Data Security

On-Premises vs. Cloud Data Warehouses: 7 Key Differences

Actian Corporation

July 31, 2024

blue connectors representing on-premises versus cloud data warehouses

In the constantly evolving landscape of data management, businesses are faced with the critical decision of choosing between on-premises and cloud data warehouses. This decision impacts everything from scalability and cost to security and performance.

Understanding deployment options is crucial for data analysts, IT managers, and business leaders looking to optimize their data strategies. At a basic level, stakeholders need to understand how on-premises and cloud data warehouses are different—and why those differences matter.

Having a detailed knowledge of the advantages and disadvantages of each option allows data-driven organizations to make informed buying decisions based on their strategic goals and operational needs. For example, decision-makers often consider factors such as:

Control over the data environment.
Security and compliance needs.
The ability to customize and scale.
Capital expenditure vs. operational expense.
Maintenance and management of the data warehouse.

The pros and cons, along with their potential impact on data management and usage, should be considered when implementing or expanding a data warehouse. It’s also important to consider future needs to ensure the data warehouse meets current, emerging, and long-term data requirements.

Location is the Biggest—But Not the Only—Differentiator

Modern on-premises data warehouses have been enabling enterprises since the 1980s. Early versions had the ability to integrate and store large data volumes and perform analytical queries.

By the 2010s, as organizations became more data-driven, data volumes began to explode—giving rise to the term “big data”—and technology advanced for data storage, processing power, and faster analytics. The data warehouse, usually residing in on-premises environments, became a mainstay for innovative businesses. During this time, the public cloud became popular and cloud data storage also became available, including cloud-based data warehouses.

The biggest difference between an on-premises data warehouse and a cloud version is where the infrastructure is hosted and managed. An on-premises data warehouse has the infrastructure physically located within the organization’s facilities, whereas cloud versions leveraged storage in hyperscaler environments.

With on-premises data warehouses, the company is responsible for purchasing, setting up, and maintaining the hardware and software—which requires the proper skillset and resources to perform effectively. With a cloud data warehouse, the infrastructure is hosted by a cloud service provider. The provider manages the hardware and software, including maintenance, updates, and scaling. Here’s a look at other key differences.

7 Primary Differences and Their Business Impact

The fundamental differences in data location have several implications:

Overall Cost Structure

On-premises data warehouses typically require a significant upfront capital expenditure for hardware and software. This is in addition to ongoing costs for maintenance, upgrades, power, and cooling.

Cloud solutions operate on a subscription or pay-as-you-go model, which essentially avoids large capital expenditures and instead uses operational expenses. Having cloud service providers handle routine maintenance, backups, and disaster recovery can reduce the operational burden on an organization’s internal IT teams. The cloud option can ultimately be more cost-effective for stable, predictable workloads that do not have unpredictable cost implications.

Scalability

Scaling an on-premises data warehouse can be complex and time intensive, often requiring the organization to install additional hardware. Cloud data warehouses offer near-infinite scalability, allowing organizations to quickly and easily scale up or down based on demand—this is one of the primary benefits of the cloud option.

Deployment and Management

With an on-premises data warehouse, deployment can be time-consuming, involving a physical setup and extensive configurations that can take weeks or months. Managing the data warehouse also requires specialized IT staff to handle day-to-day operations, security, and troubleshooting.

The cloud speeds up deployment, often requiring just a few clicks to provision resources. The cloud provider largely handles management, freeing up internal IT staff for other tasks. Because cloud data warehouses can be up and running quickly, organizations can start deriving value sooner.

Control and Customization

Operating the data warehouse on-premises gives organizations complete control over their data and infrastructure. This gives extensive options for customization to meet specific business and data needs.

One trade-off with cloud solutions is that they do not offer the same level of control and customization compared to on-premises infrastructure. As a result, organizations may face limitations when fine-tuning specific configurations and ensuring complete data sovereignty in the cloud.

Flexibility to Meet Workloads

An on-premises data warehouse is typically limited by its physical infrastructure and the capacity that was initially implemented—unless the environment is expanded. Upgrades and changes can be cumbersome and slow, which contrasts with the flexibility offered by a cloud-based data warehouse, which allows for quick adjustments to computing and storage resources to meet changing workload demands.

Security and Compliance

Security is managed internally with on-premises solutions, giving organizations full control, but also full responsibility. Compliance with changing industry regulations may require significant effort and resources to stay current. At the same time, organizations in industries such as finance and healthcare, where data privacy and security are paramount, may want to keep data on-prem for security reasons.

With cloud data warehouses, security and compliance of the physical hardware is managed by the cloud service provider, which often has security certifications in place. However, organizations must ensure they choose a provider that meets their specific compliance requirements and have internal staff that is knowledgeable about cloud configuration to ensure cloud infrastructure is configured correctly as part of the shared responsibility model.

Performance and Latency

These are two critical factors for data warehousing, especially when seconds—or even milliseconds—matter. On-premises solutions are known for their high performance due to their dedicated resources, while latency is minimized because data processing occurs locally. Cloud solutions may experience latency issues, but they benefit from the continuous optimization and upgrades provided by cloud vendors.

Make Informed Buying Decisions With Confidence

When deciding between on-premises and cloud data warehouses, organizations should consider specific requirements for current and future usage. Considerations include:

Data Volume and Growth Projections. Cloud solutions are better suited for businesses expecting rapid data growth because they offer immediate scalability.
Regulatory and Compliance Needs. On-premises solutions may be beneficial for organizations with strict compliance requirements because they offer complete control over data security, access, and compliance measures. This helps ensure that sensitive information is handled according to specific regulatory standards.
Budget and Financial Considerations. Cloud solutions offer lower initial costs and financial flexibility, which is beneficial for organizations with limited capital.
Business Agility. The cloud’s ability to rapidly scale and deploy resources makes it a good option for organizations that prioritize agility. Scalability allows them to respond swiftly to market changes, efficiently manage workloads, and accelerate the development and deployment of new applications and services.
Performance Requirements. On-premises solutions may be preferred by businesses needing high performance and low latency for workloads. Due to the proximity of data storage and computing resources, along with dedicated hardware, on-prem data warehouses can offer a performance advantage, although it’s important to note that cloud versions can offer real-time insights, too.

Consider Both Approaches With a Hybrid Solution

Choosing between on-premises and cloud data warehouses involves weighing the benefits and trade-offs of each option. Although the primary difference between on-premises and cloud data warehouses is the location and management of the infrastructure, the distinction cascades into other areas. This impacts myriad factors, such as costs, scalability, flexibility, security, and more.

By understanding key differences, data professionals, IT managers, and business decision-makers can make informed choices that align with their strategic goals. Organizations can ensure optimal data management and business success while having complete confidence in their data outcomes.

Organizations that want the benefits of both on-prem and cloud data warehouses can take a hybrid approach. A hybrid cloud data warehouse combines the scalability and flexibility of cloud with the control and security of on-premises solutions, enabling organizations to efficiently manage and analyze large volumes of data. This approach allows for seamless data integration and optimizes costs by utilizing current on-premises investments while benefiting from the scalability and flexibility offered by the cloud.

What does the future of data warehousing look like? Visit the Actian Academy for a look at where data warehousing began and where it is 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.

Data Integration

3 Key Considerations for Crafting a Winning Data Quality Business Case

Traci Curran

July 30, 2024

person crafting a winning data quality business case

In today’s rapidly evolving digital landscape, the integrity and reliability of your data can make or break your business. High data quality is not just a nice to have; it’s fundamental for informed decision-making, effective data management, and maintaining a competitive edge.

Ensuring your data’s accuracy, consistency, and reliability can significantly enhance operational efficiency and drive strategic initiatives. You must have confidence in your data. However, making the case for investments in the right technology to improve data quality can be challenging. It requires a well-crafted business case that clearly demonstrates its value and expected return on investment.

Understanding Data Quality

Data quality encompasses several key attributes:

Accuracy: How well data reflects real-world entities or events.
Consistency: The uniformity of data across different systems.
Completeness: The presence of all required data fields.
Timeliness: The availability of up-to-date information.
Validity: Adherence to specific formats and business rules.
Uniqueness: Absence of duplicate entries.

High-quality data offers numerous benefits, including improved efficiency, better customer satisfaction, enhanced compliance and risk management, and more effective use of emerging technologies like Generative AI (GenAI).

Recognizing the Need for Strong Data Quality

In today’s data-driven world, recognizing the need for strong data quality is crucial for any business aiming to stay competitive and efficient. Prioritizing data quality should be at the top of your agenda.

Watch for these indicators of potential data quality problems:

Discrepancies in data reports.
Poor marketing email delivery rates.
Declining business development efficacy.
Missing fields in CRM systems.
Increased customer or vendor complaints.
Inventory management issues.
Rising data storage and processing costs.
Increasing email opt-outs.

Benefits of High-Quality Data

High-quality data can transform your business operations, making them more efficient and driven by reliable insights. For instance, Actian customers like Ceva Logistics and Ebix Health rely on high-quality data to ensure that every decision is based on accurate, up-to-date, and complete information, enabling better customer relations and streamlined operations.

3 Steps to Craft a Winning Data Quality Business Case

1. Assess Current Data Quality

Start by conducting a thorough data quality assessment. Use data profiling tools to examine and understand the content, structure, and relationships within your data. This step involves reviewing data at both column and row levels and identifying patterns, anomalies, and inconsistencies, which will provide valuable insights into the quality of your data. Data auditing should also be part of this process, assessing the accuracy and completeness of data against predefined rules or standards. This initial assessment will help you pinpoint the specific areas where your data quality needs improvement.

