Upcoming Sessions
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March
18
ILT - ADMIN-230: Administrating Cloudera Data Platform - 4224276
Starting:2025/03/18 @ 01:00 AM (GMT+00:00) UTCEnding:2025/03/21 @ 09:00 AM (GMT+00:00) UTCType:Multi-day Session -
March
18
ILT - DANA-262: Analyzing with Cloudera Data Warehouse - 4265173
Starting:2025/03/18 @ 07:00 AM (GMT+00:00) UTCEnding:2025/03/21 @ 03:00 PM (GMT+00:00) UTCType:Multi-day Session
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DO NOT START THIS CERTIFICATION EXAM HERE! Once you have been enrolled, you will receive an email with additional instructions to schedule your exam. Read more

DESCRIPTION [DATE: March 18-21, 2025] Virtual Classroom, [AMER] 9:00 - 17:00 (MST TIMEZONE) Read more

This four-day Analyzing with Data Warehouse course will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages. Download full course description What you'll learn Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the ecosystem, learning how to: Use Apache Hive and Apache Impala to access data through queries Identify distinctions between Hive and Impala, such as differences in syntax, data formats, and supported features Write and execute queries that use functions, aggregate functions, and subqueries Use joins and unions to combine datasets Create, modify, and delete tables, views, and databases Load data into tables and store query results Select file formats and develop partitioning schemes for better performance Use analytic and windowing functions to gain insight into their data Store and query complex or nested data structures Process and analyze semi-structured and unstructured data Optimize and extend the capabilities of Hive and Impala Determine whether Hive, Impala, an RDBMS, or a mix of these is the best choice for a given task Utilize the benefits of CDP Public Cloud Data Warehouse What to expect This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. [DATE: March 18-21, 2025] Virtual Classroom, [EMEA] 9:00 - 17:00 (CET TIMEZONE) 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: March 25-28, 2025] Virtual Classroom, [AMER] 9:00 - 17:00 (Central TIMEZONE) Read more

DESCRIPTION [DATE: October 7-10, 2025] Virtual Classroom, [APAC] 9:00 - 17:00 (Beijing TIMEZONE) Read more

DESCRIPTION [DATE: July 15-18, 2025] Virtual Classroom, [APAC] 9:00 - 17:00 (Beijing TIMEZONE) Read more
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