Note: This guide applies to running Spark jobs on any platform, including Cloudera platforms; cloud vendor-specific platforms – Amazon EMR, Microsoft HDInsight, Microsoft Synapse, Google DataProc; Databricks, which is on all three major public cloud providers; and Apache Spark on Kubernetes, which runs on nearly all platforms, including…

“The most difficult thing is finding out why your job is failing, which parameters to change. Most of the time, it’s OOM errors…”…

Summary:  DataOps is a series of principles and practices that promises to bring together the conflicting goals of the different data tribes in the organization, data science, BI, line of business, operations, and IT.  What has been a growing body of best practices is now becoming the basis for a new category of data access, blending, and deployment platforms that may solve data conflicts in your organization.

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Summary:  Some observations about new major trends and directions in data science drawn from the Strata+Hadoop conference in San Jose last week.

I’m fresh off my annual field trip to the Strata+Hadoop conference in San Jose last week.  This is always exciting, enervating, and exhausting but it remains the single best place to pick up on…

Written by Andy Palmer, CEO of Tamr

Why It’s Time to Embrace “DataOps” as a New Discipline

Over the past 10 years, many of us in technology companies have experienced the emergence of “DevOps.” This new set of practices and tools has improved the velocity, quality, predictability and scale of software engineering and deployment. Starting at the large internet companies, the trend towards DevOps is now…