Data Engineering on AWS - A Batch Data Pipeline Solution (Includes Labs)

Resumen

Data is constantly flowing into organizations from many sources. To derive insights and value from this data, it needs to go through an orchestrated pipeline of ingestion, storage, processing, and serving stages.

This course will teach you how to build scalable, secure, and cost-effective batch data pipelines on AWS.

You will learn best practices for ingesting batch data from sources like databases and data lakes. The course explores services like AWS Glue and Amazon EMR for processing and transforming the raw data into analytics-ready datasets.

The course covers data cataloging with the AWS Glue Data Catalog. You will also learn how to serve processed data for analysis, machine learning, and reporting using services like Amazon Athena and Amazon QuickSight.

This course includes interactive content, videos, knowledge checks, assessments, and hands-on labs.

In this course, you will learn to do the following:

• Describe the purpose, architecture, and processes of a batch data pipeline solution on AWS.
• Identify the appropriate AWS services and configurations for building a batch data pipeline solution.
• Explain the processes of data ingestion, processing, cataloging, and serving data for consumption in a batch data pipeline.
• Implement automation, orchestration, security, and governance options for a batch data pipeline solution.
• Monitor, optimize, and troubleshoot a batch data pipeline solution on AWS.
• Build and deploy a batch data pipeline solution using AWS services like Amazon EMR, AWS Glue, Amazon S3, and Amazon Athena. (Lab 1 and 2)

This course is intended for the following job roles:

• Data Engineers
• Data Scientists
• Data Analysts
• Business Intelligence Engineers

We recommend that attendees of this course have the following:

• 2-3 years of experience in data engineering
• 1-2 years of hands-on experience with AWS services
• Completed AWS Cloud Practitioner Essentials
• Completed Fundamentals of Analytics on AWS - Parts 1 and 2
• Completed Data Engineering on AWS - Foundations

This section lays the foundation for building a batch data pipeline on AWS.

It covers the key design considerations, data ingestion methods, and provides an assessment to evaluate your understanding of constructing a robust batch data pipeline solution.

• Lesson 1:

  Course Navigation

• Lesson 2:

  Introduction

• Lesson 3:

  Designing a Batch Data Pipeline

• Lesson 4:

  Ingesting Data

• Lesson 5:

  Assessment

• Lesson 6:

  Conclusion

• Lesson 7:

  Contact Us

After designing the batch pipeline, this section dives into the implementation details. You'll learn how to process and transform data, catalog it for governance, and serve it for consumption by analytics tools.

An assessment reinforces the concepts.

• Lesson 1:

  Course Navigation

• Lesson 2:

  Introduction

• Lesson 3:

  Processing and Transforming Data

• Lesson 4:

  Cataloging data

• Lesson 5:

  Serving Data for Consumption

• Lesson 6:

  Assessment

• Lesson 7:

  Conclusion

A Day in the life of a Data Engineer (Lab) (60 min)

In this lab, you will use temperature and precipitation metrics to determine whether a company should stock summer or winter items for various cities. You'll create an AWS Glue crawler, review IAM policies, view the Data Catalog, run a Glue job to transform data,

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