quality functionality is also an essential component of data intelligence platforms that provide a holistic view of data production and consumption, as well as products that address other aspects of data intelligence, including data governance and master data management,” according to the guide.

Similarly, ISG Research’s Data Integration Buyers Guide notes the importance of bringing together data from all required sources. “Data integration is a fundamental enabler of a data intelligence strategy,” the guide points out.

Companies across all industries are looking for ways to remove barriers to easily access data and enable it to be treated as an important asset that can be consumed across the organization and shared with external partners. To do this effectively and securely, you must consider various capabilities, including data integration, data quality, data catalogs, data lineage, and metadata management solutions.

These capabilities serve as the foundation of data intelligence. They streamline data access and make it easier for teams to consume trusted data for analytics and business intelligence that inform decision making.

ISG Research Criteria for Choosing Data Intelligence Vendors

ISG Research notes that software buying decisions should be based on research. “We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data integration technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential,” according to the company.

In the 2024 Data Intelligence Buyers Guide, ISG Research evaluated software and presented findings in key categories that are important to modern businesses. The evaluation offers a framework that allows you to shorten the cycle time when considering and purchasing software.

isg report 2024

For example, ISG Research encourages you to follow a process to ensure the best possible outcomes by:

Defining the Business Case and Goals. Understand what you are trying to accomplish to justify the investment. This should include defining the specific needs of people, processes, and technology. Ventana Research, which is part of ISG Research, predicts that through 2026, three-quarters of enterprises will be engaged in data integrity initiatives to increase trust in their data.
Assessing Technologies That Align With Business Needs. Based on your business goals, you should determine the technological capabilities needed for success. This will ensure you maximize your technology investments and avoid paying for tools that you may not require. ISG Research notes that “too many capabilities may be a negative if they introduce unnecessary complexity.”
Including People and Defining Processes. While choosing the right software will help enforce data quality and facilitate getting data to more people across your organization, it’s important to consider the people who need to be involved in defining and maintaining data quality processes.
Evaluating and Selecting Technology Properly. Determine the business and technology approach that best aligns with your requirements. This allows you to create criteria for meeting your needs, which can be used for evaluating technologies.

As ISG Research points out in its buyers guide, all the products it evaluated are feature-rich. However, not all the capabilities offered by a software provider are equally valuable to all types of users or support all business requirements needed to manage products on a continuous basis. That’s why it’s important to choose software based on your specific and unique needs.

Buy With Confidence

It can be difficult to keep up with the fast-changing landscape of data products. Independent analyst reports help by enabling you to make informed decisions with confidence.

Actian is providing complimentary access to the ISG Research Data Quality Buyers Guide that offers a detailed software provider and product assessment. Get your copy to find out why Actian is ranked in the “Exemplary” category.

If you’re looking for a single, unified data platform that offers data integration, data warehousing, data quality, and more at unmatched price-performance, Actian can help. Let’s talk.

About Actian Corporation

Actian Life

Actian’s Innovation Earns Prestigious IT4IT Award

Steffen Harre

September 12, 2024

Innovation is essential for meeting organizations’ business, IT, and technical needs. It’s why Actian invests more than 20% of our revenue in research and development. In addition to the positive responses we hear from customers for helping them solve their toughest business challenges, we also receive accolades from industry peers.

For example, we recently earned the Award of Distinction in the category “IT4IT™ Standard / IT Management and Planning.” The honor was decided by the jury of The Open Group India Awards 2024, which recognized our efforts to effectively employ open standards and open source. The Jury Panel called our award a testament to our outstanding work and our clear path toward the effective use of open standards and open source.

At Actian, we use the IT4IT reference architecture to manage our business and the end-to-end lifecycles of all Actian products, such as the Actian Data Platform, Vector, and Zen.

This open standard is backed by around 900 members of the Open Group that include HCLTech and almost every other industry leader as well as government institutions.

