This OnDemand offering provides you with a 180-day subscription that begins on the date of purchase.
Skills Gained
Through instructor-led discussion and interactive, hands-on exercises, participants will learn Apache Spark and how it integrates with the entire Hadoop ecosystem, learning:
- How data is distributed, stored, and processed in a Hadoop cluster
- How to use Sqoop and Flume to ingest data
- How to process distributed data with Apache Spark
- How to model structured data as tables in Impala and Hive
- How to choose the best data storage format for different data usage patterns
- Best practices for data storage
Prerequisites
This course is designed for developers and engineers who have programming experience. Apache Spark examples and hands-on exercises are presented in Scala and Python, so 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. Prior knowledge of Hadoop is not required.
This four-day hands-on training course delivers the key concepts and expertise participants need to ingest and process data on a Hadoop cluster using the most up-to-date tools and techniques. Employing Hadoop ecosystem projects such as Spark, Hive, Flume, Sqoop, and Impala, this training course is the best preparation for the real-world challenges faced by Hadoop developers. Participants learn to identify which tool is the right one to use in a given situation, and will gain hands-on experience in developing using those tools
CCA Spark & Hadoop Developer
This course is an excellent place to start for people working towards the CCA Spark & Hadoop Developer certification. Although further study is required before passing the exam, this course covers many of the subjects tested in the CCA Spark & Hadoop Developer exam. After successfully completing this course, we recommend that participants attend Cloudera’s Designing and Building Big Data Applications course, which builds on the foundations taught here.
Introduction to Hadoop and the Hadoop Ecosystem
- Problems with Traditional Large-Scale Systems
- Hadoop!
- Data Storage and Ingest
- Data Processing
- Data Analysis and Exploration
- Other Ecosystem Tools
- Introduction to the Hands-On Exercises
Hadoop Architecture and HDFS
- Distributed Processing on a Cluster
- Storage: HDFS Architecture
- Storage: Using HDFS
- Resource Management: YARN Architecture
- Resource Management: Working with YARN
Importing Relational Data with Apache Sqoop
- Sqoop Overview
- Basic Imports and Exports
- Limiting Results
- Improving Sqoop’s Performance
- Sqoop 2
Introduction to Impala and Hive
- Introduction to Impala and Hive
- Why Use Impala and Hive?
- Querying Data With Impala and Hive
- Comparing Hive and Impala to Traditional Databases
Modeling and Managing Data with Impala and Hive
- Data Storage Overview
- Creating Databases and Tables
- Loading Data into Tables
- HCatalog
- Impala Metadata Caching
Data Formats
- Selecting a File Format
- Hadoop Tool Support for File Formats
- Avro Schemas
- Using Avro with Impala, Hive, and Sqoop
- Avro Schema Evolution
- Compression
Data File Partitioning
- Partitioning Overview
- Partitioning in Impala and Hive
Capturing Data with Apache Flume
- What is Apache Flume?
- Basic Flume Architecture
- Flume Sources
- Flume Sinks
- Flume Channels
- Flume Configuration
Spark Basics
- What is Apache Spark?
- Using the Spark Shell
- RDDs (Resilient Distributed Datasets)
- Functional Programming in Spark
Working with RDDs in Spark
- Creating RDDs
- Other General RDD Operations
Writing and Deploying Spark Applications
- Spark Applications vs. Spark Shell
- Creating the SparkContext
- Building a Spark Application (Scala and Java)
- Running a Spark Application
- The Spark Application Web UI
- Configuring Spark Properties
- Logging
Parallel Processing in Spark
- Review: Spark on a Cluster
- RDD Partitions
- Partitioning of File-Based RDDs
- HDFS and Data Locality
- Executing Parallel Operations
- Stages and Tasks
Spark RDD Persistence
- RDD Lineage
- RDD Persistence Overview
- Distributed Persistence
Common Patterns in Spark Data Processing
- Common Spark Use Cases
- Iterative Algorithms in Spark
- Graph Processing and Analysis
- Machine Learning
- Example: k-means
DataFrames and Spark SQL
- Spark SQL and the SQL Context
- Creating DataFrames
- Transforming and Querying DataFrames
- Saving DataFrames
- DataFrames and RDDs
- Comparing Spark SQL, Impala, and Hive-on-Spark
