What is Apache Kylin?
Apache Kylin is an open-source distributed analytics engine designed to provide an SQL interface and multi-dimensional analysis (OLAP) on Hadoop, supporting extremely large datasets. It is vastly used by data professionals to improve the speed of big data analytics.
History
Initially developed by eBay, Apache Kylin graduated from the Apache Software Foundation in 2015. Since then, it has grown in popularity and has been adopted by many notable companies for its useful features in data analytics and business intelligence.
Functionality and Features
Apache Kylin's significant features include:
- Interactive SQL capabilities over big dataIntegration with BI tools via standard interfaces
- Scalable and high-speed processing of massive amounts of data
- Real-time streaming data storage and processing
Architecture
Apache Kylin's architecture is built on a combination of technologies including Hadoop, HBase, and Hive. It uses these technologies to organize, store, and query massive datasets, providing businesses with insights and answers in real time.
Benefits and Use Cases
Apache Kylin is advantageous for businesses that need to analyze large volumes of data quickly and efficiently. It serves a variety of data-driven industries, including e-commerce, finance, and healthcare. Use cases mostly revolve around interactive analysis, reporting, and data discovery.
Challenges and Limitations
Despite its capabilities, Apache Kylin has certain limitations. It is primarily suited for batch processing, making it less suitable for real-time data analytics. Furthermore, its reliance on other technologies such as Hadoop and HBase may lead to complexity and limit scalability.
Integration with Data Lakehouse
In a data lakehouse setting, Apache Kylin can serve as an effective analysis engine, working seamlessly with the unified data platform. Through its OLAP capabilities, it can facilitate fast and efficient querying over the vast volumes of data in the lakehouse.
Security Aspects
Apache Kylin includes robust security measures, integrating with Apache Ranger for access control and Apache Sentry for authorization management.
Performance
Apache Kylin's performance is one of its standout features. By precomputing results of massive datasets and storing them in HBase, it speeds up the analytics process significantly.
Frequently Asked Questions (FAQs)
What is Apache Kylin? Apache Kylin is an open-source distributed analytics engine that provides SQL interface and multi-dimensional analysis on Hadoop for large datasets.
Who uses Apache Kylin? Data professionals and businesses that require analysis of large data volumes across various industries use Apache Kylin.
What are the key features of Apache Kylin? Key features include interactive SQL capabilities over big data, integration with BI tools, scalable and high-speed processing, and real-time streaming data storage and processing.
Does Apache Kylin have any limitations? Apache Kylin is primarily suited for batch processing, making it less suitable for real-time data analytics. Its dependence on Hadoop and HBase can add complexity and limit scalability.
How does Apache Kylin integrate with a data lakehouse? In a data lakehouse environment, Apache Kylin can operate as an effective analysis engine, working seamlessly with the unified data platform.
Glossary
Apache Hadoop: An open-source software framework used for distributed storage and processing of big data.
Apache HBase: An open-source, non-relational, distributed database modeled after Google's Bigtable and is used for storing sparse data.
OLAP: Online Analytical Processing, a category of software tools that allows users to analyze data from multiple database dimensions.
Data Lakehouse: A relatively new approach to manage data, providing the benefits of both data lakes and data warehouses, ideally suited for machine learning, BI reporting, and real-time analytics.
Apache Ranger and Sentry: Tools for managing authorization and security in Hadoop.
