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Databricks Certified Data Engineer Associate Course Overview

Databricks Certified Data Engineer Associate Course Overview

### Databricks Certified Data Engineer Associate Course Overview

The Databricks Certified Data Engineer Associate course equips learners with essential skills to use the Databricks Lakehouse Platform effectively. Participants will gain a comprehensive understanding of the Lakehouse architecture, including ETL tasks with Apache Spark SQL and Python, data processing, and production pipelines. This course also delves into data governance and Databricks SQL queries.

The curriculum covers real-world scenarios, enabling learners to perform data engineering tasks proficiently, such as developing and maintaining ETL pipelines, ensuring data quality, and managing Databricks clusters. This combination of knowledge and practical application prepares individuals for the certification exam, helping them advance their careers in data engineering.

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(without exam) 		850 (USD)
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Course Prerequisites

Minimum Required Prerequisites for the Databricks Certified Data Engineer Associate Course

Before enrolling in the Databricks Certified Data Engineer Associate course, it is beneficial for students to have the following foundational knowledge and skills to maximize their learning experience:


  • Basic Understanding of Databricks: Familiarity with the Databricks Lakehouse Platform and its basic functionalities.
  • Introductory Knowledge of Data Engineering: Basic concepts of data engineering, including data lakes, data warehouses, ETL processes, and data pipelines.
  • Experience with SQL and Python: Fundamental skills in SQL and Python programming, as these are essential languages for data manipulation in Databricks.
  • Familiarity with Apache Spark: Basic understanding of Apache Spark, including its core features and functionalities.
  • Hands-on Experience: Recommended but not mandatory, about six months of practical experience working with Databricks and its related tools.

These prerequisites are designed to ensure students can comfortably follow along with the course content and successfully apply their learning in practical scenarios.


Target Audience for Databricks Certified Data Engineer Associate

The Databricks Certified Data Engineer Associate course is designed for individuals aiming to validate their ability to perform foundational data engineering tasks using the Databricks Lakehouse Platform.


  • Data Engineers
  • Data Analysts transitioning to Data Engineering roles
  • Database Administrators looking to upskill
  • ETL Developers
  • Aspiring Data Scientists with an interest in data engineering tasks
  • Software Engineers working on data-centric projects
  • IT professionals with six months of hands-on experience in Databricks
  • Business Intelligence (BI) professionals exploring Databricks solutions
  • Analytics Professionals who want to enhance their data engineering capabilities
  • Students and recent graduates in computer science/IT fields seeking to enter the data engineering domain


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

Databricks Certified Data Engineer Associate Course: Learning Objectives and Outcomes

This course is designed to equip individuals with the foundational knowledge and skills required to perform introductory data engineering tasks using the Databricks Lakehouse Platform, including ETL operations, data processing, production pipelines, and data governance.

Learning Objectives and Outcomes:

  • Databricks Lakehouse Platform:

    • Understand the relationship between data lakehouses and data warehouses.
    • Identify the elements of Databricks Platform Architecture.
    • Differentiate between all-purpose clusters and job clusters.

  • ELT with Apache Spark:

    • Create views, temporary views, and CTEs in reference to files.
    • Extract, transform, and load (ETL) data using Spark SQL and Python.
    • Deduplicate and clean data in Delta Lake tables.

  • Incremental Data Processing:

    • Understand and leverage ACID transactions in Delta Lake.
    • Compare and contrast managed and external tables.
    • Perform incremental data loading and processing.

  • Production Pipelines:

    • Set up and manage multiple tasks in Databricks Jobs.
    • Schedule, debug, and retry failed tasks.
    • Create and manage alerts for failed tasks.

  • Data Governance:

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