The scientific method is a proven route to successful, tested and verified improvement. Here’s how to combine it with BI.

There are two big gaps in the Apache Kafka project when we think of operating a cluster. The first is monitoring the cluster efficiently and the second is managing failures and changes in the cluster. There are no solutions for these inside the Kafka project but there are many good 3rd party tools for both problems. Cruise Control is one of the earliest open source tools to provide a solution for the failure management problem but lately for the monitoring problem as well.

Shared Data Experience (SDX) on Cloudera Data Platform (CDP) enables centralized data access control and audit for workloads in the Enterprise Data Cloud. The public cloud (CDP-PC) editions default to using cloud storage (S3 for AWS, ADLS-gen2 for Azure). This introduces new challenges around managing data access across teams and individual users. To solve these challenges for S3 and ADLS-gen2, Cloudera has introduced a new service — the Ranger Authorization Service (RAZ).

Across the federal government, agencies are struggling to identify, organize, analyze, and act on troves of data. It’s a problem that leaders are working actively to tackle, but they’re in a race against immeasurable volumes of data that is continuously being generated in perpetuity in stores known and unknown. At the Internal Revenue Service, decades’ worth of data exceeds even the most cutting-edge processing capabilities.

Advertising agencies are faced with the challenge of providing the precision data that marketers require to make better decisions at a time when customers’ digital footprints are rapidly changing. They need to transform customer information and real-time data into actionable insights to inform clients what to execute to ensure the highest campaign performance.

The CDP Operational Database (COD) builds on the foundation of existing operational database capabilities that were available with Apache HBase and/or Apache Phoenix in legacy CDH and HDP deployments.

Spark is known for being extremely difficult to debug. But this is not all Spark’s fault. Problems in running a Spark job can be the result of problems with the infrastructure Spark is running on, inappropriate configuration of Spark, Spark issues, the currently running Spark job, other Spark jobs running at the same time – or interactions among these layers.