2. Align Data Quality With Business Objectives

Next, ensure that your data quality initiatives align with your business objectives. Identify the link between business processes, key performance indicators (KPIs), and data assets. Engage with data and analytics leaders to capture their expectations and understand what is considered the “best fit” for the organization. This alignment guarantees that the data quality improvements you plan directly contribute to your business’s overall success and strategic goals.

3. Track Progress and Measure Impact

Finally, it’s crucial to track the progress of your data quality initiatives and measure their impact. Develop an organization-wide shared definition of data quality, identify specific quality metrics, and ensure continuous measurement of these metrics. Implement a data quality dashboard that provides all stakeholders with a comprehensive snapshot of data quality, helping them see past trends and design future process improvements. Regularly communicate the results and improvements to stakeholders to maintain transparency and foster a culture of continuous improvement in data quality.

By following these steps, you’ll craft a winning business case for data quality that highlights the necessity for investment and aligns closely with your strategic business goals, ensuring sustained support and success. You’ll also build confidence in your data and decision-making.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

Data Management

The Developer’s Guide to Choosing the Right Embedded Database

Kunal Shah

July 29, 2024

In today’s digital landscape, applications are increasingly complex, demanding efficient data management solutions. Embedded databases, with their lightweight footprint and high performance, have become essential tools for developers building applications for various platforms, from mobile devices to edge computing environments. However, the plethora of options available can be overwhelming. This guide aims to equip developers with the knowledge to select the ideal embedded database for their specific needs.

Understanding Embedded Databases

An embedded database is a database management system (DBMS) integrated directly into an application, rather than running as a separate process. This architecture offers several advantages, including:

Performance: Reduced network latency and overhead.
Reliability: No external dependencies.
Security: Data resides within the application’s boundaries.
Flexibility: Tailored to specific application requirements.

However, embedded databases also come with limitations, such as scalability and concurrent access capabilities. It’s crucial to understand these trade-offs when making a selection.

Key Considerations for Database Selection

Before diving into specific database options, let’s outline the key factors to consider when choosing an embedded database:

Data Model: Determine whether your application requires a key-value, document, or relational data model.
Data Volume and Complexity: Evaluate the size and structure of your dataset.
Performance Requirements: Assess the required read and write speeds, transaction throughput, and latency.
Storage Constraints: Consider the available storage space on the target platform.
Concurrency: Determine the number of concurrent users or processes accessing the database.
ACID Compliance: Evaluate if your application requires strict ACID (Atomicity, Consistency, Isolation, Durability) guarantees.
Platform Compatibility: Ensure the database supports your target platforms (e.g., mobile, embedded systems, cloud).
Development and Maintenance Effort: Consider the learning curve and ongoing support requirements.

Types of Embedded Databases

1. Key-Value Stores

- Ideal for simple data structures with fast read and write operations.
- Use cases: Caching, configuration settings, user preferences.

2. Document Stores

- Suitable for storing complex, hierarchical data structures.
- Use cases: Content management systems, IoT data, application state management.

3. Relational Databases:

- Offer structured data storage with ACID compliance.
- Use cases: Financial applications, inventory management, analytics.

4. Time-Series Databases:

- Optimized for handling time-stamped data with high ingestion and query rates.
- Use cases: IoT sensor data, financial time series, application performance monitoring.

Database Selection for Embedded App Development

Mobile Apps

Prioritize performance, low storage footprint, and offline capabilities.
Consider document stores or embedded versions of document stores
Optimize for battery life and device resources.

IoT Devices

Focus on low power consumption, high performance, and limited storage.
Key-value stores or embedded time-series databases are often suitable.
Consider data compression and encryption for security.

Database Selection for Edge-to-Cloud Data Management

Edge Processing

Emphasize low latency, high throughput, and offline capabilities.
Time-series databases or embedded document stores can be effective.
Consider data aggregation and filtering at the edge to reduce cloud load.

Data Synchronization

Choose a database that supports efficient data replication and synchronization.
Consider hybrid approaches combining embedded and cloud databases.
Ensure data consistency and integrity across environments.

Conclusion

Selecting the right embedded database is crucial for the success of your application. By carefully considering the factors outlined in this guide and evaluating the specific requirements of your project, you can make an informed decision.

Remember that the right embedded database is the one that meets your application’s needs while optimizing performance, security, and developer productivity.

At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. Optimized for embedded systems and edge computing, Zen boasts small-footprint with fast read and write access, making it ideal for resource-constrained environments.

With seamless data synchronization from edge to cloud, Zen is fully ACID compliant supporting SQL and NoSQL data access leveraging popular programming languages allowing developers to build low-latency embedded apps.

Additional Resources:

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.

Data Management

Enhance Financial Decisions With Real-Time Data Processing

Actian Corporation

July 26, 2024

Actian Zen datapoints showing Intelligent Edge Era

Article by Ashley Knoble and Derek Comingore

Cloud computing has been a dominant computing model dating back to 2002 when Amazon Web Services (AWS) launched. In 2012, Cisco coined the term “Fog Computing,” which is a form of distributed computing that brings computation and data persistence closer to the edge.

Fog computing, also known as edge computing, set the stage for the current Intelligent Edge era. The Intelligent Edge is the convergence of both machine learning and edge computing, resulting in intelligence being generated where data is born. The benefits of the Intelligent Edge are many, including:

Reduced bandwidth consumption.
Accelerated time-to-insights.
Smart devices that take automated actions.

The Intelligent Edge requires TinyML (Tiny Machine Learning) and traditional analytics running on smaller, less powerful devices. With smaller devices comes reduced disk capacities. Hence, software install footprints must be reduced.

Harnessing a single data management platform that accommodates a variety of intelligent edge use cases is preferred for consistency, reduced security surface, and data integration efficiencies. With increased data management and analytics on edge devices, security needs also increase. Security features such as data encryption quickly become required.

Embedded Databases for Edge Computing

Unlike traditional databases, embedded databases are ideal for edge computing environments for key reasons that include:

Small Footprint. Embedded databases require minimal storage and memory, making them ideal for devices with limited resources. This allows for smaller form factors and lower costs for edge devices.
Low Power Consumption. Embedded databases are designed to be energy efficient, minimizing the power drain on battery-powered devices, which is a critical concern for many edge applications.
Fast Performance. Real-time data processing is essential for many edge applications. Embedded databases are optimized for speed, ensuring timely data storage, retrieval, and analysis at the edge.
Reliability and Durability. Edge devices often operate in harsh environments. Embedded databases are designed to be reliable and durable, ensuring data integrity even in case of power failures or device malfunctions.
Security is paramount in the edge landscape. Embedded databases incorporate robust security features to protect sensitive data from unauthorized access.
Ease of Use. Unlike traditional databases, embedded databases are designed to be easy to set up and manage. This simplifies development and deployment for resource-constrained edge projects.

Introducing Actian Zen–An Embedded Database for Use Cases at the Edge

Actian Zen is our best-in-class multi-model embedded database for disruptive intelligent edge applications. With Zen, both partners and customers build intelligent applications running directly on and near the edge.

Additionally, traditional server and cloud-based deployments are supported. This results in a cohesive end-to-end data architecture for efficient data integration and reduced security vulnerability. Intelligent edge and edge-to-cloud applications can be deployed with confidence.

Analytics can be run directly where the data is being generated, utilizing Zen’s database technology. Actian Zen saves organizations time and simplifies what is otherwise a complicated and fragmented data architecture. Customers and partners obtain millisecond query response times with Zen’s microkernel database engine. And with native ANSI SQL support, users easily connect their favorite dashboard and data integration tools.

The Family of Proven Zen Products

Zen is a feature rich intelligent edge database designed to solve a wide spectrum of industry use cases and workloads. As such, Actian offers Zen in three specific editions tailored for custom and unique use cases.

Zen Mobile is designed for smart IoT and mobile devices. Deployment is achieved via direct application, embedding as a lightweight library.
Zen Edge offers an edition custom tailored for edge gateways and complex industrial devices.
Zen Enterprise enables customers and partners to solve their largest data management workloads and challenges. Zen Enterprise accommodates thousands of concurrent users while offering flexible deployment options including traditional on-premises and cloud environments.

Key Features and Benefits for Edge Environments

By leveraging Zen, companies gain immediate access to business and operational insights. Both partners and customers reduce total cost of ownership (TCO), save expense via lesser dependence on cloud computing and storage technologies, and improve sustainability.

Employee training is also reduced by using a single cohesive data platform. In parallel, when data must be propagated to the cloud, Zen provides a rich set of data access APIs supported by popular development frameworks and platforms.

Harness Edge Intelligence Today

With the arrival of the Intelligent Edge era comes a new set of technology and business requirements. Actian Zen, a lightweight multi-model embedded database, is at the forefront of the Intelligent Edge era. And, with the latest release of Zen 16.0, we are committed to helping companies simplify and solve for both intelligent edge and edge-to-cloud applications.

Get started today by contacting us or downloading the Actian Zen Evaluation Edition.

About Actian Corporation

Actian Life

Get to Know Actian’s 2024 Interns

Katie Keith

July 24, 2024

We want to celebrate our interns worldwide and recognize the incredible value they are bringing to our company. As a newly inducted intern myself, I am honored to have the opportunity to introduce our incredible new cohort of interns!

