For your organization to become data-driven, it needs clean, curated, comprehensive data. And as more businesses recognize the strategic value of becoming data-driven, they are also recognizing challenges that come along with managing the ever-increasing volume, variety, and velocity of data. That’s why many organizations today are embracing an emerging discipline called DataOps.
In 2015, I defined DataOps as “a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists and other data professionals.”
And seven years later, this definition remains true. In fact, DataOps may be even more relevant in today’s dynamic business environment.
DataOps helps organizations rapidly deliver data that not only accelerates analytics but also enables analytics that were previously deemed impossible by acknowledging the interconnected nature of data engineering, data integration, data quality, and data security/privacy.
DevOps vs. DataOps
If all of this sounds familiar, then it’s probably because you’ve heard of DevOps, the set of practices and tools embraced by many software development organizations to improve the velocity, quality, predictability, and scale of software engineering and deployment.
At its core, DevOps is about the combination of software engineering, quality assurance and technology operations. DevOps emerged because traditional systems management wasn’t remotely adequate to meet the needs of modern, web-based application development and deployment.
When you compare DataOps vs. DevOps, you’ll find there are similarities. But there is a fundamental difference between them as well.
DevOps focuses on software development, quality assurance, and technology operations. Its goal is to help development organizations better meet the needs of modern, web-based application development and deployment.
DevOps in the Enterprise
DataOps, on the other hand, is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals. Its goal: quickly deliver data and accelerate analytics.
And while it’s true that people have been managing data for a long time, we’re at a point now where businesses can no longer manage the quantity, velocity, and variety of data available without a significant change in the fundamental infrastructure.
That’s why when data teams embrace DataOps, they can streamline the process of deploying code, without the worry of breaking what’s already in production. And because the size and complexity of production data pipelines varies widely – from simple exports to complex flows consisting of moving, merging, and aggregating multiple sources and fields and generating personalized dashboards – having defined processes in place is critical to helping data teams avoid burnout and realize benefits such as the ability to deliver new applications, faster.
Two Trends Driving the Need for DataOps
The democratization of analytics, which is giving more individuals access to cutting-edge visualization, data modeling, machine learning, and statistics. The massive increase in self-service BI and analytics initiatives and the rise in popularity of these tools validate the desire of organizations to provide more access to better data.
The implementation of “built-for-purpose”tools, which radically improve the performance and accessibility of large quantities of data at unprecedented velocities. Tamr co-founder Mike Stonebraker has been arguing convincingly for years that “one size does not fit all.” On the one hand, cloud data warehouses like Snowflake have grown rapidly. And on the other hand, data lakehouses like Databricks are also adding customers faster than ever before. This heterogeneity in tools is likely to stay in the data stack.
Together these trends create “pressure from both ends of the stack.” From the top of the stack, more users want access to more data from more systems in more combinations. And from the bottom of the stack, more data is available than ever before — some aggregated (but much of it not). The only way for data professionals to deal with pressure of heterogeneity from both the top and bottom of the stack is to embrace a new approach to managing data that blends operations and collaboration to organize and deliver data from many sources to many users reliably with the provenance required to support reproducible data flows.
And the “ops” in DataOps is very intentional. The operation of infrastructure required to support the quantity, velocity, and variety of data available in enterprises today is radically different from what traditional data management approaches have assumed. The nature of DataOps embraces the need to manage MANY data sources and MANY data pipelines with a wide variety of transformations.
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