Pig Vs Hive: Which One is Better?

Have you ever wondered whether Pig or Hive is the better choice for your big data processing needs? While both tools have their strengths, Pig excels in data transformation and scripting tasks, offering flexibility and simplicity. On the other hand, Hive's SQL-like interface makes it a preferred option for users comfortable with SQL queries, especially when dealing with structured data. Let's understand more!

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 much 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 determine which is better.

Table of Content

• Differences Between Pig and Hive
• What is Pig?
• What is Hive?

Differences Between Pig and Hive

Pig	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.

What is Pig?

Pig is a high-level scripting platform designed to process and analyze large datasets on Hadoop clusters, making data tasks more accessible and efficient. It utilizes a language called Pig Latin, which, while sharing some similarities with SQL, is tailored for distributed data processing. Pig Latin scripts are automatically translated into MapReduce jobs, eliminating the need for developers to write low-level Hadoop code.

Apache Pig originated at Yahoo in 2006 and has since become an open-source project under the Apache Software Foundation. Its primary goal is to simplify the development of data processing workflows on Hadoop. Pig Latin's high-level abstractions empower developers to express complex data transformations without delving into the intricacies of MapReduce.

Pig is widely adopted by organizations such as Yahoo, Google, and Microsoft for tasks like collecting and processing data from click streams, web crawls, and search logs. Its versatility is particularly evident in ETL operations on vast datasets, where Pig scripts offer concise and expressive solutions.

Moreover, Pig is extensible, allowing users to write custom User-Defined Functions (UDFs) in languages like Java, Python, and JavaScript, expanding its capabilities. As part of the Hadoop ecosystem, Pig seamlessly integrates with components like HDFS (Hadoop Distributed File System), Hive, and HBase, offering a higher-level data processing abstraction than raw MapReduce.

Advantages of Pig

• Creates a sequence of MapReduce Jobs that run by the Hadoop cluster
• Decrease in deployment time
• Use your 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

Disadvantages of Pig

• 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

What is Hive?

Hive is a powerful data warehousing system within the Hadoop ecosystem that facilitates the querying and analysis of vast datasets stored in HDFS (Hadoop Distributed File System) and compatible storage systems such as Amazon S3. Hive simplifies the process of working with big data by providing a SQL-like query language known as HiveQL, making it accessible to users who are familiar with SQL.

With Hive, users can leverage the full potential of Hadoop for data analysis without the need to write intricate MapReduce code. It offers a wide range of functionalities designed to optimize the performance of SQL-like queries on large datasets. This enables organizations to harness the benefits of distributed computing and parallel processing for efficient data processing.

Hive's ability to seamlessly interact with HDFS and other Hadoop ecosystem components, such as HBase and Spark, makes it a valuable tool for businesses and data professionals. Whether you're an experienced coder or new to data processing, Hive provides a user-friendly interface for unlocking insights from your data, making it a versatile choice for data warehousing and analytics.

Advantages of Hive

• Keeps queries running fast
• Takes very little time to write a 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

Disadvantages of Hive

• 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

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. Depending on your requirements and job role, you can choose any of the two. You can pick the one that defines and creates cross-language services for several languages.