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.
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
Comprehend and manage costs and security for a big data solution
Secure a Big Data solution
Identify options for ingesting, transferring, and compressing data
Leverage Amazon Athena for ad-hoc query analytics
Leverage AWS Glue to automate ETL workloads
Use visualization software to depict data and queries using Amazon QuickSight
Orchestrate big data workflows using AWS Data Pipeline
Who Should Attend?
- Individuals responsible for designing and implementing big data solutions, namely Solutions Architects
- Data Scientists and Data Analysts interested in learning about the services and architecture patterns behind big data solutions on AWS
- Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
- Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
- Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
- Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
- Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
- Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.
There are no prerequisites for this course.
1 - Day One
- 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
2 - Day Two
- 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
3 - Day Three
- 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