DataOps: Maximizing Efficiency of your Data

As organizations generate data at an unprecedented pace, they also realize the importance of data assets for improving their business processes and their managerial decision making. Hence, many organizations have already undertaken steps towards their data-driven digital transformation. The latter includes the implementation of BigData infrastructures for managing data assets at scale, as well as the deployment of advanced analytics (e.g., machine learning and artificial intelligence) towards extracting insights from the data. Early adopters are struggling with the implementation of baseline BigData and data mining infrastructures. On the other hand, digitally mature enterprises are seeking ways to improve the effectiveness and scalability of their data-driven processes. This where DataOps (i.e., Data Operations) comes into play. DataOps is a new methodology for organizing and executing enterprise analytics processes, which emphasizes automation and scalability. It is a process-oriented methodology that aims at optimizing the productivity of data teams and subsequently the efficiency of their data pipelines.

DataOps bears similarities to the DevOps, not only because of its name, but also due to its emphasis on efficient communications between the members of the team and on continuous and automated integration of the data pipelines. Nevertheless, DataOps is focused on data rather than other aspects of an IT systems development and operation. Specifically, it streamlines entire data pipelines including data collection, data preprocessing, data analytics and data visualization steps. DataOps has recently emerged as a formal methodology, in response to the need for processing the proliferating volumes of enterprise data in efficient and cost-effective ways.

DataOps: Understanding the Rationale and the Benefits

As already outlined, DataOps is much about collaboration between the members of a data team. It defines a process that streamlines the collaboration between data providers, data engineers, data scientists, and end-users i.e., it involves all stakeholders of data-driven applications. It is also about automation, as it strives to automate the interactions between the above parties in the scope of data-driven business processes. Effective and automated communications between the above actors deliver the following benefits:

• Agility and faster response in an era of rapid change. In today’s competitive environment, organizations are forced to analyze data and to take decisions at very short timescales e.g., daily or even several times within a day. This is the reason why organizations try to collect and harness data almost in real-time. In this context, data analytics actors need to automate their interaction rather than having to repeat the same steps again and again.
• Management and analysis of diverse data sources. Most enterprise data feature BigData properties (i.e., large volume, high ingestion rates, extreme diversity, significant veracity). Hence, data-driven applications must be extremely flexible in consolidating and analyzing different types of data and data streams, regardless of their heterogeneity. Therefore, automation in handling and consolidating the different data types become essential. Companies must put in place standardized and automated processes for collecting, consolidating, and analyzing diverse data streams, rather than considering how to perform data ingestion and consolidation on a case-per-case basis. To this end, effective and streamlined communications between the different members of the data team are very important.
• Seamless selection and use of the best analytical function for the problem at hand. A DataOps environment can standardize and automate the ways different types of analytics functions are deployed over the collected and consolidated datasets. Such analytics functions range from simple rule-based processing to machine learning and artificial intelligence techniques. Each of the latter techniques has its own needs in terms of data preprocessing and preparation. A DataOps infrastructure can automate these preprocessing steps for different types of analytics techniques, including a variety of machine learning models. Hence, DataOps facilitates the selection and use of the best analytics model for the task at hand.

Steps to Successful DataOps Deployments

The development of a successful DataOps infrastructure, hinges on the following steps:

• Establishment of Automated Testing infrastructures and processes: An automated testing infrastructure is a cornerstone to streamlining the deployment and execution of data pipelines. Automated testing ensures that each new pipeline is sufficiently tested prior to its production deployment. Most importantly, it ensures that the testing process is fast and efficient i.e., without any manual and error-prone processes. Nevertheless, testing automation requires that new tests be produced and deployed for each new analytics function, such as the execution of new machine learning or deep learning models.
• Implementation of Version Control for all Artifacts of Data Pipelines: Automated testing must be accompanied by structured and disciplined version control. This is essential to reverting to previous working versions of a data pipeline when tests fail. It is also important for advancing the features and functionalities of the pipeline as part of new versions. Nevertheless, data pipelines consist of many different artifacts such as scripts, data integration code, data analytics codes, as well as a wide range of configuration files. Therefore, keeping track of the versions of a data pipeline boils down to tracking and tracing the versions of the different artifacts. Hence, to establish a successful DataOps infrastructure there is a need for disciplined version control of all the different artifacts that comprise data pipelines.
• Branching and Merging for New Features and Functionalities: In a dynamic DataOps environment, new data are constantly becoming available, while there is frequently a need for implementing and deploying new features and functionalities (e.g., new machine learning models). To this end, developers much be provided with easy ways to create new branches of code that deal with new data and implement new features. Moreover, these data must be flexibly merged to the core data pipeline and its truck code. Similar to DevOps, a responsive branch and merge infrastructure is essential to implementing DataOps.
• Facilitate the Configurability and Parameterization of Data Pipelines: DataOps teams must be very flexible in deploying different features and functionalities in an agile way. To this end, data pipelines must be as configurable as possible. Hence, a DataOps infrastructure must support the invocation of parametric pipelines. For example, it should enable the configuration of the data sources and the analytics functions to be used in the scope of a data pipeline.

Overall, DataOps is a novel agile paradigm for developing, configuring, and deploying data pipelines in modern enterprises. The adoption of this paradigm enables data teams to structure and deploy reusable data pipelines that can flexibly adapt to the dynamically changing requirements of data-driven processes. Therefore, companies had better explore the benefits of a transition to DataOps as part of their BigData and data analytics projects.