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Data Analytics
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AWS Ramp-Up Guide: Data Analytics
Built for developers and data engineers, this AWS Ramp-Up Guide offers a variety of resources to help build your knowledge of Data Analytics in the AWS Cloud. It features free digital training, classroom courses, videos, whitepapers, certifications, and more.
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Courses designed to help you develop your foundation
Explore all Data Analytics courses »Data Analytics Fundamentals
FundamentalDigital | 3.5 hoursLearn the process of planning data analysis solutions by exploring five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data.
Introduction to Amazon Elastic MapReduce (EMR)
FundamentalDigital | 15 minutesGet an introduction to Amazon Elastic MapReduce (EMR), learning about the tools to use with Amazon EMR and the process of creating a cluster.
Introduction to AWS Data Pipeline
FundamentalDigital | 10 minutesExplore demonstrations and use cases for AWS Data Pipeline, the web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources.
AWS Hadoop Fundamentals
FundamentalDigital | 90 minutesLearn the basics of Hadoop, how it handles large datasets, and how you can use it in the AWS Cloud.
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Intermediate and advanced courses designed to expand your knowledge
Explore all Data Analytics courses »Big Data on AWS
IntermediateClassroom | 3 daysLearn how to work with Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform to process data and create big data environments.
Exam Readiness: AWS Certified Data Analytics - Specialty
IntermediateClassroom | 1 day Digital | 3.5 hoursPrepare for the AWS Certified Data Analytics - Specialty exam by exploring the exam topic areas. Review sample exam questions, learn to interpret the concepts, and more easily eliminate incorrect responses.
Best Practices for Data Warehousing with Amazon Redshift
IntermediateDigital | 30 minutesLearn about implementing a data warehouse using Amazon Redshift, as well as basic table design, data storage, data ingestion techniques, and workload management.