The term DataOps (like its older cousin, DevOps) means different things to different people. But one thing that everyone agrees on is its objective: accelerate the delivery of data-driven insights to the business quickly and reliably.

The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another. But instead of getting bogged down in the “what” of observability, I think it’s more valuable to address the “why.” What are we trying to accomplish with observability? What is the end goal?

In a recent webinar, Functional (& Funny) Strategies for Modern Data Architecture, we combined comedy and practical strategies for migrating from Hadoop to AWS. Unravel Co-Founder and CTO Shivnath Babu moderated a discussion with AWS Principal Architect, Global Specialty Practice, Dipankar Ghosal and WANdisco CTO Paul Scott-Murphy. Here are some of the key takeaways from the event.

One of the highlights of the DataOps Unleashed 2022 virtual conference was a roundtable panel discussion on building versus buying when it comes to your data stack. Build versus buy is a question for all layers of the enterprise infrastructure stack. But in the last five years — even in just the last year alone — it’s hard to think of a part of IT that has seen more dramatic change than that of the modern data stack.

The benefits of moving your on-prem Spark Hadoop environment to Databricks are undeniable. A recent Forrester Total Economic Impact (TEI) study reveals that deploying Databricks can pay for itself in less than six months, with a 417% ROI from cost savings and increased revenue & productivity over three years. But without the right methodology and tools, such modernization/migration can be a daunting task.