『Little Fluffy PolyClouds: The Data Engineering Playbook』のカバーアート

Little Fluffy PolyClouds: The Data Engineering Playbook

Little Fluffy PolyClouds: The Data Engineering Playbook

著者： James
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Little Fluffy PolyClouds: The Data Engineering Playbook is your essential guide to building cloud-agnostic data infrastructure. We provide practical, step-by-step strategies for designing and deploying resilient data systems across all major platforms, including AWS, Azure, and GCP.

© 2025 Little Fluffy PolyClouds: The Data Engineering Playbook 教育
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  • Episode 1: Mastering the AWS Data Assembly Line

    Episode 1: Mastering the AWS Data Assembly Line
    2025/10/23

    This is the essential guide to Domain 1: Data Ingestion and Transformation—the biggest section (34%) of the AWS Certified Data Engineer - Associate (DEA-C01) exam! We break down the core components of a successful data pipeline. Learn to compare Batch vs. Streaming with services like Kinesis and DMS, master ETL/ELT using AWS Glue and EMR, and orchestrate complex workflows with Step Functions and MWAA. Stop struggling with pipeline design and start building with confidence. Your DEA-C01 success starts here!
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    18 分
  • Episode 2: AWS Data Store Mastery

    Episode 2: AWS Data Store Mastery
    2025/10/24

    Where should you put your data? We tackle Domain 2 (26% of the DEA-C01 exam) by comparing Redshift, DynamoDB, and RDS. Learn how to design optimal schemas, use the AWS Glue Data Catalog, and implement S3 Lifecycle Policies to manage data lifespan and control costs.

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    14 分
  • Episode 3: The Pipeline Pit Crew: Monitoring, Troubleshooting, and Optimizing Your AWS Data

    Episode 3: The Pipeline Pit Crew: Monitoring, Troubleshooting, and Optimizing Your AWS Data
    2025/10/24

    Keep your data pipelines running smoothly! This episode covers Domain 3 (22% of the DEA-C01 exam). We dive into setting up alarms with CloudWatch, troubleshooting stuck jobs with Glue Logs, optimizing performance and cost in Redshift, and ensuring data quality with AWS Glue DataBrew.

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    13 分
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