Pig and Hive are two major components of the Hadoop ecosystem that make it easier to write complicated Java MapReduce processes. However, most Hive developers are unsure about when to employ them. The answer is nearly identical to the one given previously.
- Hive is used mainly by data analysts whereas Pig is generally used by researchers and programmers.
- Hive is used for completely structured data whereas Pig is used for semi-structured data.
- Hive has a declarative ‘SQL’ish language (HiveQL) whereas Pig Hadoop Component has a procedural data flow language (Pig Latin)
- Hive Hadoop Component is mainly used for creating reports whereas Pig Hadoop Component is mainly used for programming.
- Hive Hadoop Component operates on the server side of any cluster whereas Pig Hadoop Component operates on the client side of any cluster.
- Hive Hadoop Component is helpful for ETL whereas Pig Hadoop is a great ETL tool for big data because of its powerful transformation and processing capabilities.
- Hive can start an optional thrift-based server that can send queries from any nook and corner directly to the Hive server which will execute them whereas this feature is not available with Pig.
Big Data and SQL 