course overview
Overview
Scala and Python developers will learn key concepts and gain the expertise needed to ingest and process data, and develop high-performance applications using Apache Spark 2.
This four-day hands-on training course delivers the key concepts and expertise developers need to develop high-performance parallel applications with Apache Spark 2. 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.
Hands-on exercises take place on a live cluster, running in the cloud. A private cluster will be built for each student to use during the class.
With this course update, the agenda is streamlined to help you quickly become productive with the most important technologies, including Spark 2.
Audience
This course is designed for:
Skills Gained
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning skills such as:
Prerequisites
The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
Outline
1. Introduction
2. Introduction to Apache Hadoop and the Hadoop Ecosystem
3. Apache Hadoop File Storage
4. Distributed Processing on an Apache Hadoop Cluster
5. Apache Spark Basics
6. Working with DataFrames and Schemas
7. Analyzing Data with DataFrame Queries
8. RDD Overview
9. Transforming Data with RDDs
10. Aggregating Data with Pair RDDs
11. Querying Tables and Views with SQL
12. Working with Datasets in Scala
13. Writing, Configuring, and Running Spark Applications
14. Spark Distributed Processing
15. Distributed Data Persistence
16. Common Patterns in Spark Data Processing
17. Introduction to Structured Streaming
18. Structured Streaming with Apache Kafka
19. Aggregating and Joining Streaming DataFrames
20. Conclusion A. Message Processing with Apache Kafka
If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.
Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.
All $ prices are in USD unless it’s a NZ or AU date
SPVC = Self Paced Virtual Class
LVC = Live Virtual Class
Our clients have included prestigious national organisations such as Oxford University Press, multi-national private corporations such as JP Morgan and HSBC, as well as public sector institutions such as the Department of Defence and the Department of Health.
