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DataOps in Details

DataOps in Details

DataOps or Data Operations was introduced in June 2014. The rapid growth of this concept has been beneficial to the data pipeline for the balance between data management and innovation. DataOps is a bit different from DevOps (which is explained later), although it uses some of the methodologies of DevOps for its benefit. But before learning what DataOps is, let’s quickly get to know about Data Analysis.

Data Analysis is the process of analyzing raw data, extracting useful trends and information from it, and making a model with that data, which helps in decision-making. Today, Data Analysis has become an essential part of business and scientific institutions to increase their decision-making efficiency in a more scientific approach.

Now let’s come back to DataOps. It is “an automated, process-oriented methodology” focused on improving the quality of data delivery and reducing data analysis cycle times. The methods used by DataOps are similar to those of DevOps. It is basically an application of DevOps to Analytics.

Here we get another term DevOps, which intends to shorten the system development process and increase the quality of software delivered. DevOps uses a practice set that combines Development and IT Operations (hence, DevOps). It uses the agile methodology to provide high-quality software continuously. While DevOps is related to Software development, DataOps is associated with Data Analytics.

DataOps also uses the agile methodology, along with DevOps and Lean Manufacturing (which minimizes waste without decreasing productivity), to help the analysts reduce the cycle time of analytics development, resulting in improved business goals. DataOps implements an automated, collaborative and process-oriented approach to design, implement and manage data workflows and maintain a distributed data architecture. The main objective of DataOps is the delivery of high value and managing risks.

Why DataOps?

Data teams working with data organizations are prone to constant data and analytics errors. This makes it difficult for the team members and slows the overall process, thus increasing the cycle time (the time taken to deploy finished analytics after the proposal of a new idea). The cycle time of analytics may grow for many other reasons like lousy teamwork and lack of coordination and collaboration, delayed data access, inflexible data architectures, slow processing to increase quality, approvals by higher authorities, etc.

DataOps helps in solving all these problems through its agile, automated, and process-driven approach. It encourages collaboration and communication among the data teams and rapidly uses advanced technologies to automate the processes of data management and operations. All this is done so that it decreases the cycle time while increasing the product quality. Data is considered an open-source or shared asset in a DataOps domain, so all models must abide by the end-to-end design and thinking approach.

DataOps uses Statistical Process Control (SPC) for consistent observation and verification of the data pipeline. It ensures the availability of statistics and increases the data processing efficiency and quality. The SPC monitor sends an alert in case of any error.

DataOps Platforms and Tools

By 2025, data is expected to grow over 180 Zettabytes, with the Compound Annual Growth Rate of 23%. This is an excellent motivation for organizations to adapt DataOps platforms and tools to manage these enormous amounts of valuable data.

DataOps platforms bind everything from data intake to analytics and report by enabling complete end-to-end data control. On the other hand, DataOps tools aim at one of the six capabilities of DataOps.

Meta-Orchestration: The capacity to organize complex information pipelines, toolchains, and tests across teams, areas, and server centers.
Testing and Data Observability: The capability to screen applications, analyze the production, and validate new analytics before deployment.
Sandbox Creation and Management: The ability to make a temporary self-service environment for analysis and the tools to recapitulate ideas made in those sandboxes.
Continuous Deployment: The capacity to develop, test, and implement to production environments.
Collaboration and Sharing: Encourage more collaboration and sharing by using the end-to-end view of the whole analytic system.
Process Analytics: The capacity of measurement of analytics processes to understand the shortcomings and upgrades over time.

Implementation of DataOps

The first requirement to implement DataOps is to make the data readily available and accessible. Next, the accessible data needs software, platform, and tools to get organized and integrated with newer systems. Only then the new data will get continuously processed, performance will be monitored, and real-time insights will be produced.

Some of the best practices to be considered during the implementation of DataOps are:

Measuring performance and benchmarking all the stages of the data lifecycle.
Semantics rules for data and metadata must be explained.
Data validation through feedback loops.
Automate as much work as possible through advanced technologies, data science tools, and business intelligence data platforms.
Solving Bottleneck and data silos issues by optimization of processes.
Growth, evolution, and scalability designing.
Emulation of the actual production environment for experimentation by the use of disposable environments.
DataOps teams must have a variety of technical skills and backgrounds.
Improving efficiency constantly by the use of Lean Manufacturing.
Democratizing data to provide free access increases collaboration and productivity.

There are three main roles within DataOps: Data Suppliers, Data Preparers, and Data Consumers. All these roles need to interact, collaborate and work hand-in-hand for getting the most efficient output.

Benefits of DataOps

Adopting the DataOps model can be beneficial for organizations in many ways:

It serves immediate data insights due to the acceleration of data operations.
Minimizes cycle times of data science applications to get quick results.
Improves the collaboration and communication among data teams.
Predicts all possible scenarios by data analytics, thus boosting transparency.
Reproducible processes and reusable code, which result in increased efficiency.
High Data Quality.
Unified and interoperable data hub.
Due to automation, errors and anomalies are greatly nullified.

Conclusion

The implementation of DataOps is beneficial for the future, where data teams would be required to handle enormous volumes of data and to provide fast and highly accurate analytics and quality products. The automated data management model helps save a lot of time for the analysts so that they can focus more on innovation and quality production. Although the implementation process needs quite some investment, it’s profitable in the long run. As more and more companies try to control large data, getting the best quality through the right data processes will be the key to success.