Cloudera Educational Services

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 Data Platform (CDP).  Hands-on exercises allow students to practice writing Spark applications that integrate with CDP core components. Participants will learn how to use Spark SQL to query structured data, how to use Hive features to ingest and denormalize data, and how to work with “big data” stored in a distributed file system. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries. Download full course description  What you'll learn During this course, you will learn how to: Distribute, store, and process data in a CDP cluster Write, configure, and deploy Apache Spark applications Use the Spark interpreters and Spark applications to explore, process, and analyze distributed data Query data using Spark SQL, DataFrames, and Hive tables Deploy a Spark application on the Data Engineering Service What to expect This course is designed for developers and data engineers. All students are expected to have basic Linux experience, and basic proficiency with either Python or Scala programming languages. Basic knowledge of SQL is helpful.  Prior knowledge of Spark and Hadoop is not required. DATE: June 16-19, 2026 9:30 - 17:30 (SGT timezone) Virtual Classroom, APAC Read more

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 Data Platform (CDP).  Hands-on exercises allow students to practice writing Spark applications that integrate with CDP core components. Participants will learn how to use Spark SQL to query structured data, how to use Hive features to ingest and denormalize data, and how to work with “big data” stored in a distributed file system. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries. Download full course description  What you'll learn During this course, you will learn how to: Distribute, store, and process data in a CDP cluster Write, configure, and deploy Apache Spark applications Use the Spark interpreters and Spark applications to explore, process, and analyze distributed data Query data using Spark SQL, DataFrames, and Hive tables Deploy a Spark application on the Data Engineering Service What to expect This course is designed for developers and data engineers. All students are expected to have basic Linux experience, and basic proficiency with either Python or Scala programming languages. Basic knowledge of SQL is helpful.  Prior knowledge of Spark and Hadoop is not required. DATE: May 5-8, 2026 9:30 - 17:30 (SGT timezone) Virtual Classroom, APAC Read more

Overview This three-day hands-on training course delivers the key concepts and expertise developers need to optimize the performance of their Apache Spark applications. During the course, participants will learn how to identify common sources of poor performance in Spark applications, techniques for avoiding or solving them, and best practices for Spark application monitoring. Optimizing Apache Spark Applications presents the architecture and concepts behind Apache Spark and underlying data platform, then builds on this foundational understanding by teaching students how to tune Spark application code. The course format emphasizes instructor-led demonstrations illustrate both performance issues and the techniques that address them, followed by hands-on exercises that give students an opportunity to practice what they've learned through an interactive notebook environment. Download full course description What You'll Learn Students who successfully complete this course will be able to: Understand Apache Spark's architecture, job execution, and how techniques such as lazy execution and pipelining can improve runtime performance Evaluate the performance characteristics of core data structures such as RDD and DataFrames Select the file formats that will provide the best performance for your application Identify and resolve performance problems caused by data skew Use partitioning, bucketing, and join optimizations to improve SparkSQL performance Understand the performance overhead of Python-based RDDs, DataFrames, and user-defined functions Take advantage of caching for better application performance Understand how the Catalyst and Tungsten optimizers work Understand how Workload XM can help troubleshoot and proactively monitor Spark applications performance Learn how the Adaptive Query Execution engine improves performance What to Expect This course is designed for software developers, engineers, and data scientists who have experience developing Spark applications and want to learn how to improve the performance of their code. This is not an introduction to Spark. Spark examples and hands-on exercises are presented in Python and the ability to program in this language is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.   DATE: May 26-28, 2026 9:00 - 17:00 (CEST TIMEZONE) Virtual Classroom, EMEA Read more

This three-day course provides participants with a comprehensive understanding of the Cloudera platform and its integrated services, including Cloudera Data Warehouse, Cloudera Data Engineering, Cloudera Data Flow, and Cloudera AI. Participants will gain hands-on experience in designing, implementing, and optimizing data workflows and analytics solutions within the Cloudera ecosystem. The course emphasizes practical strategies for building scalable, secure, and efficient data-driven solutions tailored to enterprise needs. Key topics include data ingestion and processing, stream management, query optimization, machine learning integration, and managing resource performance in production environments. Download full course description   DATE: May 19-21, 2026 9:30 - 17:30 (SGT TIMEZONE) Virtual Classroom, APAC Read more

This course introduces Apache Iceberg, a high-performance open table format for organizing petabyte-scale analytic datasets on a file system or object store, available on Cloudera Data Warehouse and Cloudera Data Engineering on both Private and Public Cloud. Combined with Cloudera Data Platform, Iceberg can enable users to build an open data lakehouse architecture for multi-function analytics and to deploy large-scale end-to-end pipelines. This course covers various aspects of Apache Iceberg, such as benefits, architecture, internal operation, read and write operations, and advanced functions, all while drawing comparisons to Hive and building on the students’ existing knowledge and experience. DATE: May 12-15, 2026 9:30 - 17:30 (SGT TIMEZONE) Virtual Classroom, APAC Read more

This four-day course teaches the architecture, deployment, and configuration of Cloudera Data Services on Embedded Containerized Services (ECS). Cloudera Data Services provide a state-of-the-art, low- code platform that unifies the entire data lifecycle, reducing development costs and accelerating the development and deployment of use cases.   The course starts by covering best practices for managing Docker images and containers. Students will then build a Docker private registry. This Docker private registry will be used to deploy a Data Services cluster on ECS. Students will install, configure, and validate Cloudera Data Engineering, Cloudera Data Warehouse, and Cloudera Machine Learning. Through hands-on exercises, students will gain experience with Kubernetes, install a Private Cloud Embedded Container Service (ECS), and deploy Cloudera Data Services. Additionally, the course covers networking and hardware requirements and explains how Kubernetes pods dynamically scale to support Cloudera Data Services. Who should take this course This immersive course is designed for Cloudera Administrators transitioning to managing Cloudera Data Services on premises. Students should have at least 3 to 5 years of system administration experience. Students must have proficiency in the Linux Command Line Interface and knowledge of Identity Management, including Transport Layer Security and Kerberos. Familiarity with SQL select statements is recommended. Prior experience with Cloudera products is required. Students need reliable internet access to connect to the Amazon Web Services environment used in this course. Recommended prerequisite courses • ADMIN-230: Administering Cloudera on premises • ADMIN-332: Securing Cloudera on premises   DATE: July 14-17, 2026 9:30 - 17:30 (SGT TIMEZONE) Virtual Classroom, APAC Read more