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