Overview
Streamline and operationalize data pipelines securely at any scale.
Cloudera 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, enabling end-to-end visibility and security with SDX as well as seamless integrations with data services such as Cloudera Data Warehouse and Cloudera Machine Learning. Data Engineering powers consistent, repeatable, and automated data engineering workflows on a hybrid cloud platform anywhere.
Cloudera 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 Cloudera Data Warehouse, 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.
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.
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.
Cloudera 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 Cloudera Data Engineering for yourself
