Finding Savings in Your Lower Environments – ROI of Test Environments & Test Data


The Unseen Cost of Software Development Did you know that organizations typically spend over 50% of their IT environment budget on testing? While the spotlight often shines on development and production, lower environments—like testing and staging—are essential yet often overlooked areas for cost-saving. This article aims to shed light on two crucial aspects: Test Environment Management (TEM) and Test Data Management (TDM), which are critical for both enhancing organizational IT delivery and controlling IT costs.

Why Lower Environments Matter

The Backbone of Quality Software Lower environments are crucial for software testing, data validation, and quality assurance. However, these environments are complex to manage and can become cost centers if not handled efficiently. From hardware and software licensing to personnel and service costs, these areas offer tremendous potential for optimization.

ROI of Test Environment Management (ROI of TEM)

Unlock Savings Through Efficient Test Environments

  • Unlock Savings Through Efficient Test Environments
    • Reduction in Infrastructure Costs: TEM enables you to make better use of existing resources. Virtualization technologies let you run multiple test environments on a single server, reducing hardware costs.
    • Reduction in License Costs: By optimizing software licenses across test environments, you can cut down the number of required licenses, leading to substantial savings.
    • Reduction in Service Costs: Automated setup and configuration processes can significantly cut down on service costs related to environment management.
    • Reduction in Headcount: Automation also reduces the need for manual labor, thus allowing your employees to focus on value-added tasks and leading to staffing efficiencies.

Useful Tool: For a thorough evaluation, consider using ROI calculators like the one offered by Enov8. It can provide you with tailored insights into potential cost savings.

ROI of Test Data Management (ROI of TDM)

Optimize Your Test Data for Maximum Efficiency

  • Reduction in Infrastructure Costs: TDM allows you to minimize the need to replicate entire production environments, reducing hardware and storage costs.
  • Enhancement in Tester Productivity: Quick provisioning of accurate and compliant test data can lead to faster testing cycles.
  • Boost in Developer Productivity: With streamlined access to quality data, developers can focus on coding rather than data management, resulting in quicker development cycles.
  • Minimization of Support Costs: Better quality of test data can lead to more reliable testing outcomes, reducing the cost of post-launch support and rework.
  • Averting Data Breach Costs: By using de-identified data for testing, the risks of data breaches are minimized, thereby potentially saving significant costs related to data security incidents.

Synergies Between TEM and TDM

Better Together for Greater Savings Combining TEM and TDM offers synergistic benefits. For example, TEM’s efficient hardware utilization can be maximized when paired with TDM’s efficient data management. By synchronizing these two functions, organizations can achieve cumulative ROI that is greater than the sum of their parts.

Action Steps for Organizations

Your Roadmap to Implementation

  1. Conduct a Pilot Project: Start small to understand the benefits and challenges.
  2. Engage Stakeholders: Get buy-in from relevant departments.
  3. Choose the Right Tools: Look for platforms that align with your organization’s specific needs and risk tolerance.


By focusing on the ROI of both TEM and TDM, organizations can unlock significant savings while maintaining, or even enhancing, the quality of software delivery. In a world where every dollar counts, these lower environments offer a fertile ground for impactful cost optimization.

database cloning

Understanding Data Cloning: A Guide for Beginners

Data Cloning, alternatively referred to as Database Virtualization, is a sophisticated technique that encompasses the process of capturing snapshots of authentic data, subsequently resulting in the creation of miniature, albeit fully functional and operational, replicas. These compact and efficient duplicates are subsequently and expeditiously provisioned into the designated Development and Test Environments, streamlining the process of testing and ensuring the integrity of the original dataset remains uncompromised.

The Cloning Process

There are four main steps:

  1. Ingest the Source Data
  2. Snapshot the Data
  3. Replicate the Data
  4. Provision the Data to new Environments

Behind the Scenes Cloning usually employs ZFS or HyperV technologies, which allow you to transition from traditional backup and restore methods that can take hours.

Utilizing ZFS or HyperV enables database provisioning to be 100 times faster and ten times smaller.

What is ZFS?

ZFS, short for Zettabyte File System, is a revolutionary file system that places a strong emphasis on data integrity, reliability, and ease of management. It was initially developed by Sun Microsystems and is now maintained as an open-source project. As a file system, ZFS not only guarantees data integrity by using advanced error detection and correction mechanisms but also supports snapshotting, a feature that allows for the efficient creation of point-in-time representations of the data stored within the system.

