Azure Databricks & Spark For Data Engineers:Hands-on Project
What you will learn
You will learn how to build a real world data project using Azure Databricks and Spark Core. This course has been taught using real world data.
You will acquire professional level data engineering skills in Azure Databricks, Delta Lake, Spark Core, Azure Data Lake Gen2 and Azure Data Factory (ADF)
You will learn how to create notebooks, dashboards, clusters, cluster pools and jobs in Azure Databricks
You will learn how to ingest and transform data using PySpark in Azure Databricks
You will learn how to transform and analyse data using Spark SQL in Azure Databricks
You will learn about Data Lake architecture and Lakehouse Architecture. Also, you will learn how to implement a Lakehouse architecture using Delta Lake.
You will learn how to create Azure Data Factory pipelines to execute Databricks notebooks
You will learn how to create Azure Data Factory triggers to schedule pipelines as well as monitor them.
You will gain the skills required around Azure Databricks and Data Factory to pass the Azure Data Engineer Associate certification exam DP203
You will learn how to connect to Azure Databricks from PowerBI to create reports
You will gain a comprehensive understanding about Unity Catalog and the data governance capabilities offered by Unity Catalog.
You will learn to implement a data governance solution using Unity Catalog enabled Databricks workspace.
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Comidoc Review
Our Verdict
This well-structured and engaging Azure Databricks & Spark For Data Engineers: Hands-on Project course has earned its 4.63 global rating with a mix of theoretical knowledge and valuable hands-on experience. The competent instructors provide clear explanations on complex concepts, making this resource suitable for both beginners and seasoned professionals alike. However, despite its strong suit in delivering practical examples, the course falls short when it comes to keeping up-to-date with recent changes and additions in Databricks—namely Unity Catalog. While certain users appreciate the project-based approach of this course, others would benefit from a more comprehensive practical exploration within the course itself. Addressing the mentioned gaps could significantly enhance user experience and provide data engineers looking to upskill themselves with an even more valuable foundation in Azure Databricks and Spark—one that covers all aspects needed by modern professionals in the field.
What We Liked
- Comprehensive coverage of essential topics, making it a great reference resource for data engineering projects.
- Knowledgeable instructors who explain complex concepts in an easy-to-understand manner, helping learners grasp fundamental ideas.
- Hands-on assignments and practical examples that reinforce learning, enabling students to apply their skills effectively.
- Proactive instructor addressing questions within a few hours in the Q&A section—valuable support for learners.
Potential Drawbacks
- Some users report outdated course content, which may cause confusion when working with new Azure Databricks interfaces and features.
- Limited focus on specific topics like Unity Catalog in comparison to the depth of other subjects covered.
- Occasional issues with following hands-on exercises due to minor bugs or inconsistencies within the course materials.
- The repetitive use of a single Formula1 dataset might result in familiarity, but also potential confusion for learners.