Note: This is a cross-post from the Cloudera Engineering Blog Fine-Grained Authorization with Apache Kudu and Impala

Apache Impala supports fine-grained authorization via Apache Sentry on all of the tables it manages including Apache Kudu tables. Given Impala is a very common way to access the data stored in Kudu, this capability allows users deploying Impala and Kudu to fully secure the Kudu data in multi-tenant clusters even though Kudu does not yet have native fine-grained authorization of its own. This solution works because Kudu natively supports coarse-grained (all or nothing) authorization which enables blocking all access to Kudu directly except for the impala user and an optional whitelist of other trusted users. This post will describe how to use Apache Impala’s fine-grained authorization support along with Apache Kudu’s coarse-grained authorization to achieve a secure multi-tenant deployment.

Note: This is a cross-post from the Cloudera Engineering Blog Testing Apache Kudu Applications on the JVM

Although the Kudu server is written in C++ for performance and efficiency, developers can write client applications in C++, Java, or Python. To make it easier for Java developers to create reliable client applications, we’ve added new utilities in Kudu 1.9.0 that allow you to write tests using a Kudu cluster without needing to build Kudu yourself, without any knowledge of C++, and without any complicated coordination around starting and stopping Kudu clusters for each test. This post describes how the new testing utilities work and how you can use them in your application tests.

The Apache Kudu team is happy to announce the release of Kudu 1.9.0!

The new release adds several new features and improvements, including the following:

Note: This is a cross-post from the Cloudera Engineering Blog Transparent Hierarchical Storage Management with Apache Kudu and Impala

When picking a storage option for an application it is common to pick a single storage option which has the most applicable features to your use case. For mutability and real-time analytics workloads you may want to use Apache Kudu, but for massive scalability at a low cost you may want to use HDFS. For that reason, there is a need for a solution that allows you to leverage the best features of multiple storage options. This post describes the sliding window pattern using Apache Impala with data stored in Apache Kudu and Apache HDFS. With this pattern you get all of the benefits of multiple storage layers in a way that is transparent to users.

Most of the posts in the Kudu blog have been written by the project’s committers and are either technical or news-like in nature. We’d like to hear how you’re using Kudu in production, in testing, or in your hobby project and we’d like to share it with the world!