Category Archives: TDM

Mastering Data Ops

Mastering the Data Pipeline: A Comprehensive Guide to DataOps

Introdution

In today’s ever-evolving business landscape, DataOps has become an essential practice for organizations looking to optimize their data management processes. As companies increasingly rely on data to inform decisions and gain a competitive edge, it is critical to effectively manage and integrate data assets across various teams, applications, and environments.

In this blog post, we will explore eight steps for achieving DataOps excellence and how it can revolutionize the way businesses manage their data. We will also discuss the advantages of DataOps and how it enables organizations to utilize data for strategic decision-making and innovation.

Whether you are a business leader or a data professional, this post provides the information necessary to implement DataOps successfully in your organization. So let’s delve into the steps of DataOps and explore how it can help businesses tackle their data management challenges.

The Eight Steps of Data Ops

Rapid Data Provisioning

DataOps can help accelerate the delivery of data to test environments, ensuring they remain up to date and relevant. It enables a repeatable, automated process to provision and update test data. This begins with establishing a source of truth that includes all necessary metadata and access control information. DataOps can then be used to coordinate the delivery of data, and monitor and report on the test environment’s performance. This will help developers and testers identify any issues quickly and take corrective action. By leveraging DataOps, organizations can ensure their development and testing cycles stay up to date.

Reduced Manual Data Operations

DataOps is a methodology that helps organizations streamline data collaboration while ensuring data protection. It automates the data lifecycle and uses test environments to replicate production data in a secure environment. Automated data governance processes can ensure that this data remains up to date and compliant with relevant regulations, enabling teams to get more value out of their data without compromising security. Additionally, DataOps’ automated data pipelines help deliver data products faster to stakeholders. In all, DataOps can be a powerful tool for organizations looking to streamline data collaboration while still protecting their data.

Efficient Data Collaboration & Strong Data Protection

DataOps is a methodology that helps organizations streamline data collaboration while ensuring data protection. It automates the data lifecycle and uses test environments to replicate production data in a secure environment. Automated data governance processes can ensure that this data remains up to date and compliant with relevant regulations, enabling teams to get more value out of their data without compromising security. Additionally, DataOps’ automated data pipelines help deliver data products faster to stakeholders. In all, DataOps can be a powerful tool for organizations looking to streamline data collaboration while still protecting their data.

Reduced Data Friction

Reduced Data Friction is a crucial element in achieving successful DataOps. To achieve this, automating the setup and provisioning of test environments is essential. It makes it easier to switch between different environments, enabling teams to efficiently test and develop before deploying to production. Containerization and virtualization technologies can also be utilized to create lightweight clones of production databases. These clones make the data required for development and testing more readily available, enabling teams to work more efficiently. Moreover, DataOps provides better visibility into data usage, streamlining data governance processes. This feature helps teams quickly identify and resolve potential issues. Additionally, automation can be used to ensure that data is handled according to established policies, reducing the risk of data loss or misuse. These measures ultimately reduce data friction, allowing teams to focus on their core tasks and speed up the development process.

Test Environment Managers play a significant role in the efficient and streamlined data management process. They can leverage these techniques to create a more productive work environment, enhancing the overall efficiency of the team. With the use of automated procedures, DataOps can help streamline the data governance process, providing teams with greater visibility and reducing the risks of data loss or misuse. Moreover, containerization and virtualization technologies can be used to create lightweight clones of production databases, allowing for faster and more efficient testing and development. By reducing data friction, teams can focus more on their core tasks, ultimately leading to a more productive and efficient development process.

Enhanced Data Self Service

The path to increased DataOps self-service capabilities starts with the ability to quickly and easily create test environments. Automated environment, and data, creation allows teams to spin up copies of production-like environments in minutes, instead of days or weeks. It also provides a platform for self-service, enabling them to create multiple dedicated copies of the same environment without relying on shared resources.

To make the most of automated environment creation, teams must have a way to tear down environments when they are done. This saves resources and ensures that only active environments are being paid for.

