Cloudera

Cloudera Data Platform (CDP) is a fully integrated edge to AI product set. Cloudera Manager is purposely built as the DevOps tooling for building and managing Cloudera Data Platform. This four-day hands-on course presents detailed explanation, comprehensive theory, key skills, and recommended practices for successful platform administration. Upon completion of this course a CDP Administrator will learn the full range of functionality and capability of Cloudera Manager in supporting Cloudera Data Platform.

This course provides an in-depth explanation and skills to become highly productive with Cloudera Manager and Cloudera Data Platform. Cloudera Manager is a full featured and mature DevOps tool. It is used to install, configure, operate, troubleshoot, report, and upgrade CDP. Many CDP 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.

Apache Ozone is the next-generation hybrid storage service offering versatility and out-of-the-box compatibility. Ozone is an object storage format exceeding the limitations of HDFS. This course teaches architecture, internal operations, installation, file system usage, best practices, security, maintenance, monitoring, tuning and testing.

The significant improvements in CDP architecture and tools makes CDP “Secure by Design.” The Cloudera Data Platform is intended to meet the most demanding technical audit standards. This four-day hands-on course is presented as a project plan for CDP administrators to achieve technical audit standards.

The first project stage is implementing Perimeter Security by installing host level security and Kerberos. The second project stage protects Data by implementing Transport Layer Security using Auto-TLS and data encryption using Key Management System and Key Trustee Server (KMS/KTS). The third project stage controls Access for users and to data using Ranger and Atlas. The fourth stage teaches Visibility practices for auditing systems, users, and data usage. This project stage also analyzes applications in terms of vulnerabilities and introduces CDP practices for Risk Management in a fully secured Cloudera Data Platform.

This four-day course teaches the architecture, deployment, configuration, and running of CDP Data Services on Embedded Containerized Services (ECS). CDP Data Services are state-of-the-art low code computing fusing together the entire data lifecycle into a single set of tools, reducing the costs of developing Use Cases while accelerating development and deployment.

The course begins with practices recommended for managing Docker images and containers resulting in the building of a Docker private registry. The Docker private registry is used to deploy the Data Services cluster on ECS. Students will learn to install, configure, and validate Cloudera Data Engineering, Cloudera Data Warehouse, and Cloudera Machine Learning. Exercises focus on learning Kubernetes, installing Private Cloud Embedded Container Service (ECS), and deploying Cloudera Data Services. The course includes requirements for networking and hardware, and explanations of Kubernetes pods dynamically scaling to support CDP Data Services.

CDP Public Cloud Administrator Training provides participants with a comprehensive understanding of all the steps required to configure, operate, and maintain CDP Public Cloud instances. This instructor-led course covers everything from setup to configuring various data services to execute workloads on the cloud on all major cloud providers using Cloudera Management Console. It also covers various configuration options using the web interface and automation scenarios using Ansible. On the optimization side, it covers load balancing and tuning CDP PC instances. This Cloudera training course is the best preparation for the real-world challenges faced by administrators running CDP Public Cloud.

This hands-on training course delivers the key concepts and expertise developers need to improve 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.

Apache Spark Application Performance Tuning 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. The course applies to Spark 2.4, but also introduces the Spark 3.0 Adaptive Query Execution framework.

This course enables participants to store and access massive quantities of multi-structured data and perform hundreds of thousands of operations per second.

Apache HBase is a distributed, scalable, NoSQL database built on Apache Hadoop. HBase can store data in massive tables consisting of billions of rows and millions of columns, serve data to many users and applications in real time, and provide fast, random read/write access to users and applications.

This 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.

This 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.

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 course introduces Apache Ozone, a hybrid storage service addressing the limitations of HDFS. You'll also explore Apache Iceberg, an open-table format optimized for petabyte-scale datasets. The course covers Iceberg's benefits, architecture, read/write operations, streaming, and advanced features like time travel, partition evolution, and Data-as-Code. Over 25 hands-on labs and a capstone project will equip you with the skills to build an efficient, performant Open Data Lakehouse within your own environment.

This 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.

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).

One of the most critical functions of a data-driven enterprise is the ability to manage ingest and data flow across complex ecosystems.  Does your team have the tools and skill sets to succeed at this?

Apache NiFi and this four-day course provides the fundamental concepts and experience necessary to automate the ingress, flow, transformation, and egress of data using NiFi. The course also covers tuning, troubleshooting, and monitoring the dataflow process as well as how to integrate a dataflow within the Cloudera CDP Hybrid ecosystem and external systems.

Enterprise data science teams need collaborative access to business data, tools, and computing resources required to develop and deploy machine learning workflows. Cloudera Machine Learning (CML), part of the Cloudera Data Platform (CDP), provides the solution, giving data science teams the required resources.

This course covers machine learning workflows and operations using CML. 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 CML.

During this instructor-led training course, participants will learn development and operations for Cloudera Streaming Analytics, a framework for low-latency processing and analytics powered by Apache Flink and Cloudera's innovative SQL Stream Builder.

Through extensive hands-on exercises, students will gain experience deploying and managing a Flink cluster, developing and running Flink applications, and using SQL Stream Builder's continuous SQL to perform analytics on streaming data.