How observability built for data teams can unlock the promise of DataOps

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Nowadays, it's no exaggeration to say that every business is a data business. And if they aren't, they should be. That's why more and more organizations are investing in the modern data stack (think: Databricks and Snowflake, Amazon EMR, BigQuery, Dataproc).

However, these new technologies and the increasing business criticality of their data initiatives present significant challenges. Not only do today's data teams have to manage the sheer volume of data ingested daily from a wide variety of sources, they also need to be able to manage and monitor the tangle of thousands of applications interconnected and interdependent data.

The biggest challenge is managing the complexity of the intertwined systems we call the modern data stack. And as anyone who's spent time in the data trenches knows, deciphering data application performance, containing cloud costs, and mitigating data quality issues is no small task.

When something breaks down in these byzantine data pipelines, with no single source of truth to refer to, the finger pointing begins with data scientists blaming operations, operations blaming engineering, engineering blaming the developers - and so on and so forth in perpetuity.

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Is this the code? Insufficient infrastructure resources? A scheduling problem? Without a single source of truth that everyone can rally around, everyone uses their own tool, working in silos. And different tools give different answers - and untangling the threads to get to the heart of the problem takes hours (or even days).

Why modern data teams need a modern approach

Data teams today face many of the same challenges as software teams: a fractured team working in silos, under the gun to keep up with the accelerated pace of delivering more, faster, without enough people , in an increasingly complex environment.

Software teams have successfully overcome these obstacles through the discipline of DevOps. A big part of what enables DevOps teams to succeed is the observability provided by the next generation of application performance management (APM). Software teams are able to accurately and efficiently diagnose the root cause of problems, work collaboratively from a single source of truth, and enable developers to fix problems early - before software goes into trouble. production - without having to throw issues over the fence to the Ops team.

So why are data teams struggling while software teams are not? They basically use the same tools to basically solve the same problem.

Because, despite the generic similarities, observability for data teams is a completely different animal than observability for data teams.

Cost control is essential

First, consider that in addition to understanding the performance and reliability of a data pipeline, data teams also need to address the question of data quality: how can it be sure they are feeding their analytics engines with high-quality data? And, as more and more workloads move to an assortment of public clouds, it's also critical that teams are able to understand their data pipelines from a cost perspective.

Unfortunate...