DataOps | A Complete Guide to DataOps

DataOps is an approach that has become increasingly popular in recent years as a way to manage and develop data in an efficient and reliable manner. This guide provides an overview of the DataOps methodology, its key principles, and the tools and best practices that are important for implementing it in your organization.

Why Do We Need DataOps?

The Organization of Data Teams Creates Frustration

Frustrated users and managers who want faster data deliveries and adjustments than what they have received from their traditional data warehouse is a common situation in many places. Instead of fast deliveries, new requirements often end up far back in the backlog behind issues that are more urgent. And what is urgent is typically tasks related to daily operations and necessary upgrades rather than value-creating activities.

But why does it end up this way? Well, the data warehouse team quickly becomes trapped by its own success and takes on the development of data flows, datasets, and reports for an ever-growing number of user environments. But everything needs to be maintained and kept alive, and eventually you end up with limited capacity to develop new things without consuming the entire IT budget.

But that is not all - Business ends up as the customer who is never satisfied and IT as the supplier who always prioritizes other things and misunderstands the needs. This creates friction.

Can we not do this in a different way?

More Complex Data Platforms and Data Products Require New Ways of Working

Data platforms have evolved quite drastically over the past ten years. We have seen a leap from traditional on-premise data warehouses with “one-size-fits-all” DBMS systems and ETL software, to data platforms that leverage SaaS/PaaS services with infinitely scalable object storage and database management systems designed for large analytical workloads.

The volume and variety of data has increased, and with it the need and desire for data and analytics has changed the landscape from traditional reporting to a broad set of use cases based on, among other things, AI/ML and delivery of data to other applications.

Data has become software. Software needs data. Then we must borrow methods from software development, which over time has matured faster than the data world traditionally has.

A Brief Introduction to the Concept of DataOps

Definition of DataOps

How much wiser one becomes from this definition is uncertain, but the essence can tentatively be summarized as follows:

1. The point of DataOps is to change how we collaborate around data and how data is used in the organization. The methodology is inspired by DevOps and Agile principles and is intended to improve the speed, efficiency, and quality of deliveries of various data products.
2. DataOps focuses on integrating rather than separating the development and operational responsibilities between different teams, so that faster and more responsive data deliveries are achieved. DataOps encourages cross-functional collaboration, learning from mistakes for continuous improvement, and supporting data-driven decision-making.
3. Central to DataOps is embracing change through testing, integration, and delivery of data in a continuous and automated manner.

The Benefits of DataOps

The main goal of DataOps is to improve the speed, quality, and reliability of data deliveries so that organizations can:

1. Reduce errors: Automation and continuous testing reduce the likelihood of errors in the data platform, data, and data deliveries.
2. Increase speed: Data products are delivered faster to users, which means that decisions can be made more quickly.
3. Improve collaboration: DataOps promotes closer collaboration between data professionals and users, which ensures that the data and solutions produced are more relevant and valuable for the organization.

Drawbacks of DataOps

There is little negative to say about DataOps, but it is a new way of thinking and working that requires both adaptation and investment.

If you want to learn more, you should read on, because DataOps requires a bit more explanation.