Kudu vs Apache Spark: What are the differences?
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 Spark: Fast and general engine for large-scale data processing. 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.
Kudu and Apache Spark can be primarily classified as "Big Data" tools.
"Realtime Analytics" is the top reason why over 2 developers like Kudu, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.
Kudu and Apache Spark are both open source tools. It seems that Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub has more adoption than Kudu with 788 GitHub stars and 263 GitHub forks.
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Apache Kudu?
What is Apache Spark?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions