AZURE DATABRICKS Course in Chennai

• Azure Databricks Architecture
• Azure Data Lake Storage Gen2 Integration
• Delta Lake on Azure Databricks
• Azure Data Factory and Databricks Orchestration
• Azure Databricks for Streaming
• Machine Learning with Azure ML and Databricks
• Unity Catalog

Develop the skills to create reliable and scalable data pipelines for collecting, transforming, and loading data from multiple sources.

Learn how to use Apache Spark within Azure Databricks to process large datasets efficiently and perform distributed computing tasks.

Understand how to store, organize, and manage structured and unstructured data using Azure Data Lake and Delta Lake technologies.

Gain hands-on experience in cleaning, transforming, optimizing, and preparing data for analytics and reporting.

Build practical skills required for Data Engineer, Cloud Engineer, Big Data Engineer, and Analytics Engineer roles.

Databricks is a powerful cloud-based data intelligence platform that revolutionizes data management by combining data warehousing and data lakes into a single, seamless Data Lakehouse architecture. Created by the original developers of Apache Spark, Databricks empowers data teams to handle massive datasets, build efficient ETL pipelines, execute fast SQL queries, and deploy AI and machine learning models in a collaborative environment.

• Unified data and AI platform – Databricks combines data engineering, data science, and machine learning in a single cloud‑native environment built on Apache Spark.
• Collaborative notebooks – Supports interactive notebooks with Python, SQL, Scala, and R, enabling teams to code, visualize, and share analyses in real time.
• Autoscaling clusters – Dynamically scales compute clusters up or down based on workload, improving performance while reducing cloud cost.
• Delta Lake integration – Provides ACID transactions, schema enforcement, and time‑travel on data lakes, making ETL pipelines reliable and production‑ready.
• Batch and streaming in one engine – Uses Spark Structured Streaming for both batch and real‑time processing on the same codebase, simplifying pipeline design.

• Apache Spark is a fast, open-source tool used to process large amounts of data (big data).
• It helps companies analyze data quickly, even when the data is too big for normal systems.
• Spark is widely used in data engineering, data analytics, and machine learning.
• Very Fast – Processes data in memory (RAM), not just from disk
• Handles Big Data Easily – Can work with huge datasets
• Supports Real-Time Processing – Not just batch data
• Machine Learning Support – Built-in ML libraries

• Delta Lake is an open-source storage layer that brings reliability to data lakes.
• It provides ACID transactions, scalable metadata handling.
• Unifies streaming and batch data processing.
• Delta Lake helps us to easily time travel to previous versions of data.

In Apache Spark, data can be processed in two main ways depending on how and when it is handled:

1. Batch Processing: This method processes large volumes of data at scheduled intervals. Data is collected over time and then analyzed as a whole. It’s commonly used for tasks like generating daily or weekly reports.

2. Streaming Processing: This approach handles data in real-time as it is generated. Instead of waiting, the system processes incoming data continuously. making it ideal for use cases like fraud detection, live dashboards, and real-time analytics.

• Batch is ideal for historical analysis
• while streaming is best for real-time insights.

Delta Lake is a storage layer that provides reliability, performance, and ACID transactions for data lakes.

Yes. Azure Databricks is designed to process massive datasets efficiently using distributed computing.

Common languages include Python, SQL, Scala, and R.

A Lakehouse combines the flexibility of a data lake with the performance and management features of a data warehouse.

Yes. Learners work on real-world cloud data engineering projects and practical labs.

Yes. It integrates with Azure Data Lake Storage, Azure Synapse Analytics, Power BI, Azure Data Factory, and many other services.

Finance, healthcare, retail, manufacturing, telecommunications, e-commerce, and technology sectors.

Data Engineer, Cloud Data Engineer, Big Data Engineer, Analytics Engineer, and Data Platform Engineer.

Yes. Organizations increasingly use cloud data platforms to manage and analyze growing volumes of business data.

Yes. The course supports preparation for Databricks and Azure-related data engineering certification pathways.