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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Parquet vs Pig

Apache Parquet vs Pig

OverviewComparisonAlternatives

Overview

Pig
Pig
Stacks57
Followers111
Votes5
GitHub Stars686
Forks447
Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0

Pig vs Apache Parquet: What are the differences?

What is Pig? Platform for analyzing large data sets. Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce. .

What is Apache Parquet? *A free and open-source column-oriented data storage format *. It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.

Pig and Apache Parquet belong to "Big Data Tools" category of the tech stack.

Pig and Apache Parquet are both open source tools. Apache Parquet with 918 GitHub stars and 805 forks on GitHub appears to be more popular than Pig with 580 GitHub stars and 447 GitHub forks.

Netflix, Outbrain, and Cobrain are some of the popular companies that use Pig, whereas Apache Parquet is used by Grandata, Yotpo, and Bigabid. Pig has a broader approval, being mentioned in 12 company stacks & 25 developers stacks; compared to Apache Parquet, which is listed in 6 company stacks and 7 developer stacks.

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Detailed Comparison

Pig
Pig
Apache Parquet
Apache Parquet

Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.

It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.

-
Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
Statistics
GitHub Stars
686
GitHub Stars
-
GitHub Forks
447
GitHub Forks
-
Stacks
57
Stacks
97
Followers
111
Followers
190
Votes
5
Votes
0
Pros & Cons
Pros
  • 2
    Finer-grained control on parallelization
  • 1
    Open-source
  • 1
    Join optimizations for highly skewed data
  • 1
    Proven at Petabyte scale
No community feedback yet
Integrations
No integrations available
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive

What are some alternatives to Pig, Apache Parquet?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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