Pig Vs Hive: Which One is Better?
Pig and Hive are the two main components of the Hadoop ecosystem. Both have a similar objective – ease the complexity of writing complex MapReduce programs. They enable enterprises to process and analyze a great amount of data without writing complex MapReduce code. But, when to use Pig and Hive is the question most people have. Let’s discuss the advantages and disadvantages of Pig vs Hive and find out which one is better to use.
Let’s Jump in:
Let’s dig deep into both to understand the similarities and differences between Pig and Hive:
Pig is a scripting platform that runs on Hadoop clusters, designed to process and analyze large datasets. Pig uses a language called Pig Latin, which is similar to SQL. This language does not require as much code in order to analyze data.
Pig is a high-level scripting 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. Pig uses a language called Pig Latin and enables developers to follow multiple query approaches. It is easy to learn for all those who are familiar with SQL.
Pig is utilized by organizations such as Yahoo, Google, and Microsoft for collecting data sets in the form of click steams, web crawls, and search logs.
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- Creates a 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 such as ordering, filters, and joins
- The errors that Pig produces are not helpful
- Not mature
- The 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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Hive is a data warehouse system. It enables you to query and analyze large datasets stored in HDFS. Hive uses a query language called HiveQL, which is similar to SQL.
It is a Hadoop ecosystem that renders you the ability to analyze data by writing SQL-like queries. Hive has various functionalities to help you make SQL queries run faster. It supports the analysis of large datasets stored in Hadoop’s HDFS and compatible systems like the Amazon S3 file system. It uses a query language called 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 little 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
Differences Between Pig and Hive
|Operates on the client-side of a cluster.||Operates on the server-side of a cluster.|
|Procedural Data Flow Language.||Declarative SQLish Language.|
|Pig is used for programming.||Hive is used for creating reports.|
|Majorly used by Researchers and Programmers.||Used by Data Analysts.|
|Used for handling structured and semi-structured data.||It is used in handling structured data.|
|Scripts end with .pig extension.||Hive supports all extensions.|
|Supports Avro file format.||Does not support Avro file format.|
|Does not have a dedicated metadata database.||Uses an exact variation of dedicated SQL-DDL language by defining tables beforehand.|
Conclusion – Pig Vs Hive: Which One to Choose?
When it comes to decisions, Hive has more features than Pig. It is an excellent tool for the analytical querying of historical data. Pig also has some different excellent capabilities and features.
Both Pig and Hive are great data analysis tools. You can choose any of the two depending on your requirements and job role. You can pick the one that defines and creates cross-language services for several languages.
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