ORC, or Optimized Row Columnar, is a popular big data file storage format. Its rise in popularity is due to it being highly performant, very compressible, and progressively more supported by top-level Apache products, like Hive, Crunch, Cascading, Spark, and more.
I recently wanted/needed to write ORC files from my Spark pipelines, and found specific documentation lacking. So, here’s a way to do it. Continue reading
This tutorial will accomplish a few key feats that make ingesting data to Hive far less painless. In this writeup, you will learn not only how to Sqoop a source table directly to a Hive table, but also how to Sqoop a source table in any desired format (ORC, for example) instead of just plain old text.
This post is the ninth in a hopefully substantive and informative series of posts about Apache Crunch, a framework for enabling Java developers to write Map-Reduce programs more easily for Hadoop.
As a developer/engineer in the Hadoop and Big Data space, you tend to hear a lot about file formats. All have their own benefits and trade-offs: storage savings, split-ability, compression time, decompression time, and much more. All of these factors play a huge role in what file formats you use for your projects, or as a team or company-wide standard. Continue reading