One of the most fundamental aspects a data model can convey is how something changes over time. This makes sense when considering that we build data models to capture what is happening in the real world, and the real world is constantly changing. The challenge is that it’s not just that new things are occurring, it’s that existing things are changing too, and if in our data models we overwrite the old state of an entity with the new state then we have lost information about the change.
Analytical and operational access patterns are very different and until now the Hadoop ecosystem has not had a single storage engine that could support both. As a result, engineers have been forced to implement complex architectures that stitch multiple systems together in order to provide these capabilities. On one hand immutable data on HDFS offers superior analytic performance, while mutable data in Apache HBase is best for operational workloads. Apache Kudu bridges this gap.
Kudu’s architecture is shaped towards the ability to provide very good analytical performance,
Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.
This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,
After the GA of Apache Kudu in Cloudera CDH 5.10, we take a look at the Apache Spark on Kudu integration, share code snippets, and explain how to get up and running quickly, as Kudu is already a first-class citizen in Spark’s ecosystem.
As the Apache Kudu development team celebrates the initial 1.0 release launched on September 19, and the most recent 1.2.0 version now GA as part of Cloudera’s CDH 5.10 release,
Cloudera is proud to announce that Cloudera Enterprise 5.10 is now generally available (GA). The highlights of this release include the GA of the new columnar storage engine Apache Kudu, improved cloud performance and cost-optimizations, and cloud-native data governance for Amazon S3.
As usual, there are also a number of quality enhancements and bug fixes (learn more about our multi-dimensional hardening/QA process) and other improvements across the stack. Here is a partial list of what’s included (see the Release Notes for a full list):
- GA of Apache Kudu –