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Data Engineering on AWS

Entering the realm of Data for Engineering the Valuable Insights
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Duration

1 Day

Level

Intermediate Level

Design and Tailor this course

As per your team needs

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This course is designed to cover basic architecture of cloud computing followed by discussing and comparing various services provided by AWS to build a big data pipeline. Finally we will conclude this course by running an actual Big data pipeline using a subset of AWS services.

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This course is designed for Data Engineers and Architects who want to understand how to build a modern data lake and data warehouse.

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  • Capabilities of AWS w.r.t. Data Engineering
  • DataLake vs Datawarehouse
  • Serverless vs Managed Services
  • Comparing various Ingestion services in AWS – (Kinesis/Kafka)
  • Comparing various processing services in AWS – (Glue /Spark/Lambda)
  • Comparing storages in AWS – (DynamoDB/DocumentDB/Amazon Keyspaces)
  • Comparing various analytics services in AWS – (Redshift/Athena/Snowflake)
  • Kinesis – collect data from various sources
  • Amazon S3 – Build DataLake
  • Amazon EMR – Run Spark for Data processing
  • Amazon Glue – Creating Data Catalog on S3
  • Amazon Athena – Running Analytics
  • Aws Lambda – Creating Serverless functions to process data
  • DynamoDB – Storing processed data in nosql database
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Basic knowledge of AWS

  • For experiencing the hands-on during the class, it is recommended that each Participant should have an AWS account, Snowflake account and access to Talend Cloud Data Integration
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