Having a self-service platform also means that teams will always use the same environment for testing, eliminating the need to reconfigure or worry about environmental drift. It provides consistent, repeatable, and reliable tests.

By providing teams with greater self-service capabilities in DataOps, they can more efficiently devise and execute testing plans which lead to faster time to market, improved customer experiences, and increased business value.

A Single Point of Control

A Single Point of Control is one of the major principles of DataOps. It gives organizations the ability to maintain control of the entire data pipeline, from development to operations. This ensures that any changes that need to be made to the data pipeline can be done quickly and without having to make changes in multiple environments. Additionally, this helps to mitigate any potential issues that may arise from running multiple different versions of the same data pipeline.

With a single point of control, teams can ensure that all data pipelines are running in the same test environment and that all data is going through the same processes and coming from the same source. This helps to guarantee that the data is always up to date and accurate. By embracing this principle, organizations can improve their DataOps and ensure that they are achieving the best results possible.

Simplified Data Cloud Migration

DataOps is a practice that facilitates streamlined data management processes, helping organizations to achieve agility and cost savings. A key element of a successful DataOps strategy for cloud migration is having a robust platform that enables secure and efficient data transfer. This platform can support quick replication and masking, allowing for the secure movement of data without manual processes or physical media. Additionally, automated synchronization capabilities ensure that data is available for analysis and decision-making as soon as possible, minimizing downtime during the migration process and reducing the risk of data loss or corruption. With an effective DataOps strategy, organizations can ensure their data is managed effectively throughout its lifecycle, enabling them to make the most of cloud computing.

By embracing DataOps and utilizing a robust data platform, organizations can simplify their cloud migration process and reap the benefits of the cloud. This includes greater agility, scalability, and cost savings – helping them stay ahead in an increasingly digital world. To make sure they are optimizing their data operations for maximum efficiency, companies should consider investing in a comprehensive and secure data platform. This can provide them with the support they need to ensure successful cloud migration and make the most of the opportunities that digital transformation offers.

DataOps for Cost Optimization

DataOps for cost optimization can offer a number of potential benefits. For example, organizations may be able to reduce the time and money required to maintain the data pipeline by leveraging automation and cloud-based solutions. Additionally, DataOps can help organizations identify opportunities for improving data quality that will also save costs in the long-term. Organizations can look to streamline their data pipelines by reducing the number of tools used and identifying areas for consolidation. DataOps can help organizations understand how their data is being utilized and make decisions on how to leverage that information in order to reduce costs. Moreover, DataOps can also evaluate existing storage solutions and recommend areas where resources may be able to be consolidated or optimized, leading to cost savings.

DataOps for cost optimization can help organizations achieve their goals of reducing costs and improving the bottom line. By leveraging automation and cloud-based solutions, organizations can reduce time and resources spent on maintenance, while identifying opportunities for improved data quality that will result in long-term savings. Additionally, DataOps can aid in the consolidation of resources, helping to make the data pipeline more cost-effective. Utilizing these strategies, DataOps can help organizations reduce overall costs associated with their data pipelines and maximize operational efficiency

Conclusion

In conclusion, mastering the data pipeline is an essential skill for any DataOps professional. To be successful, data engineers must be well–versed in the different components of the data pipeline and have a deep understanding of the technologies and tools used to manage the data. This comprehensive guide provides a great starting point for any DataOps professional, but it is important to continue to build upon your knowledge and skills. To further develop your data pipeline expertise, take a look at some of the leading DataOps tools, including Delphix, Enov8, and K2View.

Other DataOps Reading

DataOps Success Patterns

Top DataOps Pipeline Tools to Look at

Delphix:

Delphix is a data operations platform that breaks down data silos and accelerates data delivery. It helps organizations to quickly and securely access data in private clouds, on-premises, and in the public cloud. Delphix offers a range of data operations capabilities such as data masking, data virtualization, data protection, and data synchronization.