ZFS is unique in that it combines the roles of a traditional file system and a volume manager, which simplifies storage management tasks and reduces complexity. This integrated approach allows for advanced features such as data compression, deduplication, and the ability to create and manage storage pools. Furthermore, ZFS’s inherent copy-on-write functionality ensures that data is never overwritten, safeguarding against data corruption and enabling easy recovery in the event of an issue.

What is HyperV?

HyperV, also known as Microsoft Hyper-V or simply Hyper-V, is a virtualization technology developed by Microsoft that allows users to create, manage, and run multiple virtual machines (VMs) on a single physical host. This capability enables the efficient utilization of hardware resources, as multiple operating systems and applications can coexist and run concurrently on a single server. Hyper-V is an integral component of Microsoft’s Windows Server product line and is also available as a standalone product, known as Hyper-V Server.

One of the key features of Hyper-V is its support for snapshotting, which allows administrators to capture the state of a virtual machine at a specific point in time. These snapshots can include the VM’s memory, virtual disks, and hardware configuration. The snapshot functionality is particularly useful for tasks such as testing software updates, rolling back to a previous state in case of an error, or creating point-in-time backups for disaster recovery.

Problem Statement

Traditional backup methods often involve manual processes that can be time-consuming, taking hours or even days to complete. While these backups are in progress, the data being backed up is typically inaccessible, which can lead to significant operational challenges when immediate access to the data is necessary for ongoing business activities or critical decision-making.

Moreover, the storage requirements for these traditional backup and restore operations can be substantial. Since the process creates a full, 100% copy of the original source data, the storage demands can quickly escalate. For example, a 5 TB database would necessitate an additional 15 TB of disk space if three separate restore points were required. This considerable storage overhead not only adds to the overall cost of maintaining the backup infrastructure but also has implications for the time and resources needed to manage and maintain the storage environment.

Benefits of Data Cloning

Data Cloning involves generating a snapshot, or copy, of data for backup, analysis, or engineering purposes, either in real-time or as part of a scheduled routine. Data clones facilitate the provisioning of new databases and testing changes to production systems without impacting live data.


  • Clones can be employed for development and testing without affecting production data
  • Clones consume minimal storage, averaging about 40 MB, even for a 1 TB source
  • The Snapshot & Cloning process is completed in seconds rather than hours
  • Clones can be restored to any point in time by bookmarking
  • Simplifies end-to-end data management


  • The technology required for cloning can be complex

However, various user-friendly tools on the market can mitigate this complexity.

Data Cloning Tools

Besides building your own solution, commercial cloning options include:

  • Delphix
  • RedGate SQL Clone
  • Enov8 vME (VirtualizeMe)
  • Windocks

Each tool offers unique features and benefits. It’s crucial to understand your data environment and objectives before making a final decision.

Data Cloning Use Cases

  1. DevOps: Data cloning creates exact copies of datasets for backups or replicating test data in Test Environments for development and testing.
  2. Cloud Migration: Data cloning offers a secure and efficient method for transferring TB-size datasets from on-premises to the cloud, enabling space-efficient data environments for testing and cutover rehearsal.
  3. Platform Upgrades: Data virtualization reduces complexity, lowers total cost of ownership, and accelerates projects by delivering virtual data copies to platform teams more efficiently than traditional processes.
  4. Analytics: Data clones facilitate query and report design and provide on-demand access to integrated data across sources for BI projects without compromising the original dataset.
  5. Production Support: Data cloning helps teams identify and resolve production issues by supplying complete virtual data environments for root cause analysis and change validation.

In Conclusion

Data cloning, as a cutting-edge technique, facilitates the generation of precise duplicates of datasets for a diverse array of applications, including but not limited to, producing backups or replicating crucial data to be utilized in the realms of development and testing. The intrinsic capability of data clones to expedite the provisioning process for new databases, as well as to rigorously test alterations made to production systems without causing any disruptions or adverse effects on live data, underscores the value of this approach in modern data management practices.

By employing data cloning, organizations can achieve increased efficiency, heightened agility, and greater flexibility in managing their data resources, thereby ensuring a more streamlined and effective approach to handling the ever-growing demands of data-driven operations and decision-making processes.

Mastering Data Ops

Mastering the Data Pipeline: A Comprehensive Guide to DataOps


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


In conclusion, mastering the data pipeline is an essential skill for any DataOps professional. To be successful, data engineers must be wellversed 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 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‘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 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.