Systems | Development | Analytics | API | Testing

See Cloudera Shared Data Experience (SDX) in action. You will see how they use a single pane of glass to highlight everything that happens to a piece of data across its complete lifecycle, from when it’s first ingested all the way to when it’s used to train a machine learning model.

In this video, we'll walk through an example on how you can use Cloudera Data Engineering to pull in multiple datasets from a Hive data warehouse and go through the process of enriching the data through the use of Apache Spark. We'll then run this Spark job from within Cloudera Data Engineering so that we can follow the progress and see details about the job's execution.

Cloudera DataFlow for Data Hub makes hybrid use cases possible by extending on-premises flow management, streams messaging, and stream processing and analytics capabilities to the public cloud. Watch an integrated demo of Cloudera DataFlow on Data Hub to understand how easy it is to ingest, process, and analyze your streaming data across multiple public cloud clusters.

Spark has become the de-facto processing framework for ETL and ELT workflows for good reason, but for many enterprises working with Spark has been challenging and resource-intensive. Leveraging Kubernetes to fully containerize workloads, DE provides a built-in administration layer that enables one-click provisioning of autoscaling resources with guardrails, as well as a comprehensive job management interface for streamlining pipeline delivery. DE enables a single pane of glass for managing all aspects of your data pipelines.