Enov8:

Enov8‘s TDM (Test Data Management) is a suite of tools designed to help organizations meet their data compliance requirements in the DevOps space. It provides a comprehensive set of features designed to automate and streamline the process of managing test data and ensuring data compliance. Features include data masking, data subsetting, data cloning, data virtualization, data comparison, data masking, and data privacy. Enov8 TDM provides a secure and reliable solution for organizations to ensure data compliance and reduce the risk of data breaches.

K2View:

K2View is a cloud-native data operations platform that enables organizations to automate and manage data operations across multiple clouds and data sources. It provides powerful data orchestration and data governance capabilities, as well as a range of tools for data integration, data validation, data masking, and more.
What Is Test Data Management

Test Data Management! The Anatomy & five tools to use.

Being part of the IT leadership in an organization has its advantages, but it also means you have to be familiar with technical “buzzwords”.

  • “Test Data Management” is one such term you might come across.

Do you know what it means and why TDM matters? And what about the available test data tools you can employ? If the answer to one or more of these questions is “no”, then this post is for you.

Let’s start by dissecting the expression into its various body parts. We’ll define each one and then reassemble the definitions. Once we’re done defining the term, we’ll get into the meat of the post by showing five existing test data tools that can help with test data management. Let’s get started!

Test Data Management: Breaking it Down

Let us break it down into its key components i.e. Test, Data & Management.

A definition of Testing.

Software testing is an investigation conducted to provide stakeholders with information about the quality of the software product or service under test. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not limited to, the process of executing a program or application with the intent of finding software bugs (errors or other defects).

A definition of Data.

Test data, unlike the sensitive data found in our production data, is any data that’s necessary for testing purposes. This includes test data for inputs, expected test data outputs, and test environment configuration details. Test data can come from a variety of sources, including production databases, synthetic data generators, and manual input.

A definition of Management

Management is the process of administering an organization, which can be a business, non-profit, or government body. This entails setting the organization’s goals and objectives and then coordinating the efforts of employees or volunteers to achieve these targets. The available resources that can be employed include financial, natural, technological, and human resources.

Bringing TDM Together

Now that we have the definitions for each word, it’s time to put all of them together to create a complete definition for “test data management.” Here it goes:

  • Test Data Management (TDM) is fundamentally test data preparation. It is the process of helping you prepare test data and maintain the test data in support of software testing. The goal of TDM is to provide a test environment that is as close to production as possible, and promotes data security while still being able to accurately test the software.

This may include, but is not be limited to underlying features like:

  • Test Data Profiling i.e. The Process of Discovery & Understanding your Data.
  • Test Data Preparation i.e. Generation of Realistic Test Data Using Automation to Fabricate Fake / Synthetic Data.
  • Test Data Security i.e. Using Production Data & Masking / Privacy Methods on the original production data. With the intent of ensuring “Personally Identifiable Information” (sensitive customer data) is removed and we prevent a data breach.
  • Test Data Provisioning i.e. Rapid Snapshotting, Cloning & Provisioning of Test Data/
  • Test Data Mining i.e. The ability to View and Access Valid Test Data.
  • Test Data Booking i.e. the ability to reserve Test Data for your engineering purpose

Here Are 5 Test Data Management Tools for Your Review

Here are five Data Generation Tools your organization can use to improve its approach to Test Data.

BMC (Compuware) File Aid

Compuware’s Test Data Management solution offers a standardized approach to managing test data from several data sources. Test Data Management with Compuware seeks to eliminate the need for extensive training by making it easy to create, find, extract, and compare data.

The solution can load subsets of related production data while maintaining database and application relationships. Test data management can help reduce the risk of errors, improve product quality, and shorten development timelines.

Broadcom (CA) Test Data Manager

Test Data Manager by Broadcom is a powerful test data management tool that enables organizations to manage their testing data more effectively and efficiently. Test Data Manager provides users with the ability to track, manage, and visualize their testing data in a centralized repository. Test Data Manager also offers features for managing test environments, managing test cases, and generating reports.

