Pig vs Hive: Main differences between Apache Pig and Hive

Delving into the big data and extracting insights from it requires robust tools that allow flexibility in data management and querying – filtering, aggregating, and analyses. Typically, MapReduce code is leveraged to do this but the complexity involved in writing intricate Java code to prepare MapReduce scripts led to new languages being created that allowed users to access datasets with more ease.

Pig was created by researchers at Yahoo, and has the flexibility of multiple query approach. Although somewhat similar to SQL the traditional language for data analysis in some ways, doesn’t have its declarative nature and has limitations like- being dependent on relational database schemas. Pig is more of a programming language, and is often referred to as an abstraction of the complicated syntax of Java programming required for MapReduce. Pig has has different semantics than Hive and Sql.

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Hive (invented at Facebook) on the other hand is highly similar to SQL, as it uses almost the same commands for data manipulation, making particularly suitable for those experienced in use of SQL.

These two components of the Hadoop ecosystem work atop Hadoop. The goal of both these tools is to make it easier to interact with massive datasets within Hadoop without having to write out complex MapReduce code.

Understanding the differences between Pig and Hive  

There are several differentiating elements between the two languages, and big data users need to appreciate these differences to make use of the right tool:

•  As Hive adopts SQL-based declarative approach it is often preferred for structured data especially historical data. It is therefore often referred to as a data warehouse platform.

Pig on the other hand uses Procedural Data Flow Language and is preferred for semi- structured, unstructured or decentralized data. The flexibility of Pig allows better construction of data flows and its feature of self-optimization results in lesser number of data scans.

• Hive use distinct query language called HQL whereas Pig use their own language called piglatin (procedural language).
• Partitioning can be done using HIVE whereas it’s not possible in in PIG
• In terms of practical usage, Hive is preferred for reporting and operates on the server side of a cluster while Pig is great for writing programs and operates on the client side.
• Given its characteristics, Pig is typically used by researchers and programmers but Hive is preferred by data scientists who work on large quantitative datasets.
• Hive usually executes quickly but loads slowly whereas Pig loads faster and more effectively.

Adopting a standard approach to big data analytics would hamper benefits from it.  Both Pig and Hive have their own advantages that make them apt for some situations but not in others. Analysts must carefully examine the insight requirements before deciding on the tool to use. 

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