DataOps Overview
Published October 21, 2024
The image above illustrates an overview of DataOps. The image is taken from the article The Rise of DataOps: Governance and Agility with TrueDataOps, published by Snowflake.
DataOps
DataOps is an approach that has gained significant popularity in recent years as an efficient and reliable method for managing and developing data. This guide provides an overview of the DataOps methodology, its key principles, and the tools and best practices essential for implementing it in your organization.
Why do we need DataOps?
The organization of data teams creates frustration
A common scenario in many organizations involves frustrated users and managers who desire faster data deliveries and adjustments than their traditional data warehouse can provide. Instead of quick deliveries, new requirements often end up at the back of the backlog, behind more pressing issues. These pressing matters typically relate to daily operations and necessary upgrades rather than value-creating activities.
This situation arises because data warehouse teams quickly become victims of their success. They take on the development of data flows, datasets, and reports for an increasing number of user environments. However, all these systems must be maintained and kept operational, eventually leading to limited capacity for developing new solutions without consuming the entire IT budget.
Consequently, the business side becomes the perpetually unsatisfied customer, while IT is perceived as the supplier who always prioritizes other tasks and misunderstands needs. This dynamic creates friction.
The question arises: Is there a better way to approach this?
More complex data platforms and data products require new ways of working
Data platforms have undergone a dramatic evolution over the past decade. We've witnessed a shift from traditional on-premise data warehouses with "one-size-fits-all" DBMS systems and ETL software to data platforms that leverage SaaS/PaaS services. These modern platforms offer infinitely scalable object storage and database management systems designed for large analytical workloads.
The increased volume and variety of data, coupled with the growing need and desire for advanced analytics, have transformed the landscape. We've moved from traditional reporting to a wide array of use cases based on AI/ML and the delivery of data to other applications.
Data has become software, and software needs data. Consequently, we must adopt methods from software development, which has matured more rapidly than the traditional data world.
A Brief Introduction To The Concept of DataOps
Definition of DataOps
Definition source: Gartner
"DataOps is a collaborative methodology that focuses on improving communication, integration, and automation of data flow between data developers and data users in an enterprise.
The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts. DataOps uses technology to automate the design, deployment, and management of data collection, and uses metadata to improve the usability and value of data in a changing environment."
While this definition may seem complex, the essence of DataOps can be summarized as follows:
DataOps aims to transform how we collaborate around data and how data is utilized within an organization. The methodology draws inspiration from DevOps and Agile principles, focusing on enhancing the speed, efficiency, and quality of various data product deliveries.
Rather than separating development and operational responsibilities between different teams, DataOps emphasizes integrating these functions to achieve faster and more responsive data deliveries. It encourages cross-disciplinary collaboration, learning from mistakes for continuous improvement, and supporting data-driven decision-making.
A core aspect of DataOps is embracing change through continuous and automated testing, integration, and delivery of data.
The Benefits of DataOps
The primary objective of DataOps is to improve the speed, quality, and reliability of data deliveries, enabling businesses to:
Reduce errors: Through automation and continuous testing, DataOps minimizes the likelihood of errors in the data platform, data, and data deliveries.
Increase speed: Data products reach users faster, facilitating quicker decision-making processes.
Improve collaboration: DataOps fosters closer cooperation between data professionals and users, ensuring that the produced data and solutions are more relevant and valuable to the business.
Challenges of DataOps
While there are a few inherent drawbacks to DataOps, it does represent a new way of thinking and working that requires both adaptation and investment. Organizations need to be prepared for a cultural shift and potential upfront costs in terms of time and resources.
To fully grasp the potential of DataOps and its implications, it's important to delve deeper into its principles and practices.