Azure Databricks & Spark For Data Engineers:Hands-on Project
Why take this course?
🌟 Azure Databricks & Spark For Data Engineers (PySpark / SQL) 🌟
Real World Project on Formula1 Racing using Azure Databricks, Delta Lake, Unity Catalog, Azure Data Factory [DP203]
Course Updates:
📅 May 2023: New sections 25, 26, and 27 added to include Unity Catalog. These sections cover all aspects of Unity Catalog and its implementation in a project context.
📅 March 2023: New sections 6 and 7 added, with section 8 updated. Reflecting the latest Databricks recommendations for accessing Azure Data Lake with Azure Student Subscription or Corporate Subscriptions.
📅 December 2022: Sections 3, 4, and 5 were updated to reflect recent UI changes in Azure Databricks and additional functionalities introduced by Databricks.
🎉 Welcome Data Engineers! 🎉
Embark on a journey to master one of the most sought-after tools in cloud data engineering, Azure Databricks, with a real-world project focusing on Formula1 racing data. This course is designed to take you through a comprehensive learning experience, leveraging both PySpark and SQL to handle large-scale datasets efficiently.
Course Highlights:
Azure Databricks:
- Understanding the Spark architecture, Data Sources API, and Dataframe API.
- Ingesting data from CSVs, JSON files, and more into a data lake as parquet files/tables.
- Mastering transformations like Filter, Join, Aggregations, GroupBy, and Window functions using PySpark.
- Crafting local and temporary views with Spark SQL.
- Implementing full refresh and incremental load patterns using partitions.
Delta Lake:
- Exploring the emergence of Data Lakehouse architecture and the role of Delta Lake.
- Reading, writing, updating, deleting, and merging to Delta tables using both PySpark and SQL.
- Utilizing history, time travel, and vacuum operations.
- Converting Parquet files to Delta files for robust data management.
Unity Catalog:
- Getting an overview of Data Governance and Unity Catalog.
- Setting up a Unity Catalog Metastore and enabling it in a Databricks workspace.
- Creating and managing objects within the 3-level namespace.
- Configuring access to external data lakes using Unity Catalog's data governance capabilities, such as Data Discovery, Audit, Lineage, and Access Control.
Azure Data Factory:
- Building pipelines that execute Databricks notebooks.
- Designing pipelines with dependencies and robust logic to handle unexpected scenarios like missing files.
- Scheduling pipelines for regular execution using triggers.
- Monitoring the execution of triggers and pipelines to ensure error-free outputs and results.
What You'll Learn:
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Azure Databricks Workspace Setup: Get started with creating an Azure Databricks workspace, setting up clusters, and navigating the interface.
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PySpark and SQL for Data Analysis: Dive into PySpark and Spark SQL to manipulate, transform, and analyze large datasets efficiently.
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Data Lakehouse Implementation: Learn how to implement a data lakehouse solution using Delta Lake for reliability and performance.
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Unity Catalog for Data Governance: Understand the role of Unity Catalog in maintaining data governance within Azure Databricks environments.
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Practical Project Work: Engage with hands-on project work to apply your knowledge, culminating in a comprehensive dashboard to visualize Formula1 racing data.
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Pipeline Design and Automation: Create pipelines in Azure Data Factory to orchestrate the data flow from ingestion to visualization.
Join us on this enriching learning journey and elevate your skills as a data engineer with Azure Databricks & Spark for Data Engineers. Sign up now and transform your data into actionable insights! 🚀✨
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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.