LEARN AWS GLUE: Create Serverless ETL Pipelines and Data Catalogs: 15 (Data Extreme USA) - Softcover

Rodrigues, Diego; Smart Tech Content, StudioD21

Softcover

ISBN 13: 9798265072450

Publisher: Independently published, 2025

Synopsis

LEARN AWS GLUE Create Serverless ETL Pipelines and Data Catalogs

Recommended for students and professionals seeking practical mastery in data orchestration, serverless ETL, and corporate pipeline automation with AWS Glue. The content covers everything from job creation, execution, and monitoring to advanced integration with AWS S3, Redshift, Athena, RDS, DynamoDB, Lake Formation, Glue Data Catalog, as well as connection with tools like Databricks, Apache Spark, Airflow, Python, PySpark, Pandas, Grafana, Kibana, and multi-cloud integrations with Azure and Google Cloud. Learn how to transform, catalog, and automate data flows, create scalable workflows, apply validation, tuning, partitioning, and optimize big data analytics in professional environments.

Gain autonomy to build robust pipelines, implement data lakes, governance, and secure large-scale integrations.

You will learn:

• Configure and run serverless ETL jobs with Glue and PySpark

• Integrate AWS Glue with S3, Redshift, Athena, RDS, and DynamoDB

• Orchestrate multi-cloud pipelines, data lakes, and data automation

• Manage Glue Data Catalog, workflows, triggers, and crawlers

• Apply validation, tuning, partitioning, and performance in big data

• Connect Glue to Databricks, Airflow, Python, Pandas, and Spark

• Monitor and audit pipelines with Grafana, Kibana, and CloudWatch

• Ensure security, governance, and automation of data routines

• Optimize analytics, reporting, and corporate BI processes

By the end, you will master AWS Glue to build serverless, scalable, and secure ETL solutions ready for data projects, analytics, multi-cloud integration, and corporate automation.

aws glue, etl, serverless, pipelines, s3, redshift, athena, data catalog, pyspark, airflow, databricks, big data, automation, cloud integration, governance, analytics, monitoring, partitioning, tuning

Edition details

• Publisher: Independently published
• Publication date: 2025
• Language: English
• ISBN 13: 9798265072450
• Binding: Paperback
• Number of pages: 261