{"id":9829,"date":"2018-03-03T09:02:21","date_gmt":"2018-03-03T09:02:21","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=9829"},"modified":"2018-03-03T09:02:21","modified_gmt":"2018-03-03T09:02:21","slug":"pig-vs-hive","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pig-vs-hive\/","title":{"rendered":"Pig vs Hive | Difference between Pig and Hive"},"content":{"rendered":"

As we know both Hive <\/strong>and Pig<\/strong> are the major components of Hadoop <\/strong>ecosystem. However, every time a question occurs about the difference between Pig and Hive. Also, there’s a question that when to use hive and when Pig in the daily work? <\/span><\/p>\n

So, in this pig vs hive tutorial, we will learn the usage of Apache Hive as well as Apache Pig. Moreover, we will discuss the pig vs hive performance on the basis of several features. But before all comparison between Pig vs Hive, we will also learn brief introduction of both Hive and Pig.<\/span><\/p>\n

Introduction to Apache Pig and Hive?<\/span><\/h2>\n

Before we discuss pig vs hive, let\u2019s discuss what is Apache Pig and Hive in detail:<\/span><\/p>\n

a. What is Apache Hive?<\/strong>
\nBasically, for data analysis, Hive is an integral part of\u00a0<\/span>Hadoop Ecosystem<\/strong>. We use it only when we have structured data. However, first of all, we need to make the data structured then only we can inject in the Hive tables.<\/span><\/p>\n

However, Hive can be easy for all those who are much familiar with SQL. Also, we can optimize Hive queries as similar to SQL query optimization. Moreover, in Hive, there are many other features. Such as Partition and bucketing<\/strong>. Especially, that makes your data analysis easy and quick.<\/p>\n

It becomes one of the top Apache projects later but at first, it was developed at Facebook. Also, it gives the user flexibility by writing less code and do more with it. Moreover, it converts the queries into MapReduce<\/strong>\u00a0execution.<\/p>\n

However, we don\u2019t have to worry about the backend processes much. Also, Hive uses a query language pretty much similar to SQL known as HQL (Hive query language).<\/p>\n

In addition, to processing data stored in a distributed manner, unlike SQL which requires strict adherence to schemas while storing data, Apache Hive works well. Though, Hive has lots of functions which we can directly use, that makes our work easy.<\/p>\n

Moreover, in Hive, we always have the option to create UDFs (user-defined function) if something is not available. That will definitely do your work. Mostly, business analysts, analysts prefer Hive.
\nIn short, we can summarize Apache Hive as follows-<\/p>\n