Azure Databricks Data Engineering: Build a Lakehouse

Master PySpark, Delta Lake & Native Dashboards by building a Real Estate Market Tracker from scratch in Azure Databricks
⏱️ Length: 1.1 total hours
👥 78 students
🔄 February 2026 update

Add-On Information:

Get Instant Notification of New Courses on our Telegram channel.

Note➛ Make sure your 𝐔𝐝𝐞𝐦𝐲 cart has only this course you're going to enroll it now, Remove all other courses from the 𝐔𝐝𝐞𝐦𝐲 cart before Enrolling!

Course Overview
- This course offers a streamlined introduction to data engineering with Azure Databricks, focusing on the modern Lakehouse architecture. You will leverage industry-leading tools like PySpark and Delta Lake to build robust and scalable data solutions efficiently.
- The core of this learning experience is a highly practical, hands-on project: constructing a Real Estate Market Tracker from scratch. This project comprehensively guides you through the entire data engineering lifecycle within the integrated Azure Databricks environment.
- You will learn to expertly orchestrate intricate data flows, progressing from raw data ingestion to sophisticated data transformations, ultimately culminating in the presentation of actionable insights via native Databricks dashboards. The emphasis is firmly on practical application and immediate skill acquisition.
- Designed for maximum efficiency and impact, this course provides a concentrated learning experience, ensuring that every moment is dedicated to building core competencies in cloud-native data processing and analytical techniques crucial for modern data roles.
- By the course’s completion, you will possess a functional understanding of how to integrate, clean, transform, and analyze disparate real estate data points, preparing them effectively for analytical consumption and dynamic visualization. This lays a strong foundation for tackling future, more complex data initiatives.
Requirements / Prerequisites
- A fundamental understanding of cloud concepts, particularly those related to the Microsoft Azure ecosystem, will be beneficial for grasping the context of the services utilized.
- Basic familiarity with the Python programming language is expected, as PySpark, a central component of this course, is fundamentally built upon Python syntax and constructs.
- Prior exposure to SQL queries will aid significantly in understanding data manipulation concepts, even as the course primarily focuses on PySpark DataFrames.
- An active Azure subscription is essential to provision the necessary Databricks workspace and associated cloud resources. A free trial account should be sufficient for the course’s scope.
- No prior experience with Azure Databricks specifically is required; the course is meticulously designed to introduce users to the platform from a foundational level.
- A stable internet connection and a modern web browser are necessary to seamlessly access the Azure portal and interact with the Databricks workspace.
Skills Covered / Tools Used
- PySpark Fundamentals: Master the basics of PySpark DataFrames, including their creation, essential transformations (such as `select`, `where`, `groupBy`, `join`), and core actions (`show`, `count`) for efficient, large-scale data processing.
- Delta Lake Mastery: Gain expertise in Delta Lake, understanding its crucial ACID properties, robust schema enforcement, powerful time travel capabilities, and how it enables reliable and performant data lakes and lakehouses.
- Azure Databricks Workspace Navigation: Learn to efficiently utilize the Databricks workspace environment, including managing notebooks, configuring clusters, and understanding job orchestration for automated data pipelines.
- Data Ingestion Techniques: Practice reading and loading various data formats (e.g., CSV, JSON, Parquet) into Delta Lake tables within Databricks, preparing raw data for subsequent processing.
- Data Transformation & Cleansing: Develop advanced skills in cleaning, enriching, and aggregating raw real estate data using PySpark to create a structured, high-quality, and analysis-ready dataset.
- Lakehouse Implementation: Understand the practical steps involved in building a Lakehouse architecture, effectively transitioning data from raw ingestion layers to refined, curated layers suitable for business intelligence.
- Native Databricks Dashboards: Learn to create compelling and interactive dashboards directly within Databricks, enabling the visualization of real estate market trends and insights without the need for external tools.
- SQL Analytics within Databricks: Explore how to perform powerful SQL queries directly on your Delta Lake tables, leveraging the robust Databricks SQL capabilities for deep analytical purposes.
- Cloud Storage Integration: Interact seamlessly with Azure Data Lake Storage Gen2 (ADLS Gen2) for storing both raw and processed data, understanding file system mounting and secure access patterns within Databricks.
- Basic Data Modeling for Analytics: Acquire foundational knowledge in structuring and organizing data for optimal analytical queries, focusing on performance and ease of understanding within the Lakehouse context.
Benefits / Outcomes
- You will be equipped with the essential skills to design and implement end-to-end data pipelines proficiently within the Azure Databricks environment, covering everything from initial data ingestion to advanced transformations.
- Achieve significant proficiency in utilizing PySpark for scalable data processing, empowering you to efficiently manipulate, analyze, and extract value from large and complex datasets.
- Develop specialized expertise in leveraging Delta Lake for building reliable and high-performance data lakes and lakehouses, ensuring data quality, consistency, and efficient versioning across your data assets.
- Gain the practical ability to create impactful and interactive native dashboards directly within Databricks, translating complex data into clear, actionable business insights for various stakeholders.
- Acquire invaluable practical experience by successfully building a real-world Real Estate Market Tracker, solidifying your understanding of core data engineering principles through direct, hands-on application.
- Understand and practically apply the principles of the cutting-edge Lakehouse architecture, a modern paradigm that seamlessly combines the flexibility of data lakes with the reliability and structure of data warehouses.
- Lay a robust foundation for pursuing more advanced data engineering roles, specialized certifications, or complex data projects within the expansive Azure and Databricks ecosystems.
- Boost your confidence and marketability by demonstrating competence in working with leading cloud-native data platforms, making you a more versatile and sought-after professional in the dynamic data landscape.
- Be able to independently ingest, transform, and visualize data, effectively turning raw information into strategic assets that drive informed decision-making within various business contexts.
- Enhance your problem-solving skills by tackling real-world data challenges associated with data quality, integration, and performance within a structured, project-based learning environment.
PROS
- Project-Based Learning Focus: The course is entirely centered around building a practical Real Estate Market Tracker, ensuring hands-on engagement and immediate application of learned concepts.
- Concise and Impactful: Designed to deliver maximum learning value in a minimal timeframe, making it ideal for busy professionals seeking to quickly acquire new, relevant skills.
- Industry-Relevant Technologies: Covers highly demanded skills like PySpark, Delta Lake, and Azure Databricks, which are critical in today’s data-driven industries.
- End-to-End Workflow Exposure: Guides you through the complete data engineering lifecycle, from raw data ingestion to transformation and final data visualization.
- Modern Lakehouse Architecture: Introduces and practically implements the cutting-edge Lakehouse paradigm, preparing you for the future of data management and analytics.
- Managed Cloud Environment: Leverages Azure Databricks, providing a fully managed platform that minimizes setup complexities and allows you to focus purely on coding and data engineering.
- Solid Foundational Skills: Establishes a strong base in cloud data engineering, opening doors to more advanced topics and roles in the vast field of data science and analytics.
CONS
- Limited Depth Due to Short Duration: Given its 1.1-hour length, this course necessarily offers an introductory overview. Advanced optimization strategies, robust error handling, CI/CD pipelines, security best practices, or large-scale production deployments are beyond its scope and would require further, more extensive study.