Apache Drill vs Pig

Overview

Apache Drill

Stacks74

Followers171

Votes16

Pig

Stacks57

Followers111

Votes5

GitHub Stars686

Forks447

Apache Drill vs Pig: What are the differences?

Apache Drill vs Pig

Apache Drill and Pig are both data processing tools that are widely used in the big data ecosystem. However, there are several key differences between the two.

Query Language: Apache Drill uses SQL-like queries to interact with data sources, making it easier for users familiar with SQL to work with. On the other hand, Pig uses its own scripting language called Pig Latin, which is designed for expressing data transformations.
Data Formats: Apache Drill natively supports a wide range of data formats, including JSON, Parquet, CSV, Avro, and more. It can directly query these formats without any pre-processing. Whereas, Pig requires data to be transformed into its own format called Pig Storage, which can be a time-consuming process.
Data Processing: Apache Drill is designed to work with both structured and semi-structured data, making it suitable for complex data processing tasks. Pig, on the other hand, is primarily focused on structured data processing and lacks advanced features for handling semi-structured or nested data.
Data Source Connectivity: Apache Drill can connect to various data sources, including Hadoop Distributed File System (HDFS), relational databases, NoSQL databases, and more. Pig, on the other hand, primarily operates on data stored in HDFS or HBase and requires data to be loaded into these systems prior to processing.
Performance: Apache Drill is designed for interactive queries and can provide near real-time results on large datasets. It optimizes query execution using distributed processing, vectorized processing, and columnar storage. Pig, on the other hand, is optimized for batch processing and may not provide the same level of performance for interactive queries.
User Community: Apache Drill has a rapidly growing community of users and contributors, with active development and regular updates. Pig, on the other hand, has been around for longer and has a more established user community, but its development and updates have slowed down in recent years.

In Summary, Apache Drill and Pig differ in terms of query language, data formats, data processing capabilities, data source connectivity, performance, and user community.

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CLI (Node.js)

Manual

Detailed Comparison

Apache Drill	Pig
Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.	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.
Low-latency SQL queries;Dynamic queries on self-describing data in files (such as JSON, Parquet, text) and MapR-DB/HBase tables, without requiring metadata definitions in the Hive metastore.;ANSI SQL;Nested data support;Integration with Apache Hive (queries on Hive tables and views, support for all Hive file formats and Hive UDFs);BI/SQL tool integration using standard JDBC/ODBC drivers	-
Statistics
GitHub Stars -	GitHub Stars 686
GitHub Forks -	GitHub Forks 447
Stacks 74	Stacks 57
Followers 171	Followers 111
Votes 16	Votes 5
Pros & Cons
Pros 4 NoSQL and Hadoop 3 Free 3 Lightning speed and simplicity in face of data jungle 2 Well documented for fast install 1 Nested Data support	Pros 2 Finer-grained control on parallelization 1 Proven at Petabyte scale 1 Open-source 1 Join optimizations for highly skewed data

What are some alternatives to Apache Drill, Pig?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Presto

Distributed SQL Query Engine for Big Data

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

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Apache Drill vs Pig: What are the differences?

Apache Drill vs Pig

Apache Drill and Pig are both data processing tools that are widely used in the big data ecosystem. However, there are several key differences between the two.

Query Language: Apache Drill uses SQL-like queries to interact with data sources, making it easier for users familiar with SQL to work with. On the other hand, Pig uses its own scripting language called Pig Latin, which is designed for expressing data transformations.
Data Formats: Apache Drill natively supports a wide range of data formats, including JSON, Parquet, CSV, Avro, and more. It can directly query these formats without any pre-processing. Whereas, Pig requires data to be transformed into its own format called Pig Storage, which can be a time-consuming process.
Data Processing: Apache Drill is designed to work with both structured and semi-structured data, making it suitable for complex data processing tasks. Pig, on the other hand, is primarily focused on structured data processing and lacks advanced features for handling semi-structured or nested data.
Data Source Connectivity: Apache Drill can connect to various data sources, including Hadoop Distributed File System (HDFS), relational databases, NoSQL databases, and more. Pig, on the other hand, primarily operates on data stored in HDFS or HBase and requires data to be loaded into these systems prior to processing.
Performance: Apache Drill is designed for interactive queries and can provide near real-time results on large datasets. It optimizes query execution using distributed processing, vectorized processing, and columnar storage. Pig, on the other hand, is optimized for batch processing and may not provide the same level of performance for interactive queries.
User Community: Apache Drill has a rapidly growing community of users and contributors, with active development and regular updates. Pig, on the other hand, has been around for longer and has a more established user community, but its development and updates have slowed down in recent years.

In Summary, Apache Drill and Pig differ in terms of query language, data formats, data processing capabilities, data source connectivity, performance, and user community.