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Streamline and operationalize data pipelines securely at any scale.

CDP Data Engineering is the only cloud-native service purpose-built for enterprise data engineering teams. Building on Apache Spark, Data Engineering is an all-inclusive data engineering toolset that enables orchestration automation with Apache Airflow, advanced pipeline monitoring, visual troubleshooting, and comprehensive management tools to streamline ETL processes across enterprise analytics teams.

Data Engineering is fully integrated with Cloudera Data Platform, enabling end-to-end visibility and security with SDX as well as seamless integrations with CDP services such as CDP Data Warehouse and CDP Machine Learning. Data Engineering on CDP powers consistent, repeatable, and automated data engineering workflows on a hybrid cloud platform anywhere.

CDP Data Engineering use cases

  • Automate data pipelines everywhere
  • Gain ETL visibility and control
  • Maintain data integrity throughout

Automate data pipelines everywhere

Securely deliver quality datasets to CDP Data Warehouse, CDP Machine Learning, or any other analytic tool.

Data Engineering streamlines data pipelines to analytic teams from machine learning to data warehousing and beyond. Speed time to value by orchestrating and automating pipelines to deliver curated, quality datasets anywhere securely and transparently.

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Gain ETL visibility and control

Holistically manage your data lifecycle transparently.

Managing the data lifecycle and controlling costs becomes increasingly complex when attempting to operationalize data pipelines across the enterprise at scale.

Data Engineering offers a suite of operational control and visibility features for capacity planning, pipeline automation, automatic lineage capture, and troubleshooting across business use cases.

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Screenshot of the CDP Data Engineering tool | Cloudera

Maintain data integrity throughout

Full data pipeline visibility to protect your business.

As data quantity and complexity grows, ensuring ongoing accuracy and fidelity for scaling analytical workloads across the business can be difficult.

Data Engineering offers native data pipeline monitoring and alerting to catch issues early, and visual troubleshooting to quickly resolve problems before they impact your business.


Screenshot of CDP Data Engineering - Data pipeline troubleshooting  | Cloudera

CDP Data Engineering key features

Orchestrate complex data transformation workflows backed by Apache Airflow with hundreds of operators to meet mission critical analytic requirements.

Data Engineering is containerized, scalable, and portable, with isolated workload environments and guardrails—enabling secure pipeline management with on-demand elastic compute to meet business SLAs cost-effectively.

Visualize performance metrics including CPU, memory, and I/O across all the stages of your Spark jobs to pinpoint performance bottlenecks and identify the needle in the haystack while troubleshooting.

Leverage a rich job management interface through a CLI and Rest APIs to automate and integrate with existing workflows like CI/CD pipelines and third-party tools with ease.

Data Engineering offers a fully integrated Spark on Kubernetes service that automates and streamlines artifact management, security, and resource scheduling—leveraging Apache Yunikorn to provide FIFO and GANG scheduling.

From a centralized interface, platform administrators can manage access and security, then quickly provision new workloads while easily monitoring capacity and visualizing resource usage over time. SDX also enables full lifecycle lineage tracking to know where data came from and where it’s going.

Ready to take a deeper look?

Experience Data Engineering on Cloudera Data Platform for yourself


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