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AWS Certified Data Engineer - Associate Course Overview

AWS Certified Data Engineer - Associate Course Overview

The AWS Certified Data Engineer - Associate course is a 3-day, 24-hour intensive program designed to equip participants with practical skills and knowledge in Data ingestion, transformation, and management using AWS. Through modules covering topics like Setting up schedulers, Optimizing data processing, and Managing data pipelines, learners will gain hands-on experience with AWS services such as Kinesis, Redshift, Glue, and S3. The course also emphasizes Data security and governance, including Authentication, Encryption, and Compliance. Perfect for IT professionals, this course ensures you can design, implement, and maintain robust data solutions on AWS.

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Target Audience for AWS Certified Data Engineer - Associate

  1. The AWS Certified Data Engineer - Associate course equips IT professionals with essential skills for managing data pipelines and AWS services, tailored for data engineers and related roles.


  2. Target Audience:


    • Data Engineers
    • Data Architects
    • Database Administrators
    • ETL Developers
    • Cloud Solutions Architects
    • Big Data Professionals
    • DevOps Engineers
    • Machine Learning Engineers
    • IT Managers
    • Cloud Engineers
    • Data Analysts
    • System Administrators
    • Data Scientists
    • Software Engineers focusing on data operations
    • IT Professionals transitioning to cloud data management


Learning Objectives - What you will Learn in this AWS Certified Data Engineer - Associate?

Introduction

The AWS Certified Data Engineer - Associate course enables professionals to master data ingestion, transformation, storage management, data operations, and security on AWS, preparing them to efficiently design and manage data solutions on the AWS platform.

Learning Objectives and Outcomes

  • Perform Data Ingestion

    • Read and ingest data from sources like Kinesis, MSK, and Redshift.
    • Implement batch ingestion and configure schedulers and event triggers.
    • Manage data distribution through throttling and fan-in/fan-out strategies.

  • Transform and Process Data

    • Optimize container usage for data performance.
    • Integrate and transform data from multiple sources using AWS services like EMR, Glue, and Lambda.
    • Convert data formats and debug transformation failures.

  • Choose and Configure Data Stores

    • Implement and configure storage solutions including Redshift, DynamoDB, and S3.
    • Integrate AWS Transfer Family for data migration.
    • Utilize advanced query and view capabilities with Redshift Federated Queries and Spectrum.

  • Data Cataloging and Management

    • Use Glue Data Catalog and Hive Metastore to build and reference data catalogs.
    • Synchronize partitions and manage data lifecycle policies in S3 and DynamoDB.

  • **Data Modeling and Schema

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