Cloudera Educational Services

This four-day instructor-led course begins by introducing Apache Kafka, explaining its key concepts and architecture, and discussing several common use cases. Building on this foundation, you will learn how to plan a Kafka deployment, and then gain hands-on experience by installing and configuring your own cloud-based, multi-node cluster running Kafka on the Cloudera Data Platform (CDP). You will then use this cluster during more than 20 hands-on exercises that follow, covering a range of essential skills, starting with how to create Kafka topics, producers, and consumers, then continuing through progressively more challenging aspects of Kafka operations and development, such as those related to scalability, reliability, and performance problems. Throughout the course, you will learn and use Cloudera’s recommended tools for working with Kafka, including Cloudera Manager, Schema Registry, Streams Messaging Manager, and Cruise Control. DATE: November 10-13, 2025 Virtual Classroom, EMEA 9:00 - 17:00 (CET Timezone) Read more

The Cloudera platform is intended to meet the most demanding technical audit standards. The significant improvements in Cloudera architecture and components make Cloudera “Secure by Design.” This four-day hands-on course is presented as a project plan for Cloudera administrators to build fully secured Cloudera clusters. The course begins with implementing Perimeter Security by installing host level security and Kerberos. Next, students protect Data by implementing Transport Layer Security using Auto-TLS and data encryption using Key Management System and Key Trustee Server (KMS/KTS). Following this, in the third stage, students control access for users and to data using Apache Ranger and Apache Atlas. The fourth stage focuses on visibility practices, teaching students how to audit systems, users, and data usage. Finally, the course introduces Cloudera practices for Risk Management in a fully secured Cloudera platform. This course is 60% exercise and 40% lecture.     DATE: November 10-13, 2025 Virtual Classroom, AMER 9:00 - 17:00 (Central US TIMEZONE) 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: November 19-21, 2025 Virtual Classroom, EMEA - FRENCH 9:00 - 17:00 (CET TIMEZONE) Read more

Welcome to our Introduction Series for Cloudera Education. The following contains excerpts from Admin:230 Administering Cloudera on premises, that is part of our full OnDemand Training Library.  The complete library is available for purchase. Upon completion you will see a path to continue your Cloudera training journey. Many courses include hands-on labs and the OnDemand library comes with 100 hands-on lab hours to practice the concepts and exercises taught. Please enjoy this section of your selected course to help you on your data journey. Disclaimer - The following descriptions and objectives are for the full course. Overview Lab environment is included with this course. Starting in lesson 04 you will be able to launch your environment. This course presents detailed explanation, comprehensive theory, key skills, and recommended practices for successful platform administration. Upon completion of this course a Cloudera Administrator will learn the full range of functionality and capability of Cloudera Manager. This course provides an in-depth explanation and skills to become highly productive with Cloudera Manager and the Cloudera platform. Cloudera Manager is a full featured and mature DevOps tool. It is used to install, configure, operate, troubleshoot, report, and upgrade Cloudera. Many Cloudera Administrators only use a fraction of the capabilities built into Cloudera Manager. This course teaches the architecture, deployment, configuration, logging, reporting, REST API, and much more. The course provides references for architecture and recommended practices used by enterprises around the globe. What to expect While this course is an entry point for aspiring Cloudera Administrators this course is detailed enough for more senior Cloudera Administrators to discover new functionality and capabilities. This course is intended for Linux Administrators who are taking up roles as Platform Administrators. We recommend a minimum of 2 years of system administration experience in industry. Students must have proficiency in Linux. Knowledge of Directory Services, Transport Layer Security, Kerberos, and SQL select statements is helpful. Students must have access to the Internet to reach Amazon Web Services. Read more

Welcome to our Introduction Series for Cloudera Education. The following contains excerpts from DSCI:272 - Predicting with MLOps on Cloudera AI, that is part of our full OnDemand Training Library. The complete library is available for purchase. Upon completion you will see a path to continue your Cloudera training journey. Many courses include hands-on labs and the OnDemand library comes with 100 hands-on lab hours to practice the concepts and exercises taught. Please enjoy this section of your selected course to help you on your data journey. Disclaimer - The following descriptions and objectives are for the full course.   Overview Enterprise data science teams need collaborative access to business data, tools, and computing resources required to develop and deploy machine learning workflows. Cloudera AI, part of the Cloudera platform, provides the solution, giving data science teams the required resources. This course covers machine learning workflows and operations using Cloudera AI. Participants explore, visualize, and analyze data. You will also train, evaluate, and deploy machine learning models. The course walks through an end-to-end data science and machine learning workflow based on realistic scenarios and datasets from a fictitious technology company. The demonstrations and exercises are conducted in Python (with PySpark) using Cloudera AI. ​ Course Length This course includes approximately 8.5 hours of video lectures and demonstrations.  You will need your own environment to work on the labs. The labs will take approximately 7 hours to complete.   What to expect The course is designed for data scientists who need to understand how to utilize Cloudera AI and the Cloudera platform to achieve faster model development and deliver production machine learning at scale. Data engineers, developers, and solution architects who collaborate with data scientists will also find this course valuable. Read more

About This Course Explore the core features of Cloudera AI and how they power modern AI and ML workflows. This course introduces Cloudera’s end-to-end AI capabilities—from data engineering and warehousing to model deployment—all built on a unified platform designed for enterprise-grade AI. You’ll gain foundational knowledge of tools like the Cloudera AI Workbench, AI Inference Service, Model Hub, AI Registry, and Private AI architecture, and understand how they come together to support scalable, secure, and efficient AI solutions.  Learn how the enterprise can Infuse, Build, and Run AI with Cloudera. Goal: By the end of this course, you’ll be able to identify the key components of Cloudera AI and explain how they support enterprise AI initiatives from data to deployment. TIME:  45 minutes Read more