Enov8 Test Data Manager (DCS)

Enov8 Test Data Manager, originally known as DCS (Data Compliance Suite), is a Test Data Management platform that helps you identify where data security exposures reside, rapidly remediate these risks without error and centrally validate your compliance success. The solution also comes with IT delivery accelerators to support Data DevOps (DataOps), create test data, data mining, and test data bookings.

IBM InfoSphere Optim

IBM InfoSphere Optim is a tool that manages data at the business object level while preserving the relational integrity of the data and its business context. This allows you to easily create environments that precisely reflect end-to-end test cases by mirroring conditions found in a production environment.

InfoSphere Optim also offers other features such as data masking, ensuring data security, and subsetting, which can further help you reduce the risk of data breaches when testing in non-production environments.

Informatica Test Data Management

The test data management solution from Informatica, Test Data Management, is a tool that can identify ‘sensitive data,’ subset it, mask it, and create test data. It also allows developers and testers to save and share datasets to enhance overall efficiency.

Conclusion

As previously said, there are a lot of “buzzwords” in software engineering, and that trend isn’t going to change any time soon. Some of these words are simply fads. They seem like the “latest and greatest thing.” But just as quickly as the hip kids started using them, they fall out of favor.

However, Test data management isn’t one of those fads. It’s a process that your company must master and improve if it wants to stay competitive and promote values like Data Privacy. Test Data Management is essential in the understanding of data, it impacts our IT operations & project velocity & is key to our information security protocols.

In this article, we used a divide and conquer technique to define test data management. Test data management is the process of handling test data throughout the software development life cycle. Test data management tools help organizations manage this process by providing a way to store, track, and manipulate test data. There are many different test data management and data security solutions available on the market, each with its unique features and capabilities. So have a look & choose. Each is powerful and has its nuances. Look at the capabilities of each and decide which of the “Test Data Management” features are most important to you.

Author: Mark Dwight James

This post was written by Mark Dwight James. Mark is a Data Scientist specializing in Software Engineering. His passions are sharing ideas around software development and how companies can value stream through data best practices.

What Is Data Virtualization

Data has undergone a huge shift from being an unimportant asset to being the most valuable asset a company holds. However, just holding the data doesn’t bring many benefits to your organization. To reap the benefits of the data your company collects, data analysis helps you to find valuable insights for the data you hold.

Data lays at the core of many important business decisions. Many companies prefer a data-driven decision-making policy because it greatly reduces guessing and helps the company to shift toward a more accurate form of decision-making. This greatly benefits the company as you have more trust in the choices you make and you can reduce the number of “incorrect” decisions.

For example, say a product company wants to know if users like the new feature they’ve released. They want to decide if they need to make further improvements to the feature or not. To make a more informed decision, the product company collects user satisfaction scores about the new feature. The company can then use the average user satisfaction score to make this decision. Data virtualization helps you to quickly aggregate data from this survey, as well as other important data that influences the decision, in a single, centralized view. This allows your business to make more informed decisions quicker.

This article introduces you to the concept of data virtualization and how it can help your company to make better decisions. Before we start, what are the common problems companies experience with data?

Common Data Problems for Organizations

Here’s a list of data challenges companies commonly experience:

  • It’s hard to understand the data you’ve collected.
  • Different sources of data use different formats, which makes it harder to retrieve insights.
  • Your organization experiences data lag, which means that data isn’t directly available.
  • Your organization isn’t ready to handle and process data. This could be due to, for example, missing data infrastructure and tools.

Now that you’ve read the above data problems, make sure your organization is ready to handle and process data. So what is data virtualization?

What Is Data Virtualization?

Data virtualization is a form of data management that aggregates different data sources. For example, a data virtualization tool might pull data from multiple databases or applications. However, it’s important to understand that it doesn’t copy or move any of the data. You can have multiple data silos.

Data virtualization is capable of creating a single, virtual layer that spans all of those different data sources. This means your organization can access data much faster since there’s no need to move or copy data. Furthermore, this is a major benefit as you can access data in real time. Virtualization improves the agility of the system, and companies can run analytics faster, gaining insights quicker. For many companies, being able to retrieve insights faster is a great competitive advantage!

