Apache Kylin vs Pig

Overview

Pig

Stacks57

Followers111

Votes5

GitHub Stars686

Forks447

Apache Kylin

Stacks61

Followers236

Votes24

GitHub Stars3.8K

Forks1.5K

Apache Kylin vs Pig: What are the differences?

Introduction: Apache Kylin and Pig are both powerful tools used in big data processing and analytics. However, they have key differences that make them suitable for different use cases.

Architecture: Apache Kylin is an OLAP (Online Analytical Processing) engine that is specifically designed for querying big data with sub-second latency. It uses a pre-calculation mechanism to build data cubes for fast query performance. On the other hand, Pig is a high-level data flow language that allows users to write complex data transformations for processing large datasets.
Query Language: Apache Kylin uses SQL-like queries for data retrieval and analysis, making it easier for users with SQL knowledge to work with the tool. Pig, on the other hand, uses Pig Latin, a scripting language that is more flexible and expressive for data processing tasks like ETL (Extract, Transform, Load).
Use Case: Apache Kylin is best suited for scenarios where real-time analytics on large datasets is required, such as in business intelligence applications. Pig, on the other hand, is ideal for ad-hoc data processing and ETL tasks where flexibility and customization are key requirements.
Scalability: Apache Kylin is designed to handle large-scale datasets and is optimized for query performance on distributed systems. Pig is also scalable and can process massive amounts of data, but it may require more manual optimization for performance tuning in comparison to Kylin.
Ease of Use: Apache Kylin requires less manual intervention for optimization and tuning due to its pre-calculation mechanisms, making it easier to use for users looking for quick insights from their data. Pig, on the other hand, provides more control and customization options at the cost of higher complexity for users.
Integration with Ecosystem: Apache Kylin seamlessly integrates with tools like Apache Spark, Apache Hive, and Hadoop for data processing and storage, providing a robust ecosystem for big data analytics. Pig also integrates well with these tools, but its focus on data transformation tasks may require additional components for complete data analysis workflows.

In Summary, Apache Kylin is optimized for fast OLAP queries on large datasets with minimal manual tuning, while Pig offers flexibility and control for complex data processing tasks requiring ETL operations and data transformations.

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CLI (Node.js)

Manual

Detailed Comparison

Pig	Apache Kylin
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.	Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.
-	Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Statistics
GitHub Stars 686	GitHub Stars 3.8K
GitHub Forks 447	GitHub Forks 1.5K
Stacks 57	Stacks 61
Followers 111	Followers 236
Votes 5	Votes 24
Pros & Cons
Pros 2 Finer-grained control on parallelization 1 Join optimizations for highly skewed data 1 Open-source 1 Proven at Petabyte scale	Pros 7 Star schema and snowflake schema support 5 Seamless BI integration 4 OLAP on Hadoop 3 Easy install 3 Sub-second latency on extreme large dataset
Integrations
No integrations available	Hadoop Apache Spark Tableau PowerBI Superset

What are some alternatives to Pig, Apache Kylin?

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.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Kylin vs Pig: What are the differences?

Introduction: Apache Kylin and Pig are both powerful tools used in big data processing and analytics. However, they have key differences that make them suitable for different use cases.

Architecture: Apache Kylin is an OLAP (Online Analytical Processing) engine that is specifically designed for querying big data with sub-second latency. It uses a pre-calculation mechanism to build data cubes for fast query performance. On the other hand, Pig is a high-level data flow language that allows users to write complex data transformations for processing large datasets.
Query Language: Apache Kylin uses SQL-like queries for data retrieval and analysis, making it easier for users with SQL knowledge to work with the tool. Pig, on the other hand, uses Pig Latin, a scripting language that is more flexible and expressive for data processing tasks like ETL (Extract, Transform, Load).
Use Case: Apache Kylin is best suited for scenarios where real-time analytics on large datasets is required, such as in business intelligence applications. Pig, on the other hand, is ideal for ad-hoc data processing and ETL tasks where flexibility and customization are key requirements.
Scalability: Apache Kylin is designed to handle large-scale datasets and is optimized for query performance on distributed systems. Pig is also scalable and can process massive amounts of data, but it may require more manual optimization for performance tuning in comparison to Kylin.
Ease of Use: Apache Kylin requires less manual intervention for optimization and tuning due to its pre-calculation mechanisms, making it easier to use for users looking for quick insights from their data. Pig, on the other hand, provides more control and customization options at the cost of higher complexity for users.
Integration with Ecosystem: Apache Kylin seamlessly integrates with tools like Apache Spark, Apache Hive, and Hadoop for data processing and storage, providing a robust ecosystem for big data analytics. Pig also integrates well with these tools, but its focus on data transformation tasks may require additional components for complete data analysis workflows.

Apache Kylin vs Pig

Overview