Andrea Brown (She/Her)
Clouds Operations Engineer Intern

Andrea is a Computer Science major at the University of Houston-Downtown. She lives in Houston and in her free time enjoys practicing roller skating and learning French. Her capstone project focuses on using Grafana for monitoring resources and testing them with k6 synthetics.

What she likes most about the intern program so far is the culture. “Actian has done such a great job cultivating a culture where everyone wants to see you succeed,” she notes. “Everyone is helpful and inspiring.” From the moment she was contacted for an internship to meeting employees and peers during orientation week, she felt welcome and knew right away she had made the right choice. She has no doubt this will be a unique and unforgettable experience, and she is looking forward to learning more about her capstone project and connecting with people across the organization.

Claire Li (She/Her)
UX Design Intern

Claire is based in Los Angeles and is studying interaction design at ArtCenter College of Design. For her capstone project, she will create interactive standards for the Actian Data Platform and apply them to reusable components and the onboarding experience to enhance the overall user experience.

“Actian fosters a positive and supportive environment for interns to learn and grow,” she says.

Claire enjoys the collaborative atmosphere and the opportunity to tackle real-world challenges. She looks forward to seeing how she and her fellow interns will challenge themselves to problem-solve, present their ideas, and bring value to Actian in their unique final presentations. Outside of work, she spends most of her weekends hiking and capturing nature shots.

Prathamesh Kulkarni (He/Him)
Cloud QA Intern

Prathamesh is working toward his master’s degree in Computer Science at The University of Texas at Dallas. He is originally from Pune, India.

His capstone project aims to streamline the development of Actian’s in-house API test automation tool and research the usability of GitHub Copilot in API test automation.

By automating these tasks, he and his team can reduce manual effort and expedite the creation of effective and robust test automation solutions. The amazing support he has received and the real value of the work he has been involved in have been highlights of his internship so far. He says it’s been a rewarding experience to apply what he has learned in a practical setting and see the impact of his contributions.

A fun fact about him is that he loves washing dishes—it’s like therapy to him, and he even calls himself a professional dishwasher! He is also an accomplished Indian classical percussion musician, having graduated in that field.

Marco Brodkorb
Development Vector Intern

Hailing from Thuringia, Germany, Marco is working on his master’s degree in Computer Science at Technische Universität Ilmenau. He began his work as an Actian intern by writing unit tests and then began integrating a new compression method for strings called FSST.

He is working on integrating a more efficient range join algorithm that uses ad hoc generated UB-Trees, as part of his master thesis.

Naomi Thomas (She/Her)
Education Team Intern

Naomi is from Florida and is a graduate student at the University of Central Florida pursuing a master’s degree in Instructional Design & Technology. She has five years of experience working in the education field with an undergraduate degree in Education Sciences.

For her capstone project, Naomi is diving into the instructional design process to create a customer-facing course on DataConnect 12.2 for Actian Academy. She is enjoying the company culture and the opportunity to learn from experienced instructional designers and subject matter experts. “Everyone has been incredibly welcoming and supportive, and I’m excited to be working on a meaningful project with a tangible impact!” she says.

A fun fact about her is that she has two adorable dogs named Jax and King. She enjoys reading and collecting books in her free time.

Linnea Castro (She/Her)
Cloud Operations Engineer Intern

Linnea is majoring in Computer Science at Washington State University. She is working with the Cloud Operations team to convert Grafana observability dashboards into source code—effective observability helps data tell a story, while converting these dashboards to code will make the infrastructure that supports the data more robust.

She has loved meeting new people and collaborating with the Cloud team. Their morning sync meetings bring together people across the U.S. and U.K. She says that getting together with the internship leaders and fellow interns during orientation week set a tone of connection and possibility that continues to drive her each day. Linnea is looking forward to continuing to learn about Grafana and get swifter with querying. To that end, she is eager to learn as much as she can from the Cloud team and make a meaningful contribution.

She has three daughters who are in elementary school and is a U.S. Coast Guard veteran. Her favorite book is “Mindset” by Dr. Carol Dweck because it introduced her to the concept and power of practicing a growth mindset.

Alain Escarrá García (He/Him)
Development Vector Intern

Alain is from Cuba and just finished his first year of bachelor studies at Constructor University in Bremen, Germany, where he is majoring in Software, Data, and Technology. Working with the Actian Vector team, his main project involves introducing microservice architecture for user-defined Python functions. In his free time, he enjoys music, both listening to it and learning to play different instruments.

Matilda Huang (She/Her)
CX Design Intern

Matilda is pursuing her master’s degree in Technology Innovation at the University of Washington. She is participating in her internship from Seattle. Her capstone project focuses on elevating the voice of our customers. She aims to identify friction points in our current feedback communication process and uncover areas of opportunity for CX prioritization.

Matilda is enjoying the opportunity to collaborate with members from various teams and looks forward to connecting with more people across the company.

Liam Norman (He/Him)
Generative AI Intern

Liam is a senior at Harvard studying Computer Science. His capstone project involves converting natural language queries into SQL queries to assist Actian’s sales team.

So far, his favorite part of the internship was meeting the other interns at orientation week. A fun fact: In his free time, he likes to draw cartoons and play the piano.

Laurin Martins (He/Him)
Development Vector Intern

Laurin is from a small village near Frankfurt, Germany, called Langebach and is studying for a master’s degree in IT at TU Ilmenau. His previous work for Actian includes his bachelor thesis “Multi-key Sorting in Vectorized Query Execution.”

After that, he completed an internship to implement the proposed algorithms for a wide variety of data types. He is currently working on his master’s thesis titled “Elastic Query Processing in Stateless x100.” He plans to further develop the ideas and implementation presented in his master’s thesis in a Ph.D. program in conjunction with TU Ilmenau.

In his free time, he discovered that Dungeons and Dragons is a great evening board game to play with friends. He is also the lead for the software development at a startup company (https://healyan.com)

Kelsey Mulrooney (She/Her)
Cloud Security Engineer Intern

Kelsey is from Wilmington, Delaware, and majoring in Cybersecurity at the Rochester Institute of Technology. She is involved in implementing honeypots—simulated systems designed to attract and analyze hacker activities.

Kelsey’s favorite part about the internship program so far is the welcoming environment that Actian cultivates. She looks forward to seeing how much she can accomplish in the span of 12 weeks. Outside of work, Kelsey enjoys playing percussion, specifically the marimba and vibraphone.

Justin Tedeschi (He/Him)
Cloud Security Engineer Intern

Justin is from Long Island, New York, and an incoming senior at the University of Tampa. He’s majoring in Management Information Systems with a minor in Cybersecurity. At Actian, he’s learning about vulnerabilities in the cloud and how to spot them, understand them, and also prevent them.

The internship program allows access to a variety of resources, which he’s definitely taking advantage of, including interacting with people he finds to be knowledgeable and understanding. A fun fact about Justin is that he used to be a collegiate runner—one year at the University of Buffalo, a Division 1 school, then another year at the college he’s currently attending, which is Division 2.

Guillermo Martinez Alacron
Development Vector Intern

Hailing from Mexico, Guillermo is studying Industrial Engineering and participating in an exchange at TU Ilmenau in Germany. As part of his internship, he is working on the design and implementation of a quality management system in order to obtain the ISO 9001 certification for Actian. He enjoys Star Wars, rock music, and sports—and is especially looking forward to the Olympics!

Joe Untrecht (He/Him)
Cloud Operations Engineer Intern

Joe is from Portola Valley, California, which is a small town near Palo Alto. He is heading into his senior year at the University of Wisconsin-Madison, majoring in Computer Science. He loves and cannot recommend this school enough. One interesting fact about him is that he loves playing Hacky Sack and is about to start making custom hacky sacks. Another interesting fact is that he loves all things Star Wars and believes “Revenge of the Sith” is clearly the best movie. His favorite dessert is cookies and milk.

His capstone project involves cloud resource monitoring. He has been learning how to use the various services on Amazon Web Services, Google Cloud, and Microsoft Azure while practicing how to visualize the data and use the services on Grafana. He has had an immense amount of fun working with these platforms and doesn’t think he has ever learned more than in the first three weeks of his internship. He views the internship as a great opportunity to improve his skills and build new ones. He is “beyond grateful” for this opportunity and excited to continue learning about Actian and working on his capstone project.

Jon Lumi (He/Him)
Software Development Intern

Jon is from Kosovo and is a second-year Computer Science student at Constructor University in Bremen, Germany. He is working at the Actian office in Ilmenau, Germany, and previously worked as a teaching assistant at his university for first-year courses.

His experience as an Actian intern has been nothing short of amazing because he has not only had the opportunity to grow professionally through the guidance of supervisors and the challenges he faced, but also to learn in a positive and friendly environment. Jon is looking forward to learning and experiencing even more of what Actian offers, and having a good time along the way.

Davis Palmer (He/Him)
Engineering Intern, Zen Hardware

Davis is double majoring in Mechanical Engineering and Applied Mathematics. He’s also earning a minor in Computer Science at Texas A&M University.

His capstone project consists of designing and constructing a smart building with a variety of IoT devices with the Actian Zen team. He “absolutely loves” the work he has been doing and all the people he has interacted with. Davis is looking forward to all of the intern events for the rest of the summer.

Matthew Jackson (He/Him)
Engineering Intern, Zen Hardware

Matthew is working with the Actian Zen team. He grew up only a few miles from Actian’s office in Round Rock, Texas. Going into his junior year at Colorado School of Mines in Golden, Colorado, he’s working on two majors: Computer Science with a focus on Data Science, and Electrical Engineering with a focus on Information & Systems Sciences (ISS).