Bringing Ongoing Value to Customers

To earn the award, we provided a detailed assessment that focused on the value streams we deliver and showcased how these streams bring new and ongoing benefits to customers. The assessment included these eight key aspects of our offerings:

Modern Product Management Practices. Our teams successfully use IT4IT, a scaled agile framework, DevOps, and site reliability engineering where appropriate for a modern, innovative approach to open standards and open source.
Continuous Improvement. We ensure strong management support for optimizing the lifecycles of our digital products and services with a focus on ongoing improvement and sustainable value.
Mature Product Development. From gathering requirements to meet customers’ needs to releasing new products and updates, we optimize robust, value-centric processes to deliver modern, flexible, and easy-to-use products.
Ongoing Customer Focus. The customer is at the heart of everything we do. We maintain a strong customer focus, ensuring our products meet their business needs to build confidence in the user and data management experience.
An Automation Mindset. Operations are streamlined using automated order fulfillment to provide quick and easy delivery to the customer.
Accurate Billing. Established mechanisms for metering and billing customers provide a quick overview of the Actian Units used in the cloud while ensuring transparent and accurate pricing.
Trusted Reliability. We employ a proactive approach to system reliability using site reliability engineering.
Tool Rationalization Initiative. With ongoing initiatives to optimize the software landscape in engineering and throughout our organization, we drive increased efficiency and reduce costs.

What Does the Product Journey Look Like?

Delivering industry-leading products requires detailed steps to ensure success. Our journey to product delivery is represented in detail here:

IT4IT product journey infographic

This is how the four aspects work together and are implemented:

Strategy to Portfolio. In this planning process, Actian manages ISO 27001-compliant internal and external policies in Confluence. The strategic planning is handled by a dedicated team with regular reviews by the project management office and executive leadership team. This aligns the plans to our vision and governance through the executive team.

Based on these plans, the executive leadership team provides strategic funding and resource allocation for the development of projects. The development and governance of the architecture roadmap are managed by the architecture board.

Requirement to Deploy. This building process entails sprint grooming to ensure a clear understanding of user stories and to facilitate the required collection and tracking of requirements, which then benefit future products and features.

At Actian, we use efficient, automated deployments with small batch continuous integration, robust testing, version control, and seamless integrations in our development processes. This is complemented by efficient testing, extensive automation, version-controlled test cases, realistic performance testing, and integrated shift-left practices in continuous integration and continuous development pipelines with defect management.

Of course, source code version control is used to ensure traceability through testing and comments, and to promote code reuse. The code changes are traceable for build package promotion, automated validation, and centralized repository.

Request to Fulfill. In this process, during and after delivery, Actian provides a strong user engagement with self-service resources, efficient ordering and fulfillment, integrated support, effective ticket management, and collaborative issue resolution.

The external service offering is efficient, with strong contract management, knowledge sharing, and automated deployment plans along with Jira service desk and Salesforce integration. Customer instances are created via self-service with automated orchestration, deployment guidelines, Kubernetes provisioning, and continuous deployment. In addition, the billing system provides a robust usage and metering Actian Unit hour calculation system with RabbitMQ integration and usage history generation.

Detect to Correct. In this final process that involves running the product, Actian provides collaborative SLA performance reviews in tiered service contracts (Gold, Silver, and Bronze), and Salesforce integration for SLA data. Knowledge is shared through a repository.

Actian offers a site reliability engineering framework with clear lifecycle stages, along with a rich knowledge base. A robust performance and availability monitoring system is also provided.

Identifying Opportunities for Improvements and Closing Gaps

As with any major assessment, there are ongoing opportunities for improvements and identifying gaps in services or capabilities. These are evaluated and addressed to further improve Actian products and offerings.

Opportunities for improvements to our Actian processes included 12 instances for integration. These integration opportunities can benefit the development and delivery of products through increased usage and the linked exchange of data between departments and functions.

Eighteen opportunities also exist for improvements for internal processes. These include providing a more consistent approach to standardization and best practices, which is expected to improve workflows during the development and deployment of products.

In addition to these, 14 opportunities for improvement were identified that can be addressed by improving internal tools. This includes introducing new tools as well as unifying and streamlining existing heterogeneous tools.

Curious how our products and services can help your business make confident, data-driven decisions? Let’s talk.

About Steffen Harre

Steffen Harre is Director of Quality Management at Actian, ensuring product excellence across the entire lifecycle. He built QA teams from the ground up at Thinking Instruments and later expanded his scope into Quality Management for large-scale software initiatives. Steffen's dual focus on QA (product quality) and QM (process integrity) has delivered reliable, scalable solutions for global customers. Steffen's blog posts delve into QA methodologies, testing frameworks, and DevOps integration. Read his recent contributions to build a culture of quality in your organization.

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