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Apache Kylin vs Druid: What are the differences?
Developers describe Apache Kylin as "OLAP Engine for Big Data". 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. On the other hand, Druid is detailed as "Fast column-oriented distributed data store". 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.
Apache Kylin and Druid can be categorized as "Big Data" tools.
Apache Kylin and Druid are both open source tools. It seems that Druid with 8.22K GitHub stars and 2.05K forks on GitHub has more adoption than Apache Kylin with 2.21K GitHub stars and 983 GitHub forks.
Pros of Apache Kylin
- Star schema and snowflake schema support7
- Seamless BI integration5
- OLAP on Hadoop4
- Easy install3
- Sub-second latency on extreme large dataset3
- ANSI-SQL2
Pros of Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP4
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
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Cons of Apache Kylin
Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
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What is Apache Kylin?
What is Druid?
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