Outside of school, he plays a bit of jazz and other genres as a keyboardist and trumpeter. He is a huge fan of playing winter sports like hockey, skiing, and snowboarding. This summer at Actian, he is working alongside another hardware engineering intern for Actian Zen, Davis Palmer, to build a smart model office building to act as a tech demo for Zen databases. His part of the project is performing all the high-level development, which includes conducting web development, developing projects with facial recognition AI, and other tasks at that level of abstraction. He is super interested in the project assigned to him and is excited to see where it goes…

Fedor Gromov
Development Vector Intern

Fedor is from Russia and working at the Actian office in Germany. He is attending a master’s program at Constructor University of Bremen and studying Computer Science. He’s working on adding ONNX microservice support to a microservices team. His current hobby is bouldering.

Katie Keith (She/Her)
Employee Experience Intern

Katie is from Vail, Colorado, and an upcoming senior at Loyola University in Chicago. She is receiving her BBA in Finance with a minor in Psychology. For her capstone project, she is working with the Employee Experience team to put together a Pilot Orientation Program for the new go-to-market strategy employees.

She has really enjoyed Actian’s company culture and getting to learn from her team. Katie is looking forward to cheering on her fellow interns during their capstone presentations at the completion of the internship program. In her free time, she enjoys seeing stage productions and reading. She is super thankful to be part of the Actian team!

About Katie Keith

Katie Keith is pursuing a BBA in Finance at Loyola University in Chicago, contributing to Actian's Employee Experience team. She has collaborated on a Pilot Orientation Program for new go-to-market employees, leveraging her academic research and interpersonal skills. Katie has studied the intersection of psychology and business, providing unique perspectives on employee engagement. Her blog entries at Actian reflect her passion for organizational development and onboarding. Stay tuned for her insights on creating impactful employee experiences.

Data Integration

Efficient Integrations: How to Slash Costs and Boost Efficiency

Traci Curran

July 23, 2024

In today’s dynamic global business climate, the drive for efficiency and cost reduction has never been more pressing. The key to unlocking these gains lies in efficient integrations, which optimize data workflows and streamline operations. With the increasing complexity and volume of data, the need for seamless integration across various platforms and systems can profoundly impact both top-line growth and bottom-line savings. Efficient integrations enhance operational efficiency and pave the way for innovation and competitive advantage. Your organization can significantly improve financial performance by harnessing the power of data integration and leveraging the right technology.

To create efficient integrations within your organization, focus on several key areas: optimizing business operations, leveraging automation to enhance efficiency, implementing cost-effective reporting and analytics, and using cloud integration to reduce expenses. Each of these components is crucial for developing a strategy that reduces costs and increases efficiency. By understanding and adopting these integration practices, you’ll streamline data workflows and set the foundation for scalable growth and improved business agility. Let’s explore how transforming your approach to integration can turn challenges into opportunities for optimization and innovation.

Optimizing Business Operations for Efficient Integration

Streamlining Data Management

Adopt Best Practices: Implementing data management best practices ensures streamlined operations and aids in decision-making. By eliminating data silos, seamless data integration becomes possible, presenting a coherent perspective of your business operations.
Harness Automation: The synergy of data analytics and integration workflow automation transforms raw data into actionable insights, reshaping decision-making processes.
Enhance Accessibility: Ensuring data accessibility is critical. Modern BI tools provide row-level security, allowing tailored data access while maintaining confidentiality. This enables employees to access relevant data promptly, fostering a proactive approach in all business endeavors.

Enhanced Business Insights

Utilize BI Tools: Business Intelligence (BI) tools transform large datasets into actionable insights, facilitating strategic planning and resource optimization. These tools provide a comprehensive overview of various business aspects, enhancing decision-making capabilities.
Leverage Data Analytics: Data analytics is pivotal in decoding customer behavior and steering companies toward smarter decisions. It helps identify areas of excess and untapped resources, allowing for more effective resource allocation.
Continuous Improvement: Business process improvement should be continuous as businesses evolve and expand. Implementing Data and application tools can provide insights into potential bottlenecks and optimization opportunities, improving operational efficiency.

Automation and Efficiency

Reducing Manual Work With Automation

Streamline Repetitive Tasks: Automation technologies significantly reduce the time spent on repetitive tasks such as data entry and scheduling, which are often cited as productivity killers. By automating these tasks, employees can focus on more strategic activities contributing to the organization’s growth.
Enhance Workflow Efficiency: Implementing automation can eliminate the need for manual intervention in routine tasks, allowing processes to operate more smoothly and reliably. This speeds up operations and reduces the risk of errors, making workflows more efficient.

Improving Process Accuracy

Minimize Human Errors: One of the most significant advantages of automation is its ability to perform tasks with high precision. Automated systems are less prone to the lapses in concentration that affect human workers, ensuring that each task is performed accurately and consistently.
Increase Data Integrity: Automation minimizes human errors in data handling, from entry to analysis, enhancing the reliability of business operations. This improved accuracy is crucial for making informed decisions and maintaining high-quality standards across the organization.

Cost-Effective Reporting and Analytics

Simplifying Reporting

Refinement of Business Information Management Systems: Simplifying your business information management systems can reduce complexities, leading to up to a 15% cost reduction in reporting and governance across your organization.
Automation of Reporting Processes: By automating manual steps in your reporting process, you can achieve quicker, more responsive, and more accurate financial reporting. This frees up resources and minimizes the scope for human error, allowing for better decision-making and potential spending reductions.
Enhanced Data Integrity and Accuracy: Implementing workflow automation reduces errors and increases data integrity, crucial for accurate reporting and informed decision-making.

Utilizing Data Warehousing

Cloud-Based Solutions: Transitioning to cloud-based data warehousing solutions like Actian can offer scalability, flexibility, and significant cost savings by reducing the operational pain points associated with traditional hardware.

Cost Optimization Strategies: Employing data compression, optimized ETL processes, and consumption-based pricing models in data warehousing can control expenses and align costs with usage, thereby reducing overall storage and management costs.

Data and application integration solutions offer substantial benefits that can transform your organization. By streamlining operations, enhancing data accessibility, and fostering real-time decision-making, these solutions drive efficiency and innovation. They enable seamless communication between systems, reduce redundancy, and improve data accuracy. Furthermore, integrating disparate applications and data sources provides a unified view of business processes, empowering your organization to respond swiftly to market changes and customer needs. Ultimately, embracing data and application integration is a strategic move that supports growth, scalability, and a competitive edge in today’s fast-paced business environment.

About Traci Curran

Data Management

Real-Time Data Processing With Actian Zen and Kafka Connectors

Johnson Varughese

July 17, 2024

data processing with actian zen and apache kafka

Welcome back to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. In part 1 , we explored how to leverage BtrievePython to run Btrieve2 Python applications, using the Zen 16.0 Enterprise/Server Database Engine.

This is Part 2 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen. In this blog post, we’ll walk through setting up a Kafka demo using Actian Zen, demonstrating how to manage and process real-time financial transactions seamlessly. This includes configuring environment variables, using an orchestration script, generating mock transaction data, leveraging Docker for streamlined deployment, and utilizing Docker Compose for orchestration.

Introduction to Actian Zen Kafka Connectors

In the dynamic world of finance, processing and managing real-time transactions efficiently is a must-have. Actian Zen’s Kafka Connectors offer a robust solution for streaming transaction data between financial systems and Kafka topics. The Actian Zen Kafka Connectors facilitate seamless integration between Actian Zen databases and Apache Kafka. These connectors support both source and sink operations, allowing you to stream data out of a Zen Btrieve database into Kafka topics or vice versa.

Source Connector

The Zen Source connector streams JSON data from a Zen Btrieve database into a Kafka topic. It employs change capture polling to pick up new data at user-defined intervals, ensuring that your Kafka topics are always updated with the latest information from your Zen databases.

Sink Connector

The Zen Sink connector streams JSON data from a Kafka topic into a Zen Btrieve database. You can choose to stream data into an existing database or create a new one when starting the connector.

Setting Up Environment Variables

Before diving into the configuration, it’s essential to set up the necessary environment variables. These variables ensure that your system paths and library paths are correctly configured, and that you accept the Zen End User License Agreement (EULA).

