Before CDH 5.10, every CDH cluster had to have its own Apache Hive Metastore (HMS) backend database. This model is ideal for clusters where each cluster contains the data locally along with the metadata. In the cloud, however, many CDH clusters run directly on a shared object store (like Amazon S3), making it possible for the data to live across multiple clusters and beyond any cluster’s lifespan. In this scenario clusters need to regenerate and coordinate metadata for the underlying shared data individually.
We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. sparklyr, developed by RStudio, is an R interface to Spark that allows users to use Spark as the backend for dplyr, which is the popular data manipulation package for R.
If you are interested in sparklyr, you can learn how to use it with the official document,
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,
Apache Impala (incubating) includes several features that allow you to restrict or allocate resources so as to maximize stability and performance for your Impala workloads. You can limit both CPU and memory resources used by Impala to manage and prioritize jobs on CDH clusters. This blog post describes the techniques a typical Impala deployment can use to manage its resources.
Static Service Pools
Static service pools isolate services from one another, so that a high load on one service has limited impact on other services.
Last week, the open source Open Network Insights (ONI) project, now called Spot, was accepted into the ASF Incubator. Here are the highlights about its open data model approach and initial use cases.
One of the biggest challenges organizations face today in combating cyber threats is collecting and normalizing data from numerous security event data sources (often up to thousands of them) to build the required analytics.