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Apache Kylin vs Apache Kudu: What are the differences?
Apache Kylin: 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; Apache Kudu: Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform. A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
Apache Kylin and Apache Kudu can be primarily classified as "Big Data" tools.
Apache Kylin and Apache Kudu are both open source tools. Apache Kylin with 2.3K GitHub stars and 1.02K forks on GitHub appears to be more popular than Apache Kudu with 801 GitHub stars and 268 GitHub forks.
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Learn MorePros of Apache Kudu
Pros of Apache Kylin
Pros of Apache Kudu
- Realtime Analytics10
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
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Cons of Apache Kudu
Cons of Apache Kylin
Cons of Apache Kudu
- Restart time1
Cons of Apache Kylin
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What is Apache Kudu?
A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
What is Apache Kylin?
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.
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What are some alternatives to Apache Kudu and Apache Kylin?
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.
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
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.
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.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.