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…Continue
Added by Sara Petrie on October 21, 2021 at 9:30am — No Comments
“The most difficult thing is finding out why your job is failing, which parameters to change. Most of the time, it’s OOM errors…”…Continue
Added by Sara Petrie on September 30, 2021 at 6:00am — No Comments
Added by Olha Zhydik on April 5, 2021 at 7:30am — No Comments
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.
Summary: Some observations about new major trends and directions in data science drawn from the Strata+Hadoop conference in San Jose last week.
Added by William Vorhies on March 20, 2017 at 4:48pm — No Comments
Written by Andy Palmer, CEO of Tamr
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…Continue