What are the advantages of Apache Pig over SQL and Hive?

What is an advantage of Pig over SQL?

Apache Pig is a procedural language, not declarative, unlike SQL. Hence, we can easily follow the commands. Also, offers better expressiveness in the transformation of data in every step. Moreover, while we compare it to vanilla MapReduce, it is much more like the English language.

What are the benefits of Apache Hive Pig over MapReduce?

Advantages of Hive

  • 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.

Why Pig is faster than Hive?

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. AVRO is supported by PIG making serialization faster.

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What is Apache Pig good for?

Pig is a high-level platform or tool which is used to process the large datasets. It provides a high-level of abstraction for processing over the MapReduce. It provides a high-level scripting language, known as Pig Latin which is used to develop the data analysis codes.

What are the advantages and disadvantages of Hive?

Two, the advantages and disadvantages of Hive

(1) The operation interface adopts SQL-like syntax to provide rapid development capabilities (simple and easy to use). (2) Avoid writing MapReduce and reduce the learning cost of developers.

What is Hive and its uses?

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.

What is the difference in pig and SQL?

Apache Pig Vs SQL

Pig Latin is a procedural language. SQL is a declarative language. In Apache Pig, schema is optional. We can store data without designing a schema (values are stored as $01, $02 etc.)

What is Hive compare between Hive SQL and MapReduce?

Provide SQL type language which is called HQL. Helps in querying large data sets stored in HDFS(Hadoop Distributed File System). It is an open-source tool.

MapReduce vs Hive.

S.No MapReduce Hive
6. It has several jobs therefore execution time is more. The code execution time is more but development effort is less.
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What is Apache Pig architecture?

Apache Pig architecture consists of a Pig Latin interpreter that uses Pig Latin scripts to process and analyze massive datasets. Programmers use Pig Latin language to analyze large datasets in the Hadoop environment.

What is Apache Pig vs Hive?

Hive is built on the top of Hadoop and is used to process structured data in Hadoop. Hive was developed by Facebook.

Difference between Pig and Hive :

S.No. Pig Hive
2. Pig uses pig-latin language. Hive uses HiveQL language.
3. Pig is a Procedural Data Flow Language. Hive is a Declarative SQLish Language.

Is Apache Pig still used?

Yes, it is used by our data science and data engineering orgs. It is being used to build big data workflows (pipelines) for ETL and analytics. It provides easy and better alternatives to writing Java map-reduce code.

What is the difference between Hive and SQL?

Hive gives an interface like SQL to query data stored in various databases and file systems that integrate with Hadoop.

Difference between RDBMS and Hive:

It uses SQL (Structured Query Language). It uses HQL (Hive Query Language).
Schema is fixed in RDBMS. Schema varies in it.

What are the features of Pig?

Top 12 Hadoop Pig Features

  • i. Rich set of operators. …
  • ii. Ease of programming. …
  • iii. Optimization opportunities. …
  • iv. Extensibility. …
  • v. UDF’s. …
  • vi. Handles all kinds of data. …
  • vii. Join operation. …
  • viii. Multi-query approach.

What are true about Pig?

Pig is a tool/platform which is used to analyze larger sets of data representing them as data flows. Explanation: Pig is generally used with Hadoop; we can perform all the data manipulation operations in Hadoop using Apache Pig.

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Why Pig is data flow language?

Pig–Pig is a data-flow language for expressing Map/Reduce programs for analyzing large HDFS distributed datasets. Pig provides relational (SQL) operators such as JOIN, Group By, etc. Pig is also having easy to plug in Java functions. Cascading pipe and filter processing model.