Here’s an example of the environment variables you need to set:

export PATH="/usr/local/actianzen/bin:/usr/local/actianzen/lib64:$PATH"
export LD_LIBRARY_PATH="/usr/local/actianzen/lib64:/usr/lib64:/usr/lib"
export CLASSPATH="/usr/local/actianzen/lib64"
export CONNECT_PLUGIN_PATH='/usr/share/java'
export ZEN_ACCEPT_EULA="YES"

Configuring the Kafka Connectors

The configuration parameters for the Kafka connectors are provided as key-value pairs. These configurations can be set via a properties file, the Kafka REST API, or programmatically. Here’s an example JSON configuration for a source connector:

{
    "name": "financial-transactions-source",
    "config": {
        "connector.class": "com.actian.zen.Kafka.connect.source.BtrieveSourceConnector",
        "db.filename.param": "transactions.mkd",
        "server.name.param": "financial_db",  
        "poll.interval.ms": "2000",
        "tasks.max": "1",
        "topic": "transactionLog",
        "key.converter": "org.apache.Kafka.connect.storage.StringConverter",
        "value.converter": "org.apache.Kafka.connect.storage.StringConverter",
        "topic.creation.enable": "true",
        "topic.creation.default.replication.factor": "-1",
        "topic.creation.default.partitions": "-1"
    }
}

You can also define user queries for more granular data filtering using the JSON query language detailed in the Btrieve2 API Documentation. For example, to filter for transactions greater than or equal to $1000:

"\"Transaction\":{\"Amount\":{\"$gte\":1000}}"

Orchestration Script: kafkasetup.py

The kafkasetup.py script automates the process of starting and stopping the Kafka connectors. Here’s a snippet showing how the script sets up connectors:

import requests
import json
def main():
    requestMap = {}
    requestMap["Financial Transactions"] = ({
        "name": "financial-transactions-source",
        "config": {
            "connector.class": "com.actian.zen.kafka.connect.source.BtrieveSourceConnector",
            "db.filename.param": "transactions.mkd",
            "server.name.param": "financial_db",  
            "poll.interval.ms": "2000",
            "tasks.max": "1",
            "topic": "transactionLog",
            "key.converter": "org.apache.kafka.connect.storage.StringConverter",
            "value.converter": "org.apache.kafka.connect.storage.StringConverter",
            "topic.creation.enable": "true",
            "topic.creation.default.replication.factor": "-1",
            "topic.creation.default.partitions": "-1"
        }
    }, "8083")
    for name, requestTuple in requestMap.items():
        input("Press Enter to continue...")
        (request, port) = requestTuple
        print("Now starting " + name + " connector")
        try:
            r = requests.post("http://localhost:"+port+"/connectors", json=request)
            print("Response:", r.json)
        except Exception as e:
            print("ERROR: ", e)
    print("Finished setup!...")
    input("\n\nPress Enter to begin shutdown")
    for name, requestTuple in  requestMap.items():
        (request, port) = requestTuple
        try:
            r = requests.delete("http://localhost:"+port+"/connectors/"+request["name"])
        except Exception as e:
            print("ERROR: ", e)
if __name__ == "__main__":
    main()

When you run the script, it prompts you to start each connector one by one, ensuring everything is set up correctly.

Generating Transaction Data With data_generator.py

The data_generator.py script simulates financial transaction data, creating transaction records at specified intervals. Here’s a look at the core function:

import sys
import os
import signal
import json
import random
from time import sleep
from datetime import datetime
sys.path.append("/usr/local/actianzen/lib64")
import btrievePython as BP

class GracefulKiller:

    kill_now = False
  def __init__(self):
    signal.signal(signal.SIGINT, self.exit_gracefully)
    signal.signal(signal.SIGTERM, self.exit_gracefully)
  def exit_gracefully(self, *args):
    self.kill_now = True
def generate_transactions():
    client = BP.BtrieveClient()
    assert(client != None)
    collection = BP.BtrieveCollection()
    assert(collection != None)
    collectionName = os.getenv("GENERATOR_DB_URI")
    rc = client.CollectionCreate(collectionName)
    rc = client.CollectionOpen(collection, collectionName)
    assert(rc == BP.Btrieve.STATUS_CODE_NO_ERROR), BP.Btrieve.StatusCodeToString(rc)
    interval = int(os.getenv("GENERATOR_INTERVAL"))
    kill_condition = GracefulKiller()
    while not kill_condition.kill_now:
        transaction = {
            "Transaction": {
                "ID": random.randint(1000, 9999),
                "Amount": round(random.uniform(10.0, 5000.0), 2),
                "Currency": "USD",
                "Timestamp": str(datetime.now())
            }
        }
        print(f"Generated transaction: {transaction}")
        documentId = collection.DocumentCreate(json.dumps(transaction))
        if documentId < 0:
            print("DOCUMENT CREATE ERROR: " + BP.Btrieve.StatusCodeToString(collection.GetLastStatusCode()))
        sleep(interval)
    rc = client.CollectionClose(collection)
    assert(rc == BP.Btrieve.STATUS_CODE_NO_ERROR), BP.Btrieve.StatusCodeToString(rc)
if __name__ == "__main__":
    generate_transactions()

This script runs an infinite loop, continuously generating and inserting transaction data into a Btrieve collection.

Using Docker for Deployment

To facilitate this setup, we use a Docker container. Here’s the Dockerfile that sets up the environment to run our data generator script:

FROM actian/zen-client:16.00
USER root
RUN apt update && apt install python3 -y
COPY --chown=zen-svc:zen-data data_generator.py /usr/local/actianzen/bin
ADD _btrievePython.so /usr/local/actianzen/lib64
ADD btrievePython.py /usr/local/actianzen/lib64
USER zen-svc
CMD ["python3", "/usr/local/actianzen/bin/data_generator.py"]

This Dockerfile extends from the Actian Zen client image, installs Python, and includes the data generation script. By building and running this Docker container, we can generate and stream transaction data into Kafka topics as configured.

Docker Compose for Orchestration

To manage and orchestrate multiple containers, including Kafka, Zookeeper, and our data generator, we use Docker Compose. Here’s the docker-compose.yml file that brings everything together:

version: '3.8'
services:
  zookeeper:
    image: wurstmeister/zookeeper:3.4.6
    ports:
      - "2181:2181"
  kafka:
    image: wurstmeister/kafka:2.13-2.7.0
    ports:
      - "9092:9092"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT
      KAFKA_LOG_RETENTION_HOURS: 1
      KAFKA_MESSAGE_MAX_BYTES: 10485760
      KAFKA_BROKER_ID: 1
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
  actianzen:
    build: .
    environment:
      GENERATOR_DB_URI: "transactions.mkd"
      GENERATOR_LOCALE: "Austin"
      GENERATOR_INTERVAL: "5"
    volumes:
      - ./data:/usr/local/actianzen/data

This docker-compose.yml file sets up Zookeeper, Kafka, and our Actian Zen data generator in a single configuration. By running docker-compose up, we can spin up the entire stack and start streaming financial transaction data into Kafka topics in real-time.

Visualizing the Kafka Stream

To give you a better understanding of the data flow in this setup, here’s a diagram illustrating the Kafka stream:

actian zen database with kafka source conenctor

In this diagram, the financial transaction data flows from the Actian Zen database through the Kafka source connector into the Kafka topics. The data can then be consumed and processed by various downstream applications.

Kafka Connect: Kafka Connect instances are properly joining groups and syncing. Tasks and connectors are being configured and started as expected.

Financial Transactions: Transactions from both New York and San Francisco are being processed and logged correctly. The transactions include a variety of credit and debit actions with varying amounts and timestamps.

Zen and Kafka Connectors

Conclusion

Integrating Actian Zen with Kafka Connectors provides a powerful solution for real-time data streaming and processing. By following this guide, you can set up a robust system to handle financial transactions, ensuring data is efficiently streamed, processed, and stored. This setup not only demonstrates the capabilities of Actian Zen and Kafka but also highlights the ease of deployment using Docker and Docker Compose. Whether you’re dealing with financial transactions or other data-intensive applications, this solution offers a scalable and reliable approach to real-time data management.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

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

Top 7 Benefits Enabled By an On-Premises Data Warehouse

Actian Corporation

July 8, 2024

light streaks to represent on-premises data warehouse

In the ever-evolving landscape of database management, with new tools and technologies constantly hitting the market, the on-premises data warehouse remains the solution of choice for many organizations. Despite the popularity of cloud-based offerings, the on-premises data warehouse offers unique advantages that meet a variety of use cases.

A modern data warehouse is a requirement for data-driven businesses. While cloud-based options seemed to be the go-to trend over the last few years, on-premises data warehouses offer essential capabilities to meet your needs. Here are seven common benefits:

Ensure Data Security and Compliance

In industries such as finance and healthcare, data security and regulatory compliance are critical. These sectors manage sensitive information that must be protected, which is why they have strict protocols in place to make sure their data is secure.

With the ever-present risk of cyber threats—including increasingly sophisticated attacks—and stringent regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and Payment Card Industry Data Security Standard (PCI-DSS), you face significant risk and likely penalties for non-compliance. Relying on external cloud providers for data security can potentially leave you vulnerable to breaches and complicate your compliance efforts.

An on-prem data warehouse gives you complete control over your data. By housing data within your own infrastructure, you can implement robust security measures tailored to your specific needs. This control ensures compliance with regulatory requirements and minimizes the risk of data breaches.

Deliver High Performance With Low Latency

High-performance applications and databases, including those used for real-time analytics and transactional processing, require low-latency access to data. In scenarios where speed and responsiveness are critical, cloud-based solutions may introduce latency that can hinder performance, while on-premises offerings do not.

Latency issues can lead to slower decision-making, reduced operational efficiency, and poor user experiences. For businesses that rely on real-time insights, any delay can result in missed opportunities and diminished competitiveness. On-premises data warehouses offer the advantage of proximity.

By storing data locally, within your organization’s physical building, you can achieve near-instantaneous access to critical information. This setup is particularly advantageous for real-time analytics in which every millisecond counts. The ability to process data quickly and efficiently enhances overall performance and supports rapid decision-making.

Customize Your Infrastructure

Standardized cloud solutions may not always align with the unique requirements of your organization. Customization and control over the infrastructure can be limited in a cloud environment, making it difficult to tailor solutions to specific business or IT needs.

Without the ability to customize, you may face data warehouse constraints that limit your operational effectiveness. Plus, a lack of flexibility can result in suboptimal performance and increased operational costs if you need to work around the limitations of cloud services. For example, an inability to fine-tune your data warehouse infrastructure to handle high-velocity data streams can lead to performance bottlenecks that slow down critical operations.

An on-premises data warehouse gives you control over your hardware and software stack. You can customize your infrastructure to meet specific performance and security requirements. This customization extends to the selection of hardware components, storage solutions, and network configurations, enabling you to fully optimize your data warehouse for unique workloads and applications.

