Data Management

Flip the Script on Data Management With a Next-Gen Data Catalog

Phil Ostroff

October 29, 2024

next gen data catalog

As you’re probably experiencing firsthand, businesses are generating and handling more data than ever before, creating an urgent need for sophisticated tools to manage it all. Traditional data catalogs, while once effective, often fall short in providing the agility and accessibility needed by modern organizations. Managing, organizing, and making sense of your data requires easy-to-use tools that go beyond yesterday’s metadata management solutions to provide the speed, functionality, and features your business needs.

Enter next-generation data catalogs—innovative solutions that are designed to transform the way you discover, govern, and leverage your data. These cutting-edge data catalogs offer intuitive interfaces, advanced automation, and robust governance features that empower all users, making them essential for any company striving to harness the full power of its data assets.

As you focus on making data more accessible and actionable, next-generation data catalogs can help. They have emerged as a critical component in the modern data landscape.   

How Next-Generation Data Catalogs Differ From Traditional Solutions

Traditional data catalogs have served businesses well for years, providing a repository to store and manage metadata and helping organizations locate critical data assets. However, these catalogs are often bulky, slow, and expensive, creating barriers to broader adoption, especially by non-technical users.

In addition, legacy data catalogs typically come with hefty license fees and require significant manual effort to classify and manage data. In contrast, next-generation data catalogs provide faster, more cost-effective, and user-friendly solutions designed to meet the needs of modern businesses. Next-gen data catalogs offer:

  • Cloud-Native Architecture. Traditional data catalogs are often tied to on-premises systems, which can limit scalability and accessibility. Modern catalogs are built natively for the cloud, offering a flexible, scalable architecture that grows with your business.

The architecture allows you to integrate a wide range of cloud platforms, applications, and on-premises systems seamlessly for a connected ecosystem. A cloud-native architecture also makes it easier to manage data in hybrid or multi-cloud environments, ensuring the catalog can adapt as your infrastructure evolves.

  • Ease of Use. One key differentiator of next-generation data catalogs is their focus on ease of use. Traditional solutions often require specialized skills to navigate, limiting their usability to data professionals and technical users. Next-gen catalogs, on the other hand, are designed with the non-technical user in mind.

These modern catalogs feature intuitive user interfaces and automated workflows that simplify the process of finding, accessing, and utilizing data, making them accessible to a broader range of users within your organization. Whether it’s through natural language search or user-friendly data lineage displays, these catalogs empower you to work with data without requiring deep technical expertise. Ultimately, this helps democratize the data flow process.

  • Enhanced Data Governance and Compliance. While traditional catalogs have long supported data governance, next-generation catalogs take it a step further by automating much of the data governance process. These solutions offer role-based access controls, automatic metadata tagging, and support for business glossaries, making it easier to manage sensitive information and ensure compliance with regulations like the General Data Protection Regulation (GDPR) or California Consumer Privacy Act (CCPA).

Some next-gen catalogs even incorporate advanced capabilities to automatically detect and classify sensitive data. They can then provide real-time alerts and insights to prevent data breaches.

  • Cost-Effective Solutions. The pricing model of traditional data catalogs has been a significant barrier for many organizations, with enterprise licenses costing upwards of $150,000 annually, according to a report from Eckerson Group “Next-Generation Data Catalogs.” In contrast, modern catalogs offer competitive pricing models, often at a fraction of the cost.

For example, the report notes that the annual cost for next-gen data catalogs typically ranges from $70,000 to $90,000. Lower costs, combined with new features and reduced manual effort, make these catalogs an attractive option for businesses of all sizes.

5 Ways the Actian Data Intelligence Platform Stands Out for Its Next-Generation Data Catalog

Among the modern data catalogs on the market, the Actian Data Intelligence Platform stands out as a modern solution for modernizing your data management practices. It offers an ideal platform and data catalog for data-driven organizations like yours to democratize data, centralize and unify all enterprise metadata, and offer a single source of truth.

