Apache Spark is the de facto framework of the big data world. Any serious organization that’s dealing with big data uses Spark almost exclusively. Though, it has some caveats. For the starter, it’s hard to use. And it’s very confusing to get started with, even for those with a solid …

The post MySQL table to JSON with 10 lines of Spark appeared first on Geeky Hacker.

Fast, granular, reliable ROI on ad performance was our bugle call to build Euclid, Uber’s in-house marketing platform. Early this year, Euclid replaced a legacy system, which processed ROI data somewhat manually as it struggled to keep up with Uber’s …

The post Designing Euclid to Make Uber Engineering Marketing Savvy appeared first on Uber Engineering Blog.

In a world where new data processing languages appear every day, it can be helpful to have tutorials explaining language characteristics in detail from the ground up.  This blog post is not such a tutorial.   It also isn’t a tutorial on getting started with MySQL or Hadoop, nor is it a list of best practices for the various languages I’ll reference here – there are bound to be better ways to accomplish certain tasks, and where a choice was required, I’ve emphasized clarity and readability over performance.  Finally, this isn’t meant to be a quickstart for SQL experts to access Hadoop – there are a number of SQL interfaces to Hadoop such as Impala or Hive that make Hadoop incredibly accessible to those with existing SQL skills.

Instead, this post is a pale equivalent of the …