Manage Costs With Visibility and Predictability

Cloud services often operate on a pay-as-you-go model with unlimited scalability, which can lead to unpredictable costs. While a cloud data warehouse can certainly be cost-effective, expenses can escalate quickly with increased data volume and usage. Costs can fluctuate significantly based on how much the system is used, the amount of data transferred into and out of the cloud, the number and complexity of workloads, and other factors.

Unpredictable costs can strain budgets and make financial planning difficult, which makes the CFO’s job more challenging. On-premises data warehouses solve that problem by offering greater cost predictability.

By investing in your on-prem data warehouse upfront, you avoid the variable costs associated with cloud services. This approach leads to better budget planning and cost control, making it easier to allocate resources effectively. Over the long term, on-premises solutions can be cost-efficient with a favorable total cost of ownership and strong return on investment, especially if you have stable and predictable data usage patterns.

Meet Data Sovereignty Regulations

In some regions, data sovereignty laws mandate how data is collected, stored, used, and shared within specific borders. Similarly, data localization laws may require data about a region’s residents or data that was produced in a specific area to be stored inside a country’s borders. This means data collected in one country cannot be transferred and stored in a data warehouse in another country.

Navigating complex data sovereignty requirements can be challenging, especially when you’re dealing with international operations. An on-premises data warehouse helps ensure compliance with local data sovereignty laws by keeping data within a physical building.

This approach simplifies adherence to regional regulations and mitigates the risks associated with cross-border data transfers in the cloud. You can confidently operate within legal frameworks using an on-prem data warehouse, safeguarding your reputation and avoiding legal problems.

Integrate Disparate Systems

Many organizations operate by using a mix of legacy and modern systems. Integrating these disparate technologies into a cohesive ecosystem to manage, store, and analyze data can be complex, especially when using cloud-based solutions.

Legacy systems often contain critical data and processes that are essential to daily business operations. Migrating these systems to the cloud can be risky and disruptive, potentially leading to data loss or downtime, or require significant recoding or refactoring.

On-premises data warehouses enable integration with legacy systems. You also have the assurance of maintaining continuity by leveraging your existing infrastructure while gradually adding, integrating, or modernizing your data management capabilities in the warehouse.

Enable High Data Ingestion Rates

Industries such as telecommunications, manufacturing, and retail typically generate massive amounts of data at high velocities. Efficiently ingesting and processing this data in real time is crucial for maintaining operational effectiveness and gaining timely insights.

On-premises data warehouses are well suited to handle high data ingestion rates. By keeping data ingestion processes local, you can be ensured that data is captured, processed, and analyzed with minimal delay. This capability is essential for industries where real-time data is critical for optimizing operations and identifying emerging trends.

Choosing the Most Optimal Environment for Your Data Warehouse

While cloud-based data warehouses offer many benefits, the on-premises data warehouse continues to play a vital role in addressing specific database management challenges. From ensuring data security and compliance to providing low-latency access and the ability to customize, the on-premises data warehouse remains a powerful tool to meet your organization’s data needs.

By understanding the benefits enabled by an on-premises data warehouse, you can make informed decisions about your database management strategy. Whether it’s for meeting your regulatory requirements, optimizing performance, or controlling costs, the on-premises data warehouse stands as a robust and reliable option in the diverse landscape of database management solutions.

As we noted in a previous blog, the on-prem data warehouse is not dead. At the same time, we realize the unique benefits of a cloud approach. If you want on-prem and the cloud, you can have both with a hybrid approach.

For example, the Actian Data Platform offers data warehousing, integration, and trusted insights on-prem, in the public cloud, or in hybrid environments. A hybrid approach can minimize disruption, preserve critical data, and ensure that legacy systems continue to function effectively alongside new technologies, allowing you to make decisions and drive outcomes with confidence.

About Actian Corporation

Data Intelligence

Harnessing the Power of AI in Data Cataloging

Actian Corporation

July 8, 2024

Businessman Working On Laptop With Virtual Screen. Process Automation To Efficiently Manage Files.online Documentation Database And Document Management System Concept.

In today’s era of expansive data volumes, AI stands at the forefront of revolutionizing how organizations manage and extract value from diverse data sources. Effective data management becomes paramount as businesses grapple with the challenge of navigating vast amounts of information. At the heart of these strategies lies data cataloging—an essential tool that has evolved significantly with the integration of AI, with promises of efficiency, accuracy, and actionable insights. Let’s see how in this article.

The Benefits of AI in Data Cataloging

AI revolutionizes data cataloging by automating and enhancing traditionally manual processes, thereby accelerating efficiency and improving data accuracy across various functions:

Automated Metadata Generation

AI algorithms autonomously generate metadata by analyzing and interpreting data assets. This includes identifying data types, relationships, and usage patterns. Machine learning models infer implicit metadata, ensuring comprehensive catalog coverage. Automated metadata generation reduces the burden on data stewards and ensures consistency and completeness in catalog entries. This capability is precious in environments with rapidly expanding data volumes where manual metadata creation could be more practical.

Simplified Data Classification and Tagging

AI facilitates precise data classification and tagging using natural language processing (NLP) techniques. By understanding contextual nuances and semantics, AI enhances categorization accuracy, which is particularly beneficial for unstructured data formats such as text and multimedia. Advanced AI models can learn from historical tagging decisions and user feedback to improve classification accuracy. This capability simplifies data discovery processes and enhances data governance by consistently and correctly categorizing data.

Enhanced Search Capabilities

AI-powered data catalogs feature advanced search capabilities that enable swift and targeted data retrieval. AI recommends relevant data assets and related information by understanding user queries and intent. Through techniques such as relevance scoring and query understanding, AI ensures that users can quickly locate the most pertinent data for their needs, thereby accelerating insight generation and reducing time spent on data discovery tasks.

Robust Data Lineage and Governance

AI is crucial in tracking data lineage by tracing its origins, transformations, and usage history. This capability ensures robust data governance and compliance with regulatory standards. Real-time lineage updates provide a transparent view of data provenance, enabling organizations to maintain data integrity and traceability throughout its lifecycle. AI-driven lineage tracking is essential in environments where data flows through complex pipelines and undergoes multiple transformations, ensuring all data usage is documented and auditable.

Intelligent Recommendations

AI-driven recommendations empower users by suggesting optimal data sources for analyses and identifying potential data quality issues. These insights derive from historical data usage patterns. Machine learning algorithms analyze past user behaviors and data access patterns to recommend datasets that are likely to be relevant or valuable for specific analytical tasks. By proactively guiding users toward high-quality data and minimizing the risk of using outdated or inaccurate information, AI enhances the overall effectiveness of data-driven operations.

Anomaly Detection

AI-powered continuous monitoring detects anomalies indicative of data quality issues or security threats. Early anomaly detection facilitates timely corrective actions, safeguarding data integrity and reliability. AI-powered anomaly detection algorithms utilize statistical analysis and machine learning techniques to identify deviations from expected data patterns.

This capability is critical in detecting data breaches, erroneous data entries, or system failures that could compromise data quality or pose security risks. By alerting data stewards to potential issues in real-time, AI enables proactive management of data anomalies, thereby mitigating risks and ensuring data consistency and reliability.

The Challenges and Considerations of AI in Data Cataloging

Despite its advantages, AI-enhanced data cataloging presents challenges requiring careful consideration and mitigation strategies.

Data Privacy and Security

Protecting sensitive information requires robust security measures and compliance with data protection regulations such as GDPR. AI systems must ensure data anonymization, encryption, and access control to safeguard against unauthorized access or data breaches.

Scalability

Implementing AI at scale demands substantial computational resources and scalable infrastructure capable of handling large volumes of data. Organizations must invest in robust IT frameworks and cloud-based solutions to support AI-driven data cataloging initiatives effectively.

Data Integration

Harmonizing data from disparate sources into a cohesive catalog remains complex, necessitating robust integration frameworks and data governance practices. AI can facilitate data integration by automating data mapping and transformation processes. However, organizations must ensure compatibility and consistency across heterogeneous data sources.

In conclusion, AI’s integration into data cataloging represents a transformative leap in data management, significantly enhancing efficiency and accuracy. AI automates critical processes and provides intelligent insights to empower organizations to exploit their data assets fully in their data catalog. Furthermore, overcoming data privacy and security challenges is essential for successfully integrating AI. As AI technology advances, its role in data cataloging will increasingly drive innovation and strategic decision-making across industries.

About Actian Corporation

Data Analytics

Will AI Take Data Analyst Jobs?

Dee Radh

July 3, 2024

Summary

This blog explores whether AI poses a threat to data analysts by automating core tasks and argues that AI elevates their roles, enabling deeper strategic analysis, storytelling, and ethical oversight.

AI automates routine work—such as cleaning data, querying databases, and generating basic reports—freeing analysts to focus on high-value tasks.
Human analysts remain essential for contextual insight, critical thinking, bias detection, and ethical considerations that AI cannot replicate.
The rising demand for analytics skills and AI-savvy professionals suggests data analyst roles will grow—not decline—as AI augments, not replaces, their work.

The rise of Artificial Intelligence (AI) has sparked a heated debate about the future of jobs across various industries. Data analysts, in particular, find themselves at the heart of this conversation. Will AI render human data analysts obsolete?

