Azure Databricks Development Training
Build Scalable Data Engineering Solutions with Azure Databricks
Build practical, job-ready skills in Azure Databricks development, data engineering and scalable Lakehouse solutions. Learn Apache Spark, PySpark, Databricks SQL, Delta Lake, streaming, pipelines, orchestration, Azure integration, performance tuning and production deployment through hands-on exercises.
Enquire About TrainingCourse Overview
Azure Databricks Development Training is designed to take learners from the fundamentals of Apache Spark and Azure Databricks to advanced Lakehouse data engineering and production-oriented development.
You will work with PySpark, Databricks SQL, Delta Lake, Structured Streaming, Lakeflow, Azure Data Lake Storage, Git, CI/CD and performance optimization concepts. The learning path focuses on the practical responsibilities of a Databricks developer and data engineer.
Course Highlights
Gain practical Azure Databricks development expertise through a comprehensive curriculum covering Apache Spark, PySpark, Delta Lake, data engineering, Lakehouse architecture, Azure integration, performance optimization, and real-world project implementation.
Apache Spark & PySpark
Learn Spark architecture, DataFrames, transformations, actions, joins, partitions, and scalable PySpark development.
Delta Lake & Lakehouse
Work with Delta tables, ACID transactions, schema evolution, time travel, data versioning, and modern Lakehouse architecture.
Data Pipeline Development
Build batch, incremental, and streaming data pipelines using practical Databricks data engineering patterns.
Databricks SQL
Develop analytical queries, tables, views, SQL workloads, and data warehouse-style solutions using Databricks SQL.
Azure Integration
Integrate Azure Databricks with Azure Data Lake Storage and external data sources using secure and scalable access patterns.
Performance Optimization
Learn Spark optimization, partitioning, caching, query tuning, cluster optimization, and performance best practices.
Git & CI/CD
Understand version control, collaborative development, deployment workflows, Git integration, and CI/CD concepts for Databricks projects.
Trainer Experience
Learn from an industry professional with 19+ years of experience in database, data engineering, analytics, and modern data technologies.
Why Choose Our Azure Databricks Development Training?
The training follows a structured path from fundamentals to advanced development, with emphasis on practical data engineering tasks and production-oriented Databricks solutions.
Hands-On Development
Practice PySpark, SQL, Delta Lake and Lakehouse development through practical exercises.
Beginner to Advanced
Progress from Spark and Databricks fundamentals to advanced data engineering patterns.
Real-World Data Engineering
Understand batch, incremental, CDC, streaming and orchestration scenarios used in modern data platforms.
Performance Engineering
Learn how partitions, joins, shuffles, file layout and compute choices affect Spark workloads.
Azure Data Platform Integration
Work with Azure Data Lake Storage and external data sources as part of Databricks solutions.
Production Development Practices
Learn Git, CI/CD, deployment workflows and reusable engineering practices for Databricks projects.
Who Can Join?
Freshers & Graduates
Students and graduates who want to build a career in cloud data engineering and Databricks.
SQL & Database Professionals
SQL developers, database professionals and developers looking to move into modern data platforms.
ETL & Data Engineers
ETL developers and data engineers who want to add Spark, PySpark and Lakehouse skills.
Analytics Professionals
Analytics and BI professionals who want to understand scalable data processing and Lakehouse architecture.
Azure Professionals
Azure professionals who want to expand their skills into Azure Databricks and data engineering.
Python Developers
Python developers interested in distributed data processing and PySpark development.
Career Opportunities
Azure Databricks Developer
Develop notebooks, transformations, Delta Lake workloads and data pipelines on Azure Databricks.
Data Engineer
Build scalable batch, incremental and streaming data pipelines for enterprise data platforms.
Lakehouse Developer
Design and implement Bronze, Silver and Gold data layers using modern Lakehouse patterns.
PySpark Developer
Develop distributed data-processing applications using Python and Apache Spark.
Data Platform Engineer
Work on data platform integration, automation, optimization and production engineering activities.
Cloud Data Engineer
Develop cloud-based data solutions using Azure services and modern Lakehouse technologies.
What You Will Gain
Understand Spark execution, DataFrames, transformations, joins, partitions and scalable processing.
Build Bronze, Silver and Gold data layers using Delta Lake and practical data engineering patterns.
Create batch, incremental and streaming pipelines with orchestration and data quality practices.
Work with Azure Data Lake Storage, external data sources and secure data access patterns.
Analyze Spark execution and improve partitions, joins, shuffles, file layout and compute usage.
Apply Git, CI/CD, deployment and production-oriented development practices to Databricks workloads.
Trusted Azure Databricks Development Training
Learn from an experienced training institute with a practical, technology-focused approach to professional training.
100+
Training Programs
50+
Practical Learning Activities
20+
Technology & Data Platform Areas
19+
Years Industry Experience
STUDENT SUCCESS STORIES
What Our Students Say
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Upcoming Free Demo Class
Course: Azure Databricks Development Training
Date: 12 October 2026
Time: 08:00 AM - 09:00 AM IST
Class: 1 Hour
Schedule: Weekdays-Online
Duration: 12 Weeks
Platform: Zoom Meeting
Fee: Contact us for Fee Details
Meeting Link: Contact us for zoom meeting link
Live Databricks Training Sessions
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Course Syllabus
Our Azure Databricks Development syllabus progresses from Spark and Databricks fundamentals to PySpark development, Delta Lake, Lakehouse engineering, streaming, orchestration, Azure integration, performance optimization and production deployment.
01. Azure & Databricks Fundamentals
FOUNDATION
- Introduction to cloud data platforms
- Azure data ecosystem overview
- What is Azure Databricks?
- Databricks architecture and major components
- Workspace, account and regional concepts
- Lakehouse architecture and medallion architecture
- Data engineering, analytics and AI workloads
- Databricks UI navigation
- Workspace folders and collaborative development
- Introduction to notebooks and Repos
02. Apache Spark Fundamentals
SPARK
- Spark architecture and execution model
- Driver, executors and tasks
- Jobs, stages and tasks
- Transformations and actions
- Lazy evaluation
- Partitions and parallel processing
- DataFrames and schemas
- Reading and writing structured data
- Shuffles and data movement
- Introduction to Spark UI
03. PySpark Development
DEVELOPMENT
- Python for Databricks development
- SparkSession and DataFrame API
- Schema definition and data types
- Select, filter and column expressions
- withColumn and conditional transformations
- Aggregations and groupBy
- Joins and join strategies
- Window functions
- Sorting, repartitioning and coalescing
- User-defined functions and when to avoid them
- Reusable PySpark functions
- Data quality validation with PySpark
04. Databricks SQL & Data Warehousing
SQL
- Databricks SQL fundamentals
- SQL warehouses
- Tables, views and schemas
- Advanced SQL querying
- CTEs and subqueries
- Window and analytical functions
- Temporary and session objects
- Materialized views
- SQL dashboards and visualization concepts
- BI connectivity concepts
05. Delta Lake Fundamentals
STORAGE
- Why Delta Lake?
- Delta table architecture
- ACID transactions
- Transaction log
- Schema enforcement
- Schema evolution
- Time travel
- MERGE, UPDATE and DELETE
- OPTIMIZE and data layout
- VACUUM and retention concepts
- Partitioning and file management
- Managed and external tables
06. Lakehouse Data Engineering
DATA ENGINEERING
- Bronze, Silver and Gold layers
- Batch ingestion patterns
- Incremental data processing
- Full load versus incremental load
- CDC concepts
- Data