Cloudera Educational Services

Overview This five-day course provides the in-depth explanation and skills to become highly productive with Kubernetes by teaching its architecture, deployment, configuration, logging, reporting, and more. Kubernetes's complexity is due to its extensive feature set and intricate object model, which includes concepts like Pods, Deployments, and Services. It also relies on YAML configuration files and requires managing a distributed system across multiple nodes. The course provides references for architecture and recommended practices used by administrators. Download full course description   What you'll learn Understand the architecture of Kubernetes and its components. Deploy and manage applications on Kubernetes. Utilize Kubernetes features for scaling and self-healing. Implement best practices for security and networking. Troubleshoot common issues in Kubernetes environment   Who Should Take This Course? This course is an immersive training for a full range of Kubernetes users, from administrators to DevOps to developers. This course is intended for senior technicians with strong skills in Linux and application lifecycle. Students must have proficiency in Linux CLI. Students must have access to high-speed Internet to download various public Git repositories. The classroom environment is a four-node cluster running on Linux.   DATE: December 14-18, 2026 9:30 - 17:30 (Seoul TIMEZONE) Onsite Classroom, APAC Read more

Overview This five-day course provides the in-depth explanation and skills to become highly productive with Kubernetes by teaching its architecture, deployment, configuration, logging, reporting, and more. Kubernetes's complexity is due to its extensive feature set and intricate object model, which includes concepts like Pods, Deployments, and Services. It also relies on YAML configuration files and requires managing a distributed system across multiple nodes. The course provides references for architecture and recommended practices used by administrators. Download full course description   What you'll learn Understand the architecture of Kubernetes and its components. Deploy and manage applications on Kubernetes. Utilize Kubernetes features for scaling and self-healing. Implement best practices for security and networking. Troubleshoot common issues in Kubernetes environment   Who Should Take This Course? This course is an immersive training for a full range of Kubernetes users, from administrators to DevOps to developers. This course is intended for senior technicians with strong skills in Linux and application lifecycle. Students must have proficiency in Linux CLI. Students must have access to high-speed Internet to download various public Git repositories. The classroom environment is a four-node cluster running on Linux.   DATE: October 12-16, 2026 9:30 - 17:30 (Seoul TIMEZONE) Onsite Classroom, APAC Read more

Do NOT start this certification exam here. Please FOLLOW these steps to schedule your exam*:   1. Log in to Questionmark using your email address. 2. If this is your first time logging in, click the “Request new password” tab, enter your email address, and click “E-mail new password.” 3. Use the one-time link sent to your email to log in and set a new password. 4. Click the "My Assessments" tab at the top of the screen. 5. Click the calendar icon next to the exam to schedule your date and time.   *You will also receive these same instructions in a separate email, to schedule your exam in Questionmark. Read more

This course helps customers use Cloudera Data Platform to address data governance tasks, motivated by the need for compliance with regulations such as the European Union's General Data Protection Regulation (GDPR) and the United State's Health Insurance Portability and Accountability Act (HIPAA). What you'll learn Through instructor-led discussion, demonstrations, and hands-on exercises, you will learn how to: Identify which tools in Cloudera Data Platform (CDP) to use for key data governance activities Organize data objects using classifications and business glossary terms Find access history for data objects and policies Use Data Catalog Profilers in CDP to assist in organizing data objects Use Data Catalog to foster collaboration with colleagues View and interpret a data object's lineage Create and apply resource- and tag-based access control policies Create policies for data masking and row-level filtering What to expect This course is best suited for data stewards and others who are responsible for, or have an interest in, implementing regulatory compliance or performing typical data governance activities using the Cloudera Data Platform. Familiarity with basic data governance concepts is helpful, but not required.   DATE: September 8-9, 2026 9:00 - 17:00 (GMT+2 TIMEZONE) Virtual Classroom, EMEA Read more

Designing Edge to AI Applications is a 4-day learning event that addresses advanced big data architecture topics for building edge to AI applications to cover streaming, operational data processing, analytics, and machine learning.  The workshop brings together technical contributors into a group setting to design and architect solutions to a challenging business problem. The workshop addresses big data architecture problems in general, and then applies them to the design of a challenging system. Throughout the highly interactive workshop, participants apply concepts to real-world examples resulting in detailed synergistic discussions. The workshop is conducive for participants to learn techniques for architecting big data systems, not only from Cloudera’s experience but also from the experiences of fellow participants.  More specifically, this workshop addresses advanced big data architecture topics, including, data formats, transformation, transactions, real-time, batch and machine learning processing, scalability, fault tolerance, security, and privacy, minimizing the risk of an unsound architecture and technology selection. What you'll learn Cloudera Data Platform Big Data Architecture Building Scalable applications Building Fault Tolerant Solutions Security and Privacy Deployment on Public, Private, and Hybrid Cloud What to expect Participants should mainly be architects, developer team leads, big data developers, data engineers, senior analysts, dev ops admins and machine learning developers who are working on big data or streaming applications and have an interest in how to design and develop such applications on CDP. To gain the most from the workshop, participants should have working knowledge of popular Big Data and streaming technologies such as HDFS, Spark, Kafka, Hive/Impala, Data Formats, and relational database management systems. Detailed API level knowledge is not needed, as there will not be any programming activities and instead the focus will be on architecture design. The workshop will be divided into small groups to discuss the problems, develop solutions, and present their solutions.   DATE: September 28 - October 1, 2026 9:00 - 17:00 (GMT+2 TIMEZONE) Virtual Classroom, EMEA 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: September 14-16, 2026 9:00 - 17:00 (GMT+2 TIMEZONE) Virtual Classroom, EMEA Read more