Contrary to the doomsayers’ predictions, the future is not bleak for data analysts. AI will empower data analysts to thrive, enhancing their ability to provide more insightful and impactful business decisions. Let’s explore how AI, and specifically large language models (LLMs), can work in tandem with data analysts to unlock new levels of value in data and analytics.

The Role of Data Analysts: More Than Number Crunching

First, it’s essential to understand that the role of a data analyst extends far beyond mere number crunching. Data analysts are storytellers, translating complex data into actionable insights that all decision makers can easily understand. They possess the critical thinking skills to ask the right questions, interpret results within the context of business objectives, and communicate findings effectively to stakeholders. While AI excels at processing vast amounts of data and identifying patterns, it lacks the nuanced understanding of business context and the ability to interpret data that are essential capabilities unique to human analysts.

AI as an Empowering Tool, Not a Replacement

Automating Routine Tasks

AI can automate many routine and repetitive tasks that occupy a significant portion of a data analyst’s time. Data cleaning, integration, and basic statistical analysis can be streamlined using AI, freeing analysts to focus on more complex and value-added activities. For example, AI-powered tools can quickly identify and correct data inconsistencies, handle missing values, and perform preliminary data exploration. This automation increases efficiency and allows analysts to delve deeper into data interpretation and strategic analysis.

Enhancing Analytical Capabilities

AI and machine learning algorithms can augment the analytical capabilities of data analysts. These technologies can uncover hidden patterns, detect anomalies, and predict future trends with greater accuracy and speed than legacy approaches. Analysts can use these advanced insights as a foundation for their analysis, adding their expertise and business acumen to provide context and relevance. For instance, AI can identify a subtle trend in customer behavior, which an analyst can then explore further to understand underlying causes and implications for marketing strategies.

Democratizing Data Insights

Large language models (LLMs), such as GPT-4, can democratize access to data insights by enabling non-technical stakeholders to interact with data in natural language. LLMs can interpret complex queries and generate understandable explanations very quickly, making data insights more accessible to everyone within an organization. This capability enhances collaboration between data analysts and business teams, fostering a data-driven culture where decisions are informed by insights derived from both human and AI analysis.

How LLMs Can Be Used in Data and Analytics Processes

Natural Language Processing (NLP) for Data Querying

LLMs can simplify data querying through natural language processing (NLP). Instead of writing complex SQL queries, analysts and business users can ask questions in plain English. For example, a user might ask, “What were our top-selling products last quarter?” and the LLM can translate this query into the necessary database commands and retrieve the relevant data. This capability lowers the barrier to entry for data analysis, making it more accessible and efficient.

Automated Report Generation

LLMs can assist in generating reports by summarizing key insights from data and creating narratives around them. Analysts can use these auto generated reports as a starting point, refining and adding their insights to produce comprehensive and insightful business reports. This collaboration between AI and analysts ensures that reports are both data-rich and contextually relevant.

Enhanced Data Visualization

LLMs can enhance data visualization by interpreting data and providing textual explanations. For instance, when presenting a complex graph or chart, the LLM can generate accompanying text that explains the key takeaways and trends in the data. This feature helps bridge the gap between data visualization and interpretation, making it easier for stakeholders to understand and act on the insights.

The Human Element: Context, Ethics, and Interpretation

Despite the advancements in AI, the human element remains irreplaceable in data analysis. Analysts bring context, ethical considerations, and nuanced interpretation to the table. They understand the business environment, can ask probing questions, and can foresee the potential impact of data-driven decisions on various areas of the business. Moreover, analysts are crucial in ensuring that data usage adheres to ethical standards and regulatory requirements, areas where AI still has limitations.

Contextual Understanding

AI might identify a correlation, but it takes a human analyst to understand whether the correlation is meaningful and relevant to the business. Analysts can discern whether a trend is due to a seasonal pattern, a market anomaly, or a fundamental change in consumer behavior, providing depth to the analysis that AI alone cannot achieve.

Ethical Oversight

AI systems can inadvertently perpetuate biases present in the data they are trained on. Data analysts play a vital role in identifying and mitigating these biases, ensuring that the insights generated are fair and ethical. They can scrutinize AI-generated models and results, applying their judgment to avoid unintended consequences.

Strategic Decision-Making

Ultimately, data analysts are instrumental in strategic decision-making. They can synthesize insights from multiple data sources, apply their industry knowledge, and recommend actionable strategies. This strategic input is crucial for aligning data insights with business goals and driving impactful decisions.

The End Game: A Symbiotic Relationship

The future of data analysis is not a zero-sum game between AI and human analysts. Instead, it is a symbiotic relationship where each complements the other. AI, with its ability to process and analyze data at unprecedented scale, enhances the capabilities of data analysts. Analysts, with their contextual understanding, critical thinking, and ethical oversight, ensure that AI-driven insights are relevant, accurate, and actionable.

By embracing AI as a tool rather than a threat, data analysts can unlock new levels of productivity and insight, driving smarter business decisions and better outcomes. In this collaborative future, data analysts will not only survive but thrive, leveraging AI to amplify their impact and solidify their role as indispensable assets in the data-driven business landscape.

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.

Data Management

Streamlining the Chaos: Conquering Manufacturing With Data

Kasey Nolan

July 2, 2024

depiction of conquering manufacturing with data

The Complexity of Modern Manufacturing

Manufacturing today is far from the straightforward assembly lines of the past; it is chaos incarnate. Each stage in the manufacturing process comes with its own set of data points. Raw materials, production schedules, machine operations, quality control, and logistics all generate vast amounts of data, and managing this data effectively can be the difference between smooth operations and a breakdown in the process.

Data integration is a powerful way to conquer the chaos of modern manufacturing. It’s the process of combining data from diverse sources into a unified view, providing a holistic picture of the entire manufacturing process. This involves collecting data from various systems, such as Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and Internet of Things (IoT) devices. When this data is integrated and analyzed cohesively, it can lead to significant improvements in efficiency, decision-making, and overall productivity.

The Power of a Unified Data Platform

A robust data platform is essential for effective data integration and should encompass analytics, data warehousing, and seamless integration capabilities. Let’s break down these components and see how they contribute to conquering the manufacturing chaos.

1. Analytics: Turning Data into Insights

Data without analysis is like raw material without a blueprint. Advanced analytics tools can sift through the vast amounts of data generated in manufacturing, identifying patterns and trends that might otherwise go unnoticed. Predictive analytics, for example, can forecast equipment failures before they happen, allowing for proactive maintenance and reducing downtime.

Analytics can also optimize production schedules by analyzing historical data and predicting future demand. This ensures that resources are allocated efficiently, minimizing waste and maximizing output. Additionally, quality control can be enhanced by analyzing data from different stages of the production process, identifying defects early, and implementing corrective measures.

2. Data Warehousing: A Central Repository

A data warehouse serves as a central repository where integrated data is stored. This centralized approach ensures that all relevant data is easily accessible, enabling comprehensive analysis and reporting. In manufacturing, a data warehouse can consolidate information from various departments, providing a single source of truth.

For instance, production data, inventory levels, and sales forecasts can be stored in the data warehouse. This unified view allows manufacturers to make informed decisions based on real-time data. If there’s a sudden spike in demand, the data warehouse can provide insights into inventory levels, production capacity, and lead times, enabling quick adjustments to meet the demand.

 3. Integration: Bridging the Gaps

Integration is the linchpin that holds everything together. It involves connecting various data sources and ensuring data flows seamlessly between them. In a manufacturing setting, integration can connect systems like ERP, MES, and Customer Relationship Management (CRM), creating a cohesive data ecosystem.

For example, integrating ERP and MES systems can provide a real-time view of production status, inventory levels, and order fulfillment. This integration eliminates data silos, ensuring that everyone in the organization has access to the same accurate information. It also streamlines workflows, as data doesn’t need to be manually transferred between systems, reducing the risk of errors and saving time.

Case Study: Aeriz

Aeriz is a national aeroponic cannabis brand that provides patients and enthusiasts with the purest tasting, burning, and feeling cultivated cannabis. They needed to be able to connect, manage, and analyze data from several systems, both on-premises and in the cloud, and access data that was not easy to gather from their primary tracking system.

By leveraging the Actian Data Platform, Aeriz was able to access data that wasn’t part of the canned reports provided by their third-party vendors. They were able to easily aggregate this data with Salesforce to improve inventory visibility and accelerate their order-to-cash timeline.

The result was an 80%-time savings of a full-time employee responsible for locating and aggregating data for business reporting. Aeriz can now focus resources on analyzing data to find improvements and efficiencies to accommodate rapid growth.

The Actian Data Platform for Manufacturing

Imagine having the ability to foresee equipment failures before they happen? Or being able to adjust production lines based on live demand forecasts? Enter the Actian Data Platform, a powerhouse designed to tackle the complexities of manufacturing data head-on. The Actian Data Platform transforms your raw data into actionable intelligence, empowering manufacturers to make smarter, faster decisions.

But it doesn’t stop there. The Actian Data Platform’s robust data warehousing capabilities ensure that all your critical data is centralized, accessible, and ready for deep analysis. Coupled with seamless integration features, this platform breaks down data silos and ensures a cohesive flow of information across all your systems. From the shop floor to the executive suite, everyone operates with the same up-to-date information, fostering collaboration and efficiency like never before. With Actian, chaos turns to clarity and complexity becomes a competitive advantage.

Embracing the Future of Manufacturing

Imagine analytics that predict the future, a data warehouse that’s your lone source of truth, and integration that connects it all seamlessly. This isn’t just about managing chaos—it’s about turning data into a well-choreographed dance of efficiency and productivity. By embracing the power of data, you can watch your manufacturing operations transform into a precision machine that’s ready to conquer any challenge!

