DataOps data operations data pipeline

The concept of DataOps and the main elements of this concept

The introduction to the concept of DataOps for TAdviser was prepared by Svetlana Vronskaya, author of the telegram channel Analytics Now.

DataOps is the equivalent DevOps for the data. And also, as the goal of DevOps is to organize a continuous process of development and launch, the software goal of DataOps is to organize continuous and unhindered access to and extraction of useful information from data.

We can say that DataOps is a concept, a set of practices for the continuous integration of data between processes, teams and systems.

The DataOps infrastructure consists of five main elements:

• Technologies (especially data and data sources);
• Adaptive architecture that ensures continuous improvement of technologies, services and processes;
• Enrichment of data for accurate analysis;
• Methodology DataOps for the construction and deployment of analytics and data pipelines;
• Culture and people.

Perhaps the last part is the most difficult, because in order for DataOps to work, it is necessary to create a culture of cooperation between the teams responsible for the operation of IT infrastructure, clouds, architecture and data structure, as well as data consumers, for example, analysts, processing specialists and business users.

The DataOps process itself consists of 5 steps. Before you begin with them, you must fulfill one condition - collect user requirements, define project goals, data use cases and performance indicators. Actually, like in any project. The first step is data collection. Next, structuring the data, then analyzing and enriching the data. The fourth step is to implement data models into applications using reusable templates. And the last step is the automation of quality control.

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