Five key reasons to use the Actian Data Intelligence Platform include:

  1. A Focus on Simplicity and User Experiences. Actian Data Intelligence Platform is focused on delivering simplicity—and this is reflected in the platform’s clean and intuitive user interface. The platform offers two user interfaces: Actian Explorer for business users who need to discover and explore data, and Actian Studio for data stewards responsible for managing and governing data assets. This separation of interfaces ensures that both technical and non-technical users can interact with the data catalog in ways that are most relevant to their roles.
  1. Enterprise Data Marketplace Integration. What sets Actian apart from many of its competitors is its Enterprise Data Marketplace. This feature allows business domains within your organization to share data products seamlessly, enabling greater collaboration and data democratization across departments. When users look to access a data product, the platform routes those requests through third-party workflow systems, like those from Jira or ServiceNow, to ensure governance policies are followed while maintaining ease of use. 
  1. Advanced Metadata Management and Federation. The Actian Data Intelligence Platform is built on a knowledge graph that supports multiple metamodels, allowing each business domain to create its own data catalog tailored to its specific needs. These domain-specific catalogs can then be combined into an enterprise catalog, providing a holistic view of your data landscape. This flexibility is crucial when operating across multiple business units or geographies because it allows you to maintain a unified data catalog while accommodating the unique requirements of each domain.
  1. Seamless Integration and Interoperability. With more than 70 proprietary data connectors, the Actian solution integrates with a wide range of data platforms, cloud applications, and on-premises systems. This ensures that the catalog can pull metadata from virtually any source within your organization, providing a comprehensive view of your data ecosystem. Additionally, by integrating with third-party applications such as Monte Carlo and Soda, you benefit from data quality monitoring to maintain high levels of data accuracy and reliability.
  1. Future-Ready Without the Hype. While many next-gen data catalogs are rushing to integrate AI features, Actian takes a measured approach, only adopting new technologies that have proven value to customers. For example, the Actian platform uses GenAI to summarize long descriptions and improve usability in targeted areas. This approach ensures that the platform remains cutting-edge without overwhelming users with unproven features.

A Strategic Investment in Data-Driven Success

As you continue to navigate the complexities of the digital age, the need for robust, scalable, and user-friendly data management solutions is more critical than ever. Next-generation data catalogs, like the one from the Actian Data Intelligence Platform, offer a powerful alternative to traditional solutions, providing enhanced usability, governance, and cost-efficiency.

Investing in a next-generation data catalog is not just a smart choice—it’s a strategic imperative. Actian Data Intelligence Platform, with its focus on simplicity, integration, and innovation, is well-positioned to help you gain maximum value from your data and drive meaningful business outcomes.

The Actian Data Intelligence Platform is more than just a data catalog: it bundles at no cost a data marketplace for publishing and consuming data products, putting Actian on the cutting-edge of all data catalogs,” according to the Eckerson Group.

Experience the platform for yourself with an Actian product tour.

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About Phil Ostroff

Phil Ostroff is Director of Competitive Intelligence at Actian, leveraging 30+ years of experience across automotive, healthcare, IT security, and more. Phil identifies market gaps to ensure Actian's data solutions meet real-world business demands, even in niche scenarios. He has led cross-industry initiatives that streamlined data strategies for diverse enterprises. Phil's Actian blog contributions offer insights into competitive trends, customer pain points, and product roadmaps. Check out his articles to stay informed on market dynamics.
Data Governance

Stop Wasting Data: Build a Future-Proof Strategy With Data Governance

Bob O'Donnell

October 29, 2024

Build a Future-Proof Strategy With Data Governance

These days, it’s hard to find a company that isn’t trying to better itself and become data-driven with advanced analytics, AI, or Generative AI. Enterprises across industries of all types are scrambling to integrate new, emerging technologies into their environments, hoping to get the much-vaunted promise of increased productivity and enhanced capabilities as quickly as they can.

What most organizations promptly discover as part of that process, however, is that getting their data assets organized in a way that will allow them to fully take advantage of these technologies is much harder than it first appears. The reasons are many – from a complex mix of data formats, data silos and data management tools, to uncertainty around how to best manage the process, the data preparation and organization – and collectively, these factors are fast becoming a major stumbling block for many companies.

