Databricks for Data Engineers

---

---

Description:

The Databricks for Data Engineers course focuses on building scalable data pipelines and processing large datasets using Azure Databricks. It covers core concepts like distributed data processing with Apache Spark, data transformation using PySpark, and working with Delta Lake for reliable data storage. Learners gain hands-on experience in designing end-to-end ETL pipelines, optimizing performance, and integrating with cloud services such as Azure Data Factory. This course is ideal for professionals aiming to build real-world data engineering solutions in modern cloud environments.

Modern businesses generate massive volumes of data every day. Our Azure Data Engineering services help organizations collect, process, store, and analyze data efficiently using Microsoft Azure’s powerful cloud ecosystem. Whether you’re looking to build scalable data pipelines, modernize legacy systems, or enable real-time analytics, our Azure experts deliver secure and high-performance solutions tailored to your business needs.

Azure Data Engineering involves designing, building, and managing data solutions on Microsoft Azure. It enables organizations to integrate data from multiple sources, automate data workflows, and create a centralized data platform for advanced analytics and business intelligence.

Using services such as Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, Azure Databricks, and Power BI, businesses can transform raw data into valuable insights that drive smarter decision-making.

Transform your data into actionable business intelligence with our Azure Data Engineering and Databricks Consulting services. We help organizations design, build, and optimize modern data platforms that enable real-time analytics, machine learning, and data-driven decision-making.

---

Syllabus

• Introduction to Databricks & Lakehouse Architecture
• Basics of Apache Spark (RDD, DataFrames, Transformations)
• PySpark for Data Processing & Transformation
• Working with Delta Lake (ACID, Time Travel)
• Data Ingestion (Batch & Streaming)
• Building ETL Pipelines using Azure Data Factory
• Performance Optimization (Partitioning, Caching, Skew Handling)
• Real-time Data Processing & Streaming
• Security & Data Governance (Unity Catalog basics)
• End-to-End Project (Oracle → Databricks → Reporting)

---

Course Highlights

• Hands-on, project-based learning
• Real-world datasets & use cases
• End-to-end pipeline development
• Industry best practices & optimization techniques

 

Outcome

After completing this course, you will be able to:

• Build scalable data pipelines using Azure Databricks
• Process big data efficiently using Apache Spark
• Design real-world ETL workflows and optimize performance
• Work confidently on enterprise-level data engineering projects