Difference between Pig and Hive

Day in and day out, without even realizing we are creating loads and loads of data via the Social Media platforms like Facebook, LinkedIn and etc. With the advent of such platforms, data is growing exponentially with such huge volumes. To understand the data that gets created on a daily basis, the categorization of this data is very important –

Structured Data: The data that can be stored in a database, with a specific set of columns in an organized manner.

Semi-structured Data: The data that can be partially saved on to a database and the rest of the data has to reside on a different means of storage. Example, XML records that can be partially saved on to a database.

Unstructured Data: Any data that doesn’t fit in the first two categories of data will be qualified to be coined as unstructured data (which is also commonly referred to as “Big Data”)

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Comparison Between Pig and Hive

What is Big Data?

With the necessary details and the introduction provided, we can now safely introduce the framework named Apache Hadoop as the framework that is used for processing the Big Data. It is also a very famous framework that serves the need of storing, analyzing bigger volumes of Big Data. This is the latest buzzword that you would hear that deals with analyzing, processing TB’s (Terabytes), PB’s (Petabytes) or even ZB’s (Zettabytes) of data. The Hadoop ecosystem typically serves the following two purposes:

Storing enormous amounts of data:

This is attained by adding more and more nodes to the Hadoop cluster. Generally, the block size within the Hadoop Distributed File System (HDFS) is either 64MB or 128MB as against the usual of 128KB.

Bringing computation to data:

In a very traditional way, the data is extracted to the clients for the actual computational processes. But the opposite works fine with Apache Hadoop as the data stored is such huge that it is more efficient to the opposite of the traditional way – mainly happens by the MapReduce jobs.

The Hadoop ecosystem contains more than 20 components to it are complete fully functional working and we will look at the only two components that matter for this article – Apache Pig & Apache Hive.

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What is Apache Hadoop Hive?

Hive is very much similar to a SQL Interface in Hadoop and all the DML operations of a traditional RDBMS be compared with the Hive specific functions like - Hive select, where, group by, and order by clauses. Data is stored in the HBase component within the Hadoop ecosystem and is accessible via HiveQL. Hive is a simple solution for those who do not have much introduction to using MapReduce jobs on Hadoop, internally the queries are transformed into MapReduce jobs.

Hive can thus be described as the following:

• Data warehouse infrastructure
• Provider of HiveQL similar to SQL
• Provider of utility tools for data extraction, transformation and loading of data
• Allows embedding customized mappers, reducers
• Provider of powerful statistics functions
• Hive is gaining its popularity as it is supported by Hue

Related Article: Hive Vs Impala

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What is Apache Hadoop Pig?

Pig is an ad-hoc method to create or execute MapReduce jobs on big datasets. Prime motto behind developing Pig was to curtail the time that is required for development via queries. It is a high-level data flow system which helps to render simple language platform that is termed as Pig Latin – helps in manipulating data and queries. The following are the reasons that make Pig much popular amongst the components available in Hadoop ecosystem:

• Follows multi-query approach to avoid multiple scans of the datasets.
• Pig is easy if you are well aware with SQL
• Pig provides nested data types like Maps, Tuples and Bags which are not available for usage in MapReduce
• Pig also provides support to major data operations like Ordering, Filters and Joins
• Performance wise Pig surpasses that of raw MapReduce

Differences between Apache Pig and Apache Hive

There are lots of factors that define these components altogether and hence by its usage, and also by its purpose, there are differences between these two components of the Hadoop ecosystem. Hence let us try to understand the purposes for which these are used and worked upon.

To come to a conclusion after having gone through all the necessary details to understand Big Data, Hadoop ecosystem and the components of choice of this article – Hive and Pig, it is clearly understood that there is no battle between Hive and Pig as such. Each of this provides a unique way of analyzing Big Data with reasonable affection towards Pig from the programmer community and Hive being admired by database developers.

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