AZURE DATABRICKS Training in Chennai-Edubrights

Q: What is Databricks?

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.

Q: Key Features of Databricks?

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.

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Azure Databricks – Cloud Data Engineering Course in Chennai

Master cloud-native data engineering and big data processing with Edubrights’ Azure Databricks – Cloud Data Engineering training in Chennai. This course is designed for students, freshers, data engineers, ETL developers, cloud professionals, data architects, and working professionals who want to build scalable data solutions on Microsoft Azure.

Gain hands-on experience with Azure Databricks, Apache Spark, Delta Lake, data pipelines, cloud-based ETL workflows, data transformation, performance optimization, and real-world enterprise projects through practical industry use cases.

Key Highlights:

✅ Real-Time Cloud Data Engineering Projects & Industry Use Cases

✅ Live Instructor-Led Training by Azure & Data Engineering Experts

✅ Hands-On Practice with Azure Databricks & Apache Spark

✅ Cloud Data Engineering Workflows on Microsoft Azure

✅ Data Ingestion, Transformation & ETL Pipeline Development

✅ Delta Lake Architecture & Lakehouse Implementation Concepts

✅ Scalable Data Processing with Spark DataFrames & Spark SQL

✅ Workflow Automation & Data Pipeline Orchestration

✅ Performance Optimization, Monitoring & Cost Management Techniques

✅ Integration with Azure Data Services & Enterprise Systems

✅ Data Governance, Security & Cloud Best Practices

✅ Resume Building, Portfolio Development & Mock Interview Preparation

✅ Career Guidance, Placement Assistance & Certification Support

✅ Flexible Online, Classroom & Weekend Training Options

✅ Corporate Training for Cloud, Data Engineering & Analytics Teams

Build practical Azure Databricks expertise, develop scalable cloud data solutions, and become industry-ready for careers in Cloud Data Engineering, Big Data, Analytics, and Azure Technologies.

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₹19999

₹45000

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Course Objectives

What will you learn in Azure Databricks Course

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

Popular Techniques Covered in This Course

Azure Databricks

Unity Catalog

Data Engineering

Artificial Intelligence

Machine Learning

PySpark

Spark Architecture

Python

Delta Lake

Auto Loader

Z-Order Optimization

MLflow

SPARK SQL

Get Hands-on Knowledge about Real-Time Projects

Curriculum

Module 1: Azure Databricks Architecture and Setup

Azure Databricks overview: managed Apache Spark on Microsoft Azure
Azure Databricks workspace setup: resource group, pricing tier, and VNet injection
Integration with Azure services: ADLS Gen2, Azure Synapse, and Azure ML
Navigating Azure Databricks: clusters, notebooks, and workspace structure

Module 2: Azure Data Lake Storage Gen2 Integration

ADLS Gen2 architecture: hierarchical namespace and access control
Mounting ADLS Gen2 in Databricks using service principal and OAuth
Reading and writing data: CSV, JSON, Parquet, and Delta formats
Azure Blob Storage and Azure Key Vault integration

Module 3: Delta Lake on Azure Databricks

Delta Lake fundamentals: ACID transactions and schema enforcement
Creating and managing Delta tables in Azure Databricks
MERGE INTO for upsert operations and CDC (Change Data Capture)
Delta table optimisation and partitioning strategies for Azure workloads

Module 4: Azure Data Factory and Databricks Orchestration

Azure Data Factory pipeline integration with Databricks notebooks
Passing parameters from ADF to Databricks jobs
ADF linked services and Databricks activity configuration
Orchestrating multi-step data pipelines with ADF and Databricks

Module 5: Azure Databricks for Streaming

Structured Streaming on Azure Databricks: concepts and setup
Reading from Azure Event Hubs and Azure IoT Hub
Writing streaming data to Delta Lake with checkpointing
Monitoring streaming jobs and handling failures

Module 6: Machine Learning with Azure ML and Databricks

Azure Machine Learning workspace integration with Databricks
MLflow on Azure Databricks: tracking, models, and registry
Attaching Databricks compute to Azure ML pipelines
Deploying models from Databricks to Azure ML endpoints

Module 7: Azure Databricks Security and Governance

Azure Active Directory integration and SSO for Databricks
Cluster policies and instance pools for cost and governance control
Unity Catalog on Azure Databricks for data governance
Network security: private endpoints, VNet injection, and IP access lists

Module 8: Capstone Project and Assessment

End-to-end Azure data pipeline: ADF ingestion to Databricks Delta Lake
Structured Streaming pipeline from Event Hubs to Delta tables
Unity Catalog governance setup and access control configuration
Final assessment and course certification

Receive Training From Our Skilled and Effective Trainers

Experience in the Industry Gain knowledge from experts with practical Amazon CloudFront project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture and cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.

Get Certified with EduBrights

EduBrights provides an Azure Databricks certification program designed for Data Engineers, Data Analysts, and AI/ML professionals. This certification helps you demonstrate your expertise in unified analytics, Delta Lake management, Spark processing, and performance tuning. Through practical, hands-on training, you will learn to build scalable Lakehouse pipelines using PySpark, Unity Catalog, and Structured Streaming, preparing you for real-world cloud data engineering roles in modern enterprises.

Course FAQs

What is Databricks?

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.

Key Features of Databricks?

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.

What is Apache Spark?

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

What is Delta Lake, and how does it improve data reliability?

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.

What is the difference between batch and streaming data processing in Spark?

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

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.
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.