In fact, according to a recent survey by TECHnalysis Research of IT decision makers in over 1,000 U.S. companies that are doing work with GenAI, Data Preparation and Integration is one of the top five challenges that companies face. In an interesting twist, in an Actian survey of 550 professionals (70% of which were director or higher) in 6 countries and 7 industries, 79% indicated that they believe they’re prepared for GenAI. However, when Gartner asked people in charge of AI data readiness, only 4% said they were ready.

Another problem is that most organizations don’t have any organized governance plans for their data. That same TECHnalysis Research survey found that just under 30% of large enterprises (1,000+ employees) and a whopping 64% of medium businesses (100-999 employees) don’t have governance plans of any kind for their GenAI projects.

One key issue is that many people don’t fully understand what data governance optimization is and why it’s important. On top of that, even organizations that have started to put together data governance policies and procedures don’t know best practices to ensure that they’re getting the right kind of data fed into their algorithms and large language models (LLMs). The net result is a large percentage of organizations aren’t using their critical data as effectively as they could be and that, in turn, typically translates into customized models and applications that aren’t as effective or as productive as they were expected to be. In fact, according to Gartner, by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive ethical governance frameworks.

To address these issues, companies clearly need not only a wide range of tools to best organize, manage and prepare their data for use, but also a framework and set of guidelines. This ensures that the most effective policies and procedures for acquiring and using data are in place to maximize the return on investment of implementing these emerging technologies.

That’s where a company like Actian, a division of HCL Software, comes into play. Actian currently offers a range of tools designed to organize and optimize a company’s data assets for a wide variety of innovative technologies, including GenAI. This is essential because, as many businesses have already discovered, the quality of the output that an application creates is utterly dependent on the quality of the data its underlying model is trained on. It’s a classic case of garbage in, garbage out—or more positively, high-quality data in, effective, impactful and trustworthy results out.

Actian’s suite of tools tackles everything from data organization to advanced analytics, all designed towards optimizing large volumes of data for ingestion. In particular, the company’s tools have a strong focus on metadata, which is quickly proving to be an essential part of the training process. Essentially, the more accurately and thoroughly an organization’s data can be documented or described via metadata, the more effectively a company can use that data in its training process for multiple applications, including GenAI. Plus, well-documented data can help reduce hallucinations and other misleading output that all LLMs are still occasionally prone to produce.

To help broaden its range of capabilities in these areas, Actian recently completed the purchase of Zeenea, a company that’s built a Data Intelligence Platform centered around a Data Catalog. Actian Data Intelligence Platform’s Data Catalog lets companies organize all their various data assets into a single catalog structure that leverages metadata to create a single searchable repository. This, in turn, helps data consumers within an organization find the information they need via either simple text-based searches or a visual Knowledge Graph. The Knowledge Graph utilizes semantic metadata to link together numerous independent data sources and provide context and easy-to-see visual connections across these data assets.

The latest enhancement to these catalog capabilities is the company’s new Actian Federated Data Catalog, which takes the data cataloging concept to a new level by integrating its capabilities across an organization. This federated catalog leverages a domain-oriented data management approach where the teams most familiar with the data manage their own data assets, permissions, and governance in a dedicated data catalog. Domains can then publish their most valuable data assets in a shared Enterprise Data Marketplace, ready to be consumed as products by all business functions within the organization. By using the same principles and concepts across these different domains, organizations end up with a decentralized, yet consistent data management structure that provides an easier and more effective method for sharing critical data. Most importantly, they do so in a manner that provides a consistent set of governance principles, helping them avoid potential regulatory and other data compliance issues.

In addition to these data preparation and organization tools, Actian also provides its customers with a tested, mature set of data governance solutions and a comprehensive data governance framework to ensure best practices can be leveraged across the data preparation process.

Actian’s effective Data Governance Framework provides a straightforward but comprehensive set of policies and procedures that can help ensure that organizations of different types and with different needs can all get the most from their data assets. More than just a list of rules to follow, the framework has suggestions on organizational structure, questions and topics to be addressed in meetings, strategies for implementing some of the key concepts, and more.

The framework is also designed to help ensure buy-in across an organization’s critical decision-makers—a key make or break point for many advanced projects—as well as offer important practical benefits. For example, a properly followed framework can help organizations stay within any regulatory and legal requirements to which they might be subject, avoid data-driven bias in the output results, prevent loss of critical IP, address any potential ethical issues, and much more.

