Pig and Hive are the two main components of the Hadoop ecosystem. They came into presence out of the only need for enterprises to interact with great amount of data without fretting about writing complex MapReduce code. Both have a similar objective – ease the complexity of writing complex MapReduce programs. But, when to use pig and hive is the question most of the people have. Let’s discuss the difference between Pig and Hive.
Let’s Jump in:
Let’s dig deep into both to understand the similarities and difference between Pig and Hive:
A high level platform for creating codes that run on Hadoop, Pig makes it easier to analyze, process and clean big data without writing vanilla MapReduce jobs in Hadoop. It was developed in 2006 at Yahoo and enables developers to follow multiple query approach. Pig is utilized by organizations like Yahoo, Google and Microsoft for collecting data sets in the form of click steams, web crawls and search logs. It is easy to learn for all those who are familiar with SQL.
- Creates sequence of MapReduce Jobs that run by Hadoop cluster
- Decrease in deployment time
- Use own language called pig Latin
- Perfect for programmers and software developers
- Easy to write and read
- Provides data operations like ordering, filters and joins
- The error that pig produces are not helpful
- Not mature
- Data schema is not enforced explicitly but implicitly
- Commands are not executed until you dump in an intermediate result
- No ide for Vim rendering more functionality than syntax completion to write the pig scripts
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It is a Hadoop ecosystem which renders you the ability to analyze data by writing SQL like queries. Hive has various functionalities to help you make SQL queries run faster. Supporting analysis of large datasets stored in Hadoop’s HDFS and compatible systems like Amazon S3 file system, Hive is also known as HiveQL, Hive Query Language. If you are not proficient in coding, choose Hive because you don’t have to write complex codes of MapReduce.
- Keeps queries running fast
- Takes very less time to write Hive query in comparison to MapReduce code
- HiveQL is a declarative language like SQL
- Provides the structure on an array of data formats
- Multiple users can query the data with the help of HiveQL
- Very easy to write query including joins in Hive
- Simple to learn and use
- Useful when the data is structured
- You can do any analytical operation using MR programming
- Debugging code is very difficult
- You can’t do complicated operations
When it comes to decision, Hive has more features than Pig. It is an amazing tool for analytical querying of historical data. So, pick the one that defines and creates cross-language services for several languages.