Although Pig can be quite a powerful and simple language to use, the downside is that it’s something new to learn and master. Some folks at Facebook developed a runtime Hadoop® support structure that allows anyone who is already fluent with SQL (which is commonplace for relational data-base developers) to leverage the Hadoop platform right out of the gate.
Their creation, called Hive™, allows SQL developers to write Hive Query Language (HQL) statements that are similar to standard SQL statements; now you should be aware that HQL is limited in the commands it understands, but it is still pretty useful. HQL statements are broken down by the Hive service into MapReduce jobs and executed across a Hadoop cluster.
For anyone with a SQL or relational database background, this section will look very familiar to you. As with any database management system (DBMS), you can run your Hive queries in many ways. You can run them from a command line interface (known as the Hive shell), from a Java Database Connectivity (JDBC) or Open Database Connectivity (ODBC) application leveraging the Hive JDBC/ODBC drivers, or from what is called a Hive Thrift Client. The Hive Thrift Client is much like any database client that gets installed on a user’s client machine (or in a middle tier of a three-tier architecture): it communicates with the Hive services running on the server. You can use the Hive Thrift Client within applications written in C++, Java, PHP, Python, or Ruby (much like you can use these client-side languages with embedded SQL to access a database such as DB2 or Informix).
Hive looks very much like traditional database code with SQL access. However, because Hive is based on Hadoop and MapReduce operations, there are several key differences. The first is that Hadoop is intended for long sequential scans, and because Hive is based on Hadoop, you can expect queries to have a very high latency (many minutes). This means that Hive would not be appropriate for applications that need very fast response times, as you would expect with a database such as DB2. Finally, Hive is read-based and therefore not appropriate for transaction processing that typically involves a high percentage of write operations.
If you're interested in SQL on Hadoop, in addition to Hive, IBM offers Big SQL which makes accessing Hive datasets faster and more secure. Checkout our videos, below, for a quick overview of Hive and Big SQL.
ebook: Understanding Big Data Beyond the Hype
Stay on top of all the changes including, Hadoop-based analytics, streaming analytics, warehousing (including BigSQL), data asset discovery, integration, and governance