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.
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.
SMM 1.2 Released with Powerful New Alerting and Topic Lifecycle Management Features with Schema Registry Integration
[Editor’s note: Now that the recent merger is complete, the Cloudera Engineering blog will expand to cover products, such as this, originally developed for the Hortonworks platform. Please stay tuned for future product announcements regarding availability of these products on the Cloudera platform.]
Since the release of Streams Messaging Manager (SMM) at the end of last summer, our customers have started to cure the Kafka Blindness within their organizations by using SMM to monitor their Kafka clusters and streaming microservices applications.
Using Native Math Libraries to Accelerate Spark Machine Learning Applications
[Editor’s note: The original version of this article was published as part of our Guru How-To series for Data Science. Be sure to also check out the series for Cloudera Data Warehouse.]
Spark ML is one of the dominant frameworks for many major machine learning algorithms, such as the Alternating Least Squares (ALS) algorithm for recommendation systems, the Principal Component Analysis algorithm, and the Random Forest algorithm.
Integrating Machine Learning Models into Your Big Data Pipelines in Real-Time With No Coding
[Editor’s note: This article was originally published on the Hortonworks Community Connection, but reproduced here because CDSW is now available on both Cloudera and Hortonworks platforms.]
Using Deployed Models as a Function as a Service
Using Cloudera Data Science Workbench with Apache NiFi, we can easily call functions within our deployed models from Apache NiFi as part of flows. I am working against CDSW on HDP (https://www.cloudera.com/documentation/data-science-workbench/latest/topics/cdsw_hdp.html),