Heroku is a powerful platform for application development. Users can build and deploy on the cloud, and you can effortlessly scale up once your app takes off. And behind every app, you'll find an equally powerful database: Heroku Postgres. If you're building Heroku apps, you'll find them to be a rich source of operational and customer data. Add in the right Business Intelligence (BI) tools, and you'll be able to derive insights about the inner workings of your organization.

Cloudera Data Engineering is a serverless service for Cloudera Data Platform (CDP) that allows you to submit jobs to auto-scaling virtual clusters. CDE enables you to spend more time on your applications, and less time on infrastructure. CDE allows you to create, manage, and schedule Apache Spark jobs without the overhead of creating and maintaining Spark clusters.

The blog “Migrating Apache NiFi Flows from HDF to CFM with Zero Downtime” detailed how many common NiFi dataflows can be easily migrated when the Hortonworks DataFlow and Cloudera Flow Management clusters are running side-by-side. But what if you lack the resources to run multiple NiFi clusters concurrently? Not a problem.

In this blog post, I’ll set up and run a couple of experiments to demonstrate the effects of different kinds of partition pruning in Spark.

Over the past several years, there has been an explosion of different terms related to the world of IT operations. Not long ago, it was standard practice to separate business functions from IT operations. But those days are a distant memory now, and for good reason.