There’s no question that new technologies, like GenAI and the new kinds of applications the technology is enabling, are opening up some amazing new potential for companies to improve their productivity, stay ahead of their competitors, enhance their bottom line, and become truly “data driven.” At the same time, as with many other new technologies, it’s also opening up the potential for new types of risks and challenges.

As a result, companies who are eager to jump into the new exciting applications of data, like GenAI, need to be certain that they’re well prepared for the adventure. Taken together, Actian’s set of data preparation tools along with the Actian Data Intelligence Platform, Federated Data Catalog and governance framework can help companies have a smooth, organized, and comprehensive data preparation process. Given how important this process is and how much it can impact the ultimate success or failure of advanced data initiatives, it’s clear that it’s a topic that organizations of all types and sizes need to get much smarter about now.

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About Bob O'Donnell

Bob O'Donnell is President and Chief Analyst of TECHnalysis Research, LLC, offering strategic consulting and market research to the technology industry. With a track record of delivering data-driven insights, Bob has advised leading tech firms on market positioning and product strategy. A recognized industry voice, Bob has been featured in publications like USA Today and TechCrunch. Follow him on X @bobodtech or visit his company's website for his latest research. As a guest on the Actian blog, Bob dives into tech trends around analytics, data integration, and cloud innovation. Explore his articles for thought leadership on the future of data.
Data Integration

The Future of Data Integration

Actian Corporation

October 23, 2024

illustration banner of server room for data integration

Big data remains more important than ever to meet the goals of businesses in just about every industry imaginable. Because of data’s value, new advancements are being made every year to help organizations optimize and manage the data they collect and store to the fullest extent possible.  

Cloud computing and AI have driven recent advancements in big data integration, helping make data more accessible and higher-quality while remaining secure. And the future of data integration looks even brighter, with innovations currently on the horizon that will allow greater access and better stewardship than ever before. Let’s look at some of the most impactful data integration trends made recently and innovations you’ll hopefully see soon. 

Data Integration Trends

Here are eight recent trends and technologies that have contributed to improvements in data integration and will impact future best practices for data integration architectures. If these advancements aren’t on your radar yet, they may be soon. 

1. Real-Time Data Integration

Waiting for large volumes of data to be processed before you can gain insights from them is a thing of the past. With a move from batch processing to real-time integration that uses change data capture, you can extract the information you need from your most crucial data quicker than ever — even in real-time. You can analyze massive data repositories by employing parallel processing without straining your system resources, giving you insights at the speed of business. 

2. Cloud Computing

Cloud computing has been around for decades, but recent advancements are making it easier for non-coders to set up data storage in the cloud instead of data lakes and data warehouses on physical servers. Cloud computing makes unifying disparate data sources and accessing data much easier, faster, and less costly, and now, no-code and low-code solutions are making it easier to create cloud-native architectures. With edge computing now being integrated into cloud computing tools, data integration can move even faster. 

3. The Convergence of ELT and ETL

Because many businesses are moving their data repositories from data lakes to cloud servers, deciding whether to use extract, load, and transform (ELT) or extract, transform, and load (ETL) processes for data integration is becoming less common. A new data integration process with continuous transformations, sometimes called extract, transform, load, and transform (ETLT), is emerging to improve data quality by constantly refining the data for data mesh distribution.

4. No-Code and Low-Code Data Integration

Emerging no-code and low-code — also called “self-service” — cloud architecture tools are improving speed for the future of data integration, but they’re also making overall data integration processes much easier. Many data integration tools and data platforms are being streamlined so that professionals without coding experience can perform collection and analysis tasks that once required specialized expertise. Overcoming data integration challenges for those without coding knowledge means you can share data with more stakeholders who find it valuable. 

5. IoT Data Integration

The Internet of Things (IoT) is a system of internet-enabled electronic devices that can share data with each other. The type and complexity of data these devices can share — and feed into your repository — is advancing, but so is how that real-time data is integrated for business uses. The ever-increasing use of 5G and 5G RedCap makes transmitting data from things like health monitoring devices faster; edge computing advancements make processing the data faster and simplified, while more intuitive iPaaS solutions are making it easier to organize and manage data from multiple data-collecting apps. 

