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Advance your skills and knowledge in Data Analytics by learning from AWS experts
Data Warehousing on AWS
3 Days | Level: Intermediate
Discover concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. You will also learn how to collect, store, and prepare data for the data warehouse by using other AWS services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon Simple Storage Service (Amazon S3) and explore techniques to use business intelligence (BI) tools to perform analysis on your data.
This course is aimed at:
- Data architects
- Database administrators
- Database developers
- Data analysts
- Data scientists
Learn to:
- Evaluate the relationship between Amazon Redshift and other Big Data systems
- Evaluate use cases for data warehousing workloads and review real-world implementation of AWS data and analytic services as part of a data warehousing solution
- Choose an appropriate Amazon Redshift node type and size for your data needsUnderstand which security features are appropriate for Amazon Redshift, such as encryption, IAM permissions, and database permissions
- And much more
Building a Serverless Data Lake
1 Day | Level: Intermediate
This one-day course is formulated to educate you techniques of designing, building, and operating a serverless data lake solution with AWS services. It will cover topics such as ingesting data from any data source at large scale, storing the data securely and durably, enabling the capability to use the right tool to process large volumes of data, and understanding the options available for analyzing the data in near-real time.
This course is aimed at:
- Solutions architects
- Big Data developers
- Data architects and analysts
- Data analysis practitioners
Learn to:
- Collect large amounts of data using services such as Kinesis Streams and Firehose and store the data durably and securely in Amazon Simple Storage Service.
- Create a metadata index of your data lake.
- Choose the best tools for ingesting, storing, processing, and analyzing your data in the lake.
- Apply the knowledge to hands-on labs that provide practical experience with building an end-to-end solution.
- Configure Amazon Simple Notification Service (Amazon SNS) to audit, monitor, and receive event notifications about activities in the data warehouse
- Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters
- Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data
Big Data on AWS
3 Days | Level: Intermediate
Get to familiarize yourself on cloud-based Big Data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform. You'll discover how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. This course also guides you on building 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.
This course is aimed at:
- Solutions architects
- SysOps administrators
- Data scientists
- Data analysts
Learn to:
- Place AWS solutions inside a Big Data ecosystem
- Leverage Apache Hadoop in the context of Amazon EMR
- Identify the components of an Amazon EMR cluster, then launch and configure an Amazon EMR cluster
- Use common programming frameworks available for Amazon EMR, including Hive, Pig, and streaming
- And much more
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