Data Engineer

The modern day data explosion has to be met with new solutions and Cloudera is excited to introduce the next generation table format for large scale analytic datasets within Cloudera. 

This 2−day instructor-led course provides a comprehensive, hands-on introduction to Apache Ozone, the next-generation hybrid storage service offering versatility and out-of-the-box compatibility. Participants will learn the architecture and internal operations of Ozone, gaining the skills to manage files and objects, control replication, handle failover and recovery, using a service overcoming the limitations of traditional HDFS.

This four-day hands-on training course delivers the key concepts and knowledge developers need to use Apache Spark to develop high- performance, parallel applications on the Cloudera platform.

The Open Data Lakehouse is a modern data architecture that enables versatile analytics on streaming and stored data within cloud-native object stores. This architecture can span hybrid and multi-cloud environments.

This three-day hands-on course teaches developers how to optimize Apache Spark application performance. Participants learn to identify and resolve common performance issues, apply Spark tuning best practices, and monitor applications through instructor-led demonstrations and interactive notebook exercises.

This two-day instructor-led course teaches participants how to use Trino for real-time data federation across distributed data sources. Learn to build enterprise-grade federated analytics architectures that reduce data movement, simplify data engineering, and accelerate insights.

This three-day hands-on course provides an overview of the Cloudera platform and its key services, including Data Warehouse, Data Engineering, Data Flow, and Cloudera AI. Participants learn to build, optimize, and manage scalable, secure data and AI solutions through practical exercises.

The exam tests the skills and knowledge required by data engineers to use the Cloudera Data Platform.

The exam tests the skills and knowledge required by data lakehouse engineers to design, build, and optimize open storage architectures on the Cloudera platform. Candidates demonstrate proficiency in implementing multi-engine transactional tables using Apache Iceberg, managing schema and partition evolution, performing point-in-time table restoration via snapshot rollbacks, and enforcing ACID compliance across streaming and batch pipelines. The exam also covers performance tuning strategies such as data compaction, hidden partitioning, and cross-engine query optimization utilizing Apache Spark, Trino, and Hive.