6. The Emergence of Data Mesh

First conceived in 2019, this data integration trend provides a business-minded alternative to data lakes that has become popular with organizations that collect large amounts of data, like Netflix and PayPal. Instead of storing data in a central location, it is distributed directly to the sources who need it, allowing faster access and dissemination. Remember that because data is moving straight from collection to analysis, you need to have a structured data conversion process in place so that those receiving the data can make sense of it. 

7. AI and Machine Learning

Possibly the most significant critical drivers in making the future of data integration faster, more accessible, and higher quality are advanced artificial intelligence and machine learning capabilities. Thanks to the recent evolution of AI, systems and processes that once required expert coders to build are now being constructed seamlessly into data integration platforms that allow more people to access trusted data. Machine learning is advanced enough to facilitate retail customer data integration from images, videos, and text, for example, but it also constantly refines and focuses data to make it easier to analyze buying habits. While their impact may not be noticeable now, AI and machine learning will begin streamlining and improving your data integration platforms and tools to increase efficiency, accessibility, and quality. 

8. Data Security and Governance

As more data integration processes move to cloud servers, rely less on coded systems and routines, and perform functions in real-time, there’s a huge need for increased cybersecurity and a more nuanced data governance framework. Many of these advancements aim to get more data to stakeholders who need it most but may not be as familiar with protecting it as data professionals are. To this end, Zero-Trust Architecture (ZTA) is becoming more popular, and data access permissions will become more stringent. This requires data governance professionals to focus more on stewardship and access control models that limit access or make it more sophisticated to prevent unauthorized access to data.  

Data Integration Trends: By the Numbers

Data is king, so don’t take our word for these trends shaping the future of data integration. Here are some statistics that provide insights into where the field is headed. 

  • The global data integration market size is expected to reach $17.1 billion by 2025, with $4.87 billion of that belonging to the U.S. 
  • Marketing, including in the retail industry, makes up the largest sector of data integration income at 26%, but HR data integration is growing.
  • In 2022, 35.5% of organizations chose on-site servers over cloud-based solutions, with 32.3% of these citing cybersecurity concerns and 24.6% worried about proper data integration.
  • AI and machine learning experienced an 85% increase for SaaS products in late 2023 compared to the year before.
  • The average breach cost of data stored in public clouds is $5.17 million.
  • 40% of business initiatives fail because of poorly integrated data sets.
  • Data integration is the fastest-growing Data and AI market, showing 117% growth year-over-year (YoY).
  • Using AI in data integration processes can improve data quality by up to 20%.


Download the Data Integration Infographic

Looking Ahead

Since 2023, there has been no expense spared in developing generative AI technology, and the evolution of this technology shows no sign of stopping. Expect to see more AI, GenAI, and machine learning integrations in data processing tools, which should increase the quality of all kinds of data. These technologies should also make processes more streamlined, which can help overcome data integration challenges organizations have concerning cloud-based services. The lines between implementing ELT or ETL will continue to be blurred as more organizations adopt cloud computing, and data structuring and processing become more automated by AI and machine learning. 

Data is an ever-growing market, so expect more money to be spent on these data integration trends and other surprising innovations in the coming years.  

A Cutting-Edge Data Integration Platform

You can find many of these data integration trends already hard at work in hybrid integration platforms (HIPs) and solutions like DataConnect from Actian. You can automate data pipelines with low- or no-code, institute business rules and data quality standards that are automatically incorporated into your workflow, and enable real-time data connections.

 

 

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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.
Databases

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

Dee Radh

October 22, 2024

Actian Vector Overview Chart

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.

 

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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.
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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

get-to-know-zeenea-blog-hero

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. 

Ashley Knoble headshot

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

woman discussing why actian

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.

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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.

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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

guide to modernizing hcl informix

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:

  1. Start building your business strategy.
  2. Evaluate your existing Informix database environment.
  3. Kick off your modernization project.
  4. 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.

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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

Table Cloning: Create Instant Snapshots Without Data Duplication

Actian Corporation

September 27, 2024

table cloning concept abstract

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.

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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.
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.

Kunal Shah - Headshot

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

build-smart-agriculture-iot-system

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!

Johnson Varughese headshot

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.