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Cloudera Developer Training for Spark and Hadoop - Virtual English
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11 Oct 2021 - 14 Oct 2021 | Cloudera Developer Training for Spark and Hadoop - Virtual English |
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 2, 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:
- Using the Spark shell for interactive data analysis.
- The features of Spark’s Resilient Distributed Datasets.
- How Spark runs on a cluster.
- Parallel programming with Spark.
- Writing Spark applications.
- Processing streaming data with Spark.
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 Ingestion and Storage
- Data Processing
- Data Analysis and Exploration
- Other Ecosystem Tools
- 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
- Key-Value Pair RDDs
- Map-Reduce
- Other Pair RDD Operations
Aggregating Data with Pair RDDs
Querying Tables and Views with Apache Spark SQL
- Querying Tables in Spark Using SQL
- Querying Files and Views
- The Catalog API
- Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
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
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
Apache Spark Streaming: Introduction to DStreams
- Apache Spark Streaming Overview
- Example: Streaming Request Count
- DStreams
- Developing Streaming Applications
Apache Spark Streaming: Processing Multiple Batches
- Multi-Batch Operations
- Time Slicing
- State Operations
- Sliding Window Operations
- Preview: Structured Streaming
Apache Spark Streaming: Data Sources
- Streaming Data Source Overview
- Apache Flume and Apache Kafka Data Sources
- Example: Using a Kafka Direct Data Source
Open calls
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11 Oct 2021 - 14 Oct 2021 | Cloudera Developer Training for Spark and Hadoop - Virtual English |
