In “Information Platforms and the Rise of the Data Scientist,” Jeff Hammerbacher describes Information Platforms as “the locus of their organization’s efforts to ingest, process, and generate information,” and how they “serve to accelerate the process of learning from empirical data.”
One of the biggest ingredients in the Information Platform built by Jeff’s team at Facebook was Hive, a framework for data warehousing on top of Hadoop. Hive grew from a need to manage and learn from the huge volumes of data that Facebook was producing every day from its burgeoning social network. After trying a few different systems, the team chose Hadoop for storage and processing, since it was cost-effective and met their scalability needs.
Hive was created to make it possible for analysts with strong SQL skills (but meager Java programming skills) to run queries on the huge volumes of data that Facebook stored in HDFS. Today, Hive is a successful Apache project used by many organizations as a general-purpose, scalable data processing platform.
Of course, SQL isn’t ideal for every big data problem—it’s not a good fit for building complex machine-learning algorithms, for example—but it’s great for many analyses, and it has the huge advantage of being very well known in the industry. What’s more, SQL is the lingua franca in business intelligence tools (ODBC is a common bridge, for example), so Hive is well placed to integrate with these products.
This chapter is an introduction ...