Big Data on AWS
Leverage best practices to design big data solutions on AWS.
Big Data on AWS introduces you to cloud-based big data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon Quicksight, Amazon Athena and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.
Duration3 days/24 hours of instruction
Public Classroom Pricing
Group Rate: $1925
Private Group Pricing
Have a group of 5 or more students? Request special pricing for private group training today.
Download the Course Brochure
- Overview of Big Data
- Big Data Ingestion and Transfer
- Big Data Streaming and Amazon Kinesis
- Lab 1: Using Amazon Kinesis to Stream and Analyze Apache Server Log Data
- Big Data Storage Solutions
- Big Data Processing and Analytics
- Lab 2: Using Amazon Athena to Query Log Data From Amazon S3
- Apache Hadoop and Amazon EMR
- Lab 3: Storing and Querying Data on Amazon DynamoDB
- Using Amazon EMR
- Hadoop Programming Frameworks
- Lab 4: Processing Server Logs With Hive on Amazon EMR
- Web Interfaces on Amazon EMR
- Lab 5: Running Pig Scripts in Hue on Amazon EMR
- Apache Spark on Amazon EMR
- Lab 6: Processing NY Taxi data using Spark on Amazon EMR
- Using AWS Glue to automate ETL workloads
- Amazon Redshift and Big Data
- Visualizing and Orchestrating Big Data
- Lab 7: Using TIBCO Spotfire to Visualize Data
- Managing Big Data Costs
- Securing Your Amazon Deployments
- Big Data Design Patterns
Note: Course outline may vary slightly based on the regional location and/or language in which the class is delivered.
- Solutions Architects
- Data Scientists
- Data Analysts
- Fit AWS solutions inside of a big data ecosystem
- Leverage Apache Hadoop in the context of Amazon EMR
- Identify the components of an Amazon EMR cluster
- Launch and configure an Amazon EMR cluster
- Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
- Leverage Hue to improve the ease-of-use of Amazon EMR
- Use in-memory analytics with Spark on Amazon EMR
- Choose appropriate AWS data storage options
- Identify the benefits of using Amazon Kinesis for near real-time big data processing
- Leverage Amazon Redshift to efficiently store and analyze data
A full refund will be issued for class cancellations made at least 15 business days before the course begins. Payment is non‑refundable for cancellations or reschedules made within 15 business days from the course start date and for No‑Shows (students who do not attend class).
For reschedules made within 15 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of 90 days from the original date.
A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.