About Kasey Nolan

Kasey Nolan is Solutions Product Marketing Manager at Actian, aligning sales and marketing in IaaS and edge compute technologies. With a decade of experience bridging cloud services and enterprise needs, Kasey drives messaging around core use cases and solutions. She has authored solution briefs and contributed to events focused on cloud transformation. Her Actian blog posts explore how to map customer challenges to product offerings, highlighting real-world deployments. Read her articles for guidance on matching technology to business goals.

Data Intelligence

The Role of Data Catalogs in Accelerating AI Initiatives

Actian Corporation

July 2, 2024

Ai Trading Ea Expert Advisors Machine Learning Analyze Business Data, 3d Illustration Robot Trading Graph Chart. Business Financial Investment Forex And Stock Exchange Digital Technology

In today’s data-driven landscape, organizations increasingly rely on AI to gain insights, drive innovation, and maintain a competitive edge. Indeed, AI technologies, including machine learning, natural language processing, and predictive analytics, transform businesses’ operations, enabling them to make smarter decisions, automate processes, and uncover new opportunities. However, the success of AI initiatives depends significantly on the quality, accessibility, and efficient management of data.

This is where the implementation of a data catalog plays a crucial role.

By facilitating data governance, discoverability, and accessibility, data catalogs enable organizations to harness the full potential of their AI projects, ensuring that AI models are built on a solid foundation of accurate and well-curated data.

First: What is a Data Catalog?

A data catalog is a centralized repository that stores metadata—data about data—allowing organizations to manage their data assets more effectively. This metadata, collected by various data sources, is automatically scanned to enable catalog users to search for their data and get information such as the availability, freshness, and quality of a data asset.

Therefore, by definition, a data catalog has become a standard for efficient metadata management and data discovery. We broadly define a data catalog as being:

A detailed inventory of all data assets in an organization and their metadata, designed to help data professionals quickly find the most appropriate data for any analytical business purpose.

How Does Implementing a Data Catalog Boost AI Initiatives in Organizations?

Now that we’ve briefly defined what a data catalog is, let’s discover how data catalogs can significantly boost AI initiatives in organizations:

Enhanced Data Discovery

The success of AI models is determined by the ability to access and utilize large, diverse datasets that accurately represent the problem domain. A data catalog enables this success by offering robust search and filtering capabilities, allowing users to quickly find relevant datasets based on criteria such as keywords, tags, data sources, and any other semantic information provided. These Google-esque search features enable data users to efficiently navigate the organization’s data landscape and find the assets they need for their specific use cases.

For example, a data scientist working on a predictive maintenance model for manufacturing equipment can use a data catalog to locate historical maintenance records, sensor data, and operational logs. This enhanced data discovery is crucial for AI projects, as it enables data scientists to identify and retrieve the most appropriate datasets for training and validating their models.

The Difference: Get highly personalized discovery experiences with the Actian Data Intelligence Platform. Our platform enables data consumers to enjoy a unique discovery experience via personalized exploratory paths by ensuring that the user profile is taken into account when ranking the results in the catalog. Our algorithms also give smart recommendations and suggestions on your assets day after day.

View our data discovery features.

Improved Data Quality and Trustworthiness

The underlying data must be of high quality for AI models to deliver accurate and reliable results. High-quality data is crucial because it directly impacts the model’s ability to learn and make predictions that reflect real-world scenarios. Poor-quality data can lead to incorrect conclusions and unreliable outputs, negatively affecting business decisions and outcomes.

A data catalog typically includes features for data profiling and data quality assessment. These features help identify data quality issues such as missing values, inconsistencies, and outliers, which can skew AI model results. By ensuring that only clean and trustworthy data is used in AI initiatives, organizations can enhance the reliability and performance of their AI models.

The Difference: Actian Data Intelligence Platform uses GraphQL and knowledge graph technologies to provide a flexible approach to integrating best-of-breed data quality solutions into our catalog. Sync the datasets of your third-party DQM tools via simple API operations. Our powerful Catalog API capabilities will automatically update any modifications made in your tool directly within our platform.

View our data quality features.

Improved Data Governance and Compliance

Data governance is critical for maintaining data integrity, security, and compliance with regulatory requirements. It involves the processes, policies, and standards that ensure data is managed and used correctly throughout its lifecycle. Regulatory requirements such as the GDPR in Europe and the CCPA in California, United States are examples of stringent laws that organizations must adhere to.

In addition, data governance promotes transparency, accountability, and traceability of data, making it easier for stakeholders to spot errors and mitigate risks associated with flawed or misrepresented AI insights before they negatively impact business operations or damage the organization’s reputation. Data catalogs support these governance initiatives by providing detailed metadata, including data lineage, ownership, and usage policies.

For AI initiatives, robust data governance means data can be used responsibly and ethically, minimizing data breaches and non-compliance risks. This protects the organization legally and ethically and builds trust with customers and stakeholders, ensuring that AI initiatives are sustainable and credible.

The Difference: Actian Data Intelligence Platform guarantees regulatory compliance by automatically identifying, classifying, and managing personal data assets at scale. Through smart recommendations, our solution detects personal information. It suggests which assets to tag – ensuring that information about data policies and regulations is well communicated to all data consumers within the organization in their daily activities.

View our data governance features.

Collaboration and Knowledge Sharing

AI projects often involve cross-functional teams, including data scientists, engineers, analysts, and business stakeholders. Data catalogs are pivotal in promoting collaboration by serving as a shared platform where team members can document, share, and discuss data assets. Features such as annotations, comments, and data ratings enable users to contribute their insights and knowledge directly within the data catalog. This functionality fosters a collaborative environment where stakeholders can exchange ideas, provide feedback, and iterate on data-related tasks.

For example, data scientists can annotate datasets with information about data quality or specific characteristics functional for machine learning models. Engineers can leave comments regarding data integration requirements or technical considerations. Analysts can rate the relevance or usefulness of different datasets based on their analytical needs.

The Difference: Actian Data Intelligence Platform provides discussion tabs for each catalog object, facilitating effective communication between Data Stewards and data consumers regarding their data assets. Shortly, data users will also be able to provide suggestions regarding the content of their assets, ensuring continuous improvement and maintaining the highest quality of data documentation within the catalog.

Common Understanding of Enterprise-Wide AI Terms

Data catalogs often incorporate a business glossary, a centralized repository for defining and standardizing business terms and data & AI definitions across an organization. A business glossary enhances alignment between business stakeholders and data practitioners by establishing clear definitions and ensuring consistency in terminology.

This clarity is essential in AI initiatives, where precise understanding and interpretation of data are critical for developing accurate models. For example, a well-defined business glossary allows data scientists to quickly identify and utilize the right data sets for training AI models, reducing the time spent on data preparation and increasing productivity. By facilitating a common understanding of data across departments, a business glossary accelerates AI development cycles and empowers organizations to derive meaningful insights from their data landscape.

The Difference: Actian Data Intelligence Platform provides data management teams with a unique place to create their categories of semantic concepts, organize them in hierarchies, and configure the way glossary items are mapped with technical assets.

View our Business Glossary features.

In Conclusion

In the rapidly evolving landscape of AI-driven decision-making, data catalogs have emerged as indispensable tools for organizations striving to leverage their data assets effectively. They ensure that AI initiatives are built on a foundation of high-quality, well-governed, well-documented data, which is essential for achieving accurate insights and sustainable business outcomes.

As organizations continue to invest in AI capabilities, adopting robust data catalogs will play a pivotal role in maximizing the value of data assets, driving innovation, and maintaining competitive advantage in an increasingly data-centric world.

On-Premises vs. Cloud Data Warehouses: 7 Key Differences

Location is the Biggest—But Not the Only—Differentiator

7 Primary Differences and Their Business Impact

Overall Cost Structure

Scalability

Deployment and Management

Control and Customization

Flexibility to Meet Workloads

Security and Compliance

Performance and Latency

Make Informed Buying Decisions With Confidence

Consider Both Approaches With a Hybrid Solution

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

3 Key Considerations for Crafting a Winning Data Quality Business Case

Understanding Data Quality

Recognizing the Need for Strong Data Quality

Benefits of High-Quality Data

3 Steps to Craft a Winning Data Quality Business Case

1. Assess Current Data Quality

2. Align Data Quality With Business Objectives

3. Track Progress and Measure Impact

About Traci Curran

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

The Developer’s Guide to Choosing the Right Embedded Database

Understanding Embedded Databases

Key Considerations for Database Selection

Types of Embedded Databases

1. Key-Value Stores

2. Document Stores

3. Relational Databases:

4. Time-Series Databases:

Database Selection for Embedded App Development

Mobile Apps

IoT Devices

Database Selection for Edge-to-Cloud Data Management

Edge Processing

Data Synchronization

Conclusion

About Kunal Shah

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Enhance Financial Decisions With Real-Time Data Processing

Embedded Databases for Edge Computing

Introducing Actian Zen–An Embedded Database for Use Cases at the Edge

The Family of Proven Zen Products

Key Features and Benefits for Edge Environments

Harness Edge Intelligence Today

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Get to Know Actian’s 2024 Interns

About Katie Keith

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Efficient Integrations: How to Slash Costs and Boost Efficiency

Optimizing Business Operations for Efficient Integration

Streamlining Data Management

Enhanced Business Insights

Automation and Efficiency

Reducing Manual Work With Automation

Improving Process Accuracy

Cost-Effective Reporting and Analytics