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AWS Data Engineer

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AWS Data Engineer

AWS

Duration
45 Hours

Course Description

              An AWS Data Engineer course typically covers the core AWS services used for building and managing data pipelines, including data ingestion, transformation, storage, and analysis. The curriculum often includes hands-on experience with tools like AWS Glue, Redshift, EMR, Athena, and S3, with a focus on security, cost optimization, and best practices for data engineering on AWS.

Course Outline For AWS Data Engineer

  • 1. Databricks Lakehouse Platform: Covers core concepts, architecture, cluster types, versioning, sharing, and Databricks Repos.
  • 2. ELT with Apache Spark SQL and Python: Focuses on ELT processes, data extraction, transformation, and manipulation techniques.
  • 3. Incremental Data Processing: Explores Delta Lake functionalities, Delta Live Tables, Auto Loader, and change data capture.
  • 4. Production Pipelines: Addresses creating and managing production pipelines, including task management, scheduling, and error handling.
  • 5. Data Governance: Covers data governance principles, meta stores, catalogs, Unity Catalog, and security. 

Exam details

  • The exam has 45 multiple-choice questions.
  • The time limit is 90 minutes.
  • It is an online, proctored exam.
  • Recommended experience is at least 6 months of hands-on data engineering on the Databricks platform.
  • The certification is valid for two years. 

Related training

  • Databricks offers instructor-led and self-paced training for the Databricks Certified Data Engineer Associate Exam.
  • Other online platforms also provide courses for preparation, such as Udemy and Pluralsight. 

Prerequisites

While not mandatory, it is recommended to have:

  • Basic knowledge of SQL and Python.
  • Basic understanding of cloud fundamentals.
  • Experience with the Databricks Lakehouse Platform.
  • Understanding of big data concepts, including the 5 Vs. 
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