Cloudera

Cloudera Developer Training for Spark and Hadoop

28 hours
1995 €
Classroom or Live Virtual Class
Classroom or Live Virtual Class

23 Nov 2020 - 26 Nov 2020   |  

28 h.    1995 €

Cloudera Developer Training for Spark and Hadoop

28 h | 1995 € | Madrid o Live Virtual Class | Spanish
from Monday to Thursday (09:00h - 17:00h)
Calendario de sesiones

23 Nov 2020 - 26 Nov 2020   |  

28 h.    1995 €

Cloudera Developer Training for Spark and Hadoop

28 h | 1995 € | Barcelona o Live Virtual Class | Spanish
from Monday to Thursday (09:00h - 17:00h)
Calendario de sesiones

Description

This Spark course enables participants to build complete, unified Big Data applications combining batch, streaming, and interactive analytics on all their data. With Apache Spark, developers can write sophisticated parallel applications for faster business decisions and better user outcomes, applied to a wide variety of use cases, architectures, and industries.

This course is part of the developer learning path. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with large datasets stored in a distributed file system, and execute Spark applications on a Hadoop cluster. 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.

PUE is Cloudera Training Partner, authorized by Cloudera to deliver official training in Cloudera technologies.

Furthermore, PUE is accredited and recognized to carry out consulting and mentoring services in the implementation of Cloudera solutions in the business field with the added value in the practical and business approach to knowledge that is translated in its official courses.

Audience and prerequisites

This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop and/or Spark is not required.

  • Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required.
  • Basic familiarity with the Linux command line is assumed.
  • Basic knowledge of SQL is helpful.

Objectives

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • How the Apache Hadoop ecosystem fits in with the data processing lifecycle
  • How data is distributed, stored, and processed in a Hadoop cluster
  • How to write, configure, and deploy Apache Spark applications on a Hadoop cluster
  • How to use the Spark shell and Spark applications to explore, process, and analyze distributed data
  • How to query data using Spark SQL, DataFrames, and Datasets
  • How to use Spark Streaming to process a live data stream

Certification included

This is the official course recommended by Cloudera for preparing their associated official certification exam valued at 295.00€, which is included in the price of the course for all members of the PUE Alumni program.

The successful completion of this exam is needed for obtaining Cloudera Certified Associate Spark and Hadoop Developer certification. This certification has been designed to verify that candidates have acquired the concepts and skills required in the following areas:

  • Data ingest.
  • Transformation, stage and store.
  • Data analysis.

Topics

Introduction to Apache Hadoop and the Hadoop Ecosystem

  • Apache Hadoop Overview
  • Data Processing
  • Introduction to the Hands-On Exercises

Apache Hadoop File Storage

  • Apache Hadoop Cluster Components
  • HDFS Architecture
  • Using HDFS

Distributed Processing on an Apache Hadoop Cluster

  • YARN Architecture
  • Working With YARN

Apache Spark Basics

  • What is Apache Spark?
  • Starting the Spark Shell
  • Using the Spark Shell
  • Getting Started with Datasets and DataFrames
  • DataFrame Operations

Working with DataFrames and Schemas

  • Creating DataFrames from Data Sources
  • Saving DataFrames to Data Sources
  • DataFrame Schemas
  • Eager and Lazy Execution

Analyzing Data with DataFrame Queries

  • Querying DataFrames Using Column Expressions
  • Grouping and Aggregation Queries
  • Joining DataFrames

RDD Overview

  • RDD Overview
  • RDD Data Sources
  • Creating and Saving RDDs
  • RDD Operations

Transforming Data with RDDs

  • Writing and Passing Transformation Functions
  • Transformation Execution
  • Converting Between RDDs and DataFrames

Aggregating Data with Pair RDDs

  • Key-Value Pair RDDs
  • Map-Reduce
  • Other Pair RDD Operations

Querying Tables and Views with SQL

  • Querying Tables in Spark Using SQL
  • Querying Files and Views
  • The Catalog API

Working with Datasets in Scala

  • Datasets and DataFrames
  • Creating Datasets
  • Loading and Saving Datasets
  • Dataset Operations

Writing, Configuring, and Running Apache Spark Applications

  • Writing a Spark Application
  • Building and Running an Application
  • Application Deployment Mode
  • The Spark Application Web UI
  • Configuring Application Properties

Spark Distributed Processing

  • Review: Apache Spark on a Cluster
  • RDD Partitions
  • Example: Partitioning in Queries
  • Stages and Tasks
  • Job Execution Planning
  • Example: Catalyst Execution Plan
  • Example: RDD Execution Plan

Distributed Data Persistence

  • DataFrame and Dataset Persistence
  • Persistence Storage Levels
  • Viewing Persisted RDDs

Common Patterns in Apache Spark Data Processing

  • Common Apache Spark Use Cases
  • Iterative Algorithms in Apache Spark
  • Machine Learning
  • Example: k-means

Introduction to Structured Streaming

  • Apache Spark Streaming Overview
  • Creating Streaming DataFrames
  • Transforming DataFrames
  • Executing Streaming Queries

Structured Streaming with Apache Kafka

  • Overview
  • Receiving Kafka Messages
  • Sending Kafka Messages

Aggregating and Joining Streaming DataFrames

  • Streaming Aggregation
  • Joining Streaming DataFrames

Conclusion

Message Processing with Apache Kafka

  • What Is Apache Kafka?
  • Apache Kafka Overview
  • Scaling Apache Kafka
  • Apache Kafka Cluster Architecture
  • Apache Kafka Command Line Tools

Open calls

23 Nov 2020 - 26 Nov 2020   |  

28 h.    1995 €

Cloudera Developer Training for Spark and Hadoop

28 h | 1995 € | Madrid o Live Virtual Class | Spanish
from Monday to Thursday (09:00h - 17:00h)
Calendario de sesiones

23 Nov 2020 - 26 Nov 2020   |  

28 h.    1995 €

Cloudera Developer Training for Spark and Hadoop

28 h | 1995 € | Barcelona o Live Virtual Class | Spanish
from Monday to Thursday (09:00h - 17:00h)
Calendario de sesiones