Table of Contents
When would you use Hadoop PIG?
When to use Pig?
- When you are a programmer and know scripting language very well.
- When you don’t want to create schema for all the data load related work.
- Pig is a complete ETL tool for Big Data.
- Pig runs on the client side of the Hadoop cluster.
- Pig has a procedural data flow language called Pig Latin.
When should you not use a PIG?
Limitations of the Apache Pig are: As the Pig platform is designed for ETL-type use cases, it’s not a better choice for real-time scenarios. Apache Pig is not a good choice for pinpointing a single record in huge data sets. Apache Pig is built on top of MapReduce, which is batch processing oriented.
Where do you use hive?
Hive is an ETL and data warehouse tool on top of Hadoop ecosystem and used for processing structured and semi structured data. Hive is a database present in Hadoop ecosystem performs DDL and DML operations, and it provides flexible query language such as HQL for better querying and processing of data.
What is Hive and Pig?
Pig is a Procedural Data Flow Language. Hive is a Declarative SQLish Language. 4. It was developed by Yahoo. It was developed by Facebook.
What’s the difference between Hive and Pig?
Pig is a Procedural Data Flow Language. Hive is a Declarative SQLish Language.
Where do we use pigs?
Pig is used for the analysis of a large amount of data. It is abstract over MapReduce. Pig is used to perform all kinds of data manipulation operations in Hadoop. It provides the Pig-Latin language to write the code that contains many inbuilt functions like join, filter, etc.
Why do we need Apache Pig?
Why Do We Need Apache Pig? Programmers who are not so good at Java normally used to struggle working with Hadoop, especially while performing any MapReduce tasks. Apache Pig is a boon for all such programmers. Using Pig Latin, programmers can perform MapReduce tasks easily without having to type complex codes in Java.
Why is hive used?
Hive allows users to read, write, and manage petabytes of data using SQL. Hive is built on top of Apache Hadoop, which is an open-source framework used to efficiently store and process large datasets. As a result, Hive is closely integrated with Hadoop, and is designed to work quickly on petabytes of data.
Why hive is used instead of Pig?
Hive Query language (HiveQL) suits the specific demands of analytics meanwhile PIG supports huge data operation. PIG was developed as an abstraction to avoid the complicated syntax of Java programming for MapReduce. On the other hand HIVE, QL is based around SQL, which makes it easier to learn for those who know SQL.
Which one do you use – hive or pig?
Usually, companies select one of the Hive and Pig and hardly any company uses both in a production environment. They decide it depending on the kind of data they have majorly. Mainly if a company has more historical data, they use Hive. Which one do you use in your company and personal work?
What is the difference between Apache Hive and Apache Pig?
In Hive, there is a declarative language called HiveQL which is like SQL. In Pig, there is a procedural language called Pig Latin. b. Mainly Used for Mainly, data analysts use Apache Hive. Mainly, researchers and programmers use Apache Pig. Basically, Hive allows structured data. However, Apache Pig allows both structured and semi-structured data.
What is the difference between hive component and pig component?
Basically, Hive component operates on a server side of the cluster. However, Pig server operates on the client side of the cluster. e. ETL (Extract-Transform-Load)
What is Hive and how does Facebook use it?
Facebook played an active role in the birth of Hive as Facebook uses Hadoop to handle Big Data. Hadoop uses MapReduce to process data. Previously, users needed to write lengthy, complex codes to process and analyze data. Not everyone was well-versed in Java and other complex programming languages.