As mentioned, data virtualization doesn’t copy or move any data. It only stores particular meta information about the different locations of the data that you want to integrate into your data virtualization tool.

What Is the Importance of Data Virtualization?

First of all, data virtualization acts as the pinnacle of data integration. It allows an organization to integrate many different data sources into a single data model. This means companies can manage all of their data from a single, centralized interface.

Moreover, data virtualization is a great tool for collecting, searching, and analyzing data from different sources. Furthermore, as there’s no data copying involved, it’s also a more secure way of managing your data since you don’t have to transfer the data.

In other words, data virtualization helps companies to become more agile and use their data faster, creating a competitive advantage as you receive analytics and insights more quickly.

What Are the Capabilities of Data Virtualization?

This section describes the capabilities of data virtualization and why they matter for your business.

  1. Agility
    A data virtualization tool allows you to represent data in different ways, format data, discover new relationships between data, or create advanced views that provide you with new insights. The options are endless. Agility is the most important capability of data virtualization as it decreases the time to a solution.
  2. High performance
    A data virtualization tool doesn’t copy or move any data. This contributes to its high-performance nature. Less data replication allows for faster data performance.
  3. Caching
    Caching frequently used data helps you to further improve the performance of your data virtualization tool. Whenever you query for data or a specific data view, part of the data is already cached for you. This puts fewer constraints on your network and improves the availability of your data.
  4. Searchability
    A data virtualization tool allows you to create data views that provide you with actionable insights. Furthermore, data virtualization provides you with a single, centralized interface to search your data.

Next, let’s explore the benefits of data virtualization for your organization.

What Are the Benefits of Data Virtualization?

Here are 10 important benefits of employing a data virtualization tool for your organization.

  1. Helps with hiding the data complexity from the different underlying data sources, data formats, and data structures.
  2. Avoids replication of data to improve performance.
  3. Gives real-time data access and insights.
  4. Provides higher data security as no data is replicated or transferred.
  5. Reduces costs since no investments are needed in additional storage solutions.
  6. Allows for faster business decisions based on data insights.
  7. Reduces the need for development resources to integrate all different data sources.
  8. Allows for data governance to be applied efficiently. For example, data rules can be applied with a single operation to all different data sources.
  9. Improves data quality.
  10. Increases productivity as you can quickly integrate new data sources with your current data virtualization tool.

Now that we have a better understanding of the benefits of data virtualization, it’s time to get serious. The next section explains how you can implement data virtualization in your organization.

How to Get Started With Data Virtualization

Do you want to get started with data virtualization for your organization? The most important tip is to start small. Assign a dedicated team who spends time on integrating one or a couple of data sources. Start with data sources that are most valuable for your organization. This way, you’ll see the benefits of data virtualization quickly.

Next, when your team has completed some simple data integrations, it’s time to scale up your operations and use the tool for most of your data sources. You can think about more complex data models, integrate complex data sources, or use data sources with mixed data types.

Furthermore, you can start to experiment with caching to see where it can be applied effectively to gain the most performance benefits. Remember to apply caching to frequently used data or data models.

As a general rule of thumb, prioritize high-value data sources to reap the most benefits.

Conclusion

One final note: data virtualization isn’t the same as data visualization. The two terms are often used interchangeably, but they have very different meanings. Data virtualization isn’t focused on visualizing data. The main goal of data virtualization is to reduce the effort of integrating multiple data sources and providing your organization with a single, centralized interface to view and analyze data.

In the end, the real business value of data virtualization lays in the agility and faster access to data insights. For many organizations active in the industry of big data or predictive analytics, it’s a real competitive advantage to access insights faster than your competitors. This allows you to make profitable decisions faster than the competition.

If you want to learn more, the following YouTube video by DataAcademy further explains the concept of data virtualization in easy-to-understand terms.

Author

This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!