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Soft skills can be almost as important as data engineering skills when you apply for a job. Soft skills can make the difference between stress and efficiency or being unsatisfied with your position and a raise. When data engineers and data scientists earn bachelor’s degrees, they usually take classes in topics like data warehousing, programming languages, machine learning, and data science.

With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.

What do Google’s DevOps Research and Assessment (DORA) and Rollbar have to do with each other? DORA identified four key metrics to measure DevOps performance and identified four levels of DevOps performance from Low to Elite. One way for a team to become an Elite DevOps performer is by focusing on Continuous Code Improvement.

Gartner recognizes Fivetran as the “vendor of choice” for enterprises looking to modernize their data infrastructure.

With Databox, Achieve moved from a manual process of tracking data to one that allows them to motivate their team, eliminate room for errors, and ultimately make improvements.

Analyzing Return User Behaviour helps you optimize your site for better engagement and more conversions.

A true service mesh should focus on how to manage and orchestrate connectivity globally. Connecting a new service mesh for each use case is a much simpler problem to solve, but doing so won’t help you scale. You’ll just be throwing a service mesh in each cluster and calling it a day.  The more appealing solution is to stitch together environments.