Apache Pig vs Apache Hive – Top 12 Useful Differences

Difference Between Apache Pig and Apache Hive

The Apache Pig story begins in the year 2006 when the researcher as Yahoo was struggling with MapReduce Java codes. It was difficult to reuse and maintain code for compilation.  At the same time, they observed that MapReduce users were not comfortable with declarative languages such as SQL. They started to work on new language that was supposed to fit in a sweet spot between the declarative style of SQL, low-level and procedural style of MapReduce. This resulted in the birth of Pig and the first release of Pig came in September 2008 and by end of 2009 about half of the jobs at Yahoo were Pig jobs.

The Apache Hive story begins in the year 2007 when non-Java Programmer have to struggle while using Hadoop MapReduce. IT professional from database background were facing challenges to work on Hadoop Cluster. Initially, researchers, working at Facebook came up with Hive language. This language was very similar to SQL language. So language was called Hive Query Language (HQL) and later it becomes project of open source Apache Community. After becoming project of Apache Community there was a major development in Apache Hive. Facebook was the first company to come up with Apache Hive.

Let me explain about Apache Pig vs Apache Hive in more details.

Introducing Apache Pig vs Apache Hive

Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Apache is open source project of Apache Community. Apache Pig provides a simple language called Pig Latin, for queries and data manipulation.

Pig is being utilized by companies like Yahoo, Google and Microsoft for collecting huge amounts of data sets in the form of click streams, search logs and web crawls.

• Apache Pig provides nested data types like Maps, Tuples, and Bags
• Apache Pig Follows multi-query approach to avoid multiple scans of the datasets.
• Programmers familiar with scripting language prefer Apache Pig
• Pig is easy if you are well aware of SQL
• No need to create schema to work on Apache Pig
• Pig also provides support to major data operations like Ordering, Filters, and Joins
• Apache Pig framework translates Pig Latin into sequences of MapReduce programs

Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Apache Hive is an Apache open-source project built on top of Hadoop for querying, summarizing and analyzing large data sets using a SQL-like interface. Apache hive provides the SQL-like language called HiveQL, which transparently convert queries to MapReduce for execution on large datasets stored in Hadoop Distributed File System (HDFS).

• Apache Hive is a Data warehouse Infrastructure.
• Apache Hive is an ETL tool (Extraction-Transformation-Loading)
• Apache hive is similar to SQL
• Apache Hive enables customized mappers and reducers
• Apache Hive increases the schema design flexibility using data serialization and deserialization
• Apache hive is an analytical tool

Head to Head Comparison Between Apache Pig vs Apache Hive (Infographics):

Key differences between Apache Pig vs Apache Hive:

• Apache Pig is more faster comparing Apache Hive
• Apache Pig and Apache Hive both runs on top of Hadoop MapReduce
• Apache Pig is best for Structured and Semi-structured while Apache Hive is best for structured data
• Apache Pig is a procedural language while Apache Hive is a declarative language
• Apache Pig supports cogroup feature for outer joins while Apache Hive does not support
• Apache Pig does not have a pre-defined database to store table/ schema while Apache Hive has pre-defined tables/schema and stores its information in a database.
• Apache Pig is also suited for complex and nested data structure while Apache Hive is less suited for complex data
• Researchers and programmers use Apache pig while Data Analysts use Apache Hive

When to use Apache Pig:

• When you are a programmer and know scripting language
• When you don’t want to create schema while loading
• ETL requirements
• When you are working on client side of the Hadoop cluster
• When you are working on Avro Hadoop file format

When to use Apache Hive:

• Data warehousing requirements
• Analytical Queries of historical data
• Data Analysis who are familiar with SQL
• While working on structured data
• By Data Analysts
• To visualize and create reports

Apache Pig vs Apache Hive Comparison Table

I am discussing major artifacts and distinguishing between Apache Pig and Apache Hive.

Conclusion -Between Apache Pig vs Apache Hive:

Apache Pig and Apache Hive, both are commonly used on Hadoop cluster. Both Apache Pig and Apache Hive is a powerful tool for data analysis and ETL. Apache Pig and Apache Hive are mostly used in the production environment. A user needs to select a tool based on data types and expected output. Both tools provide a unique way of analyzing Big Data on Hadoop cluster. Based on above discussion user can choose between Apache Pig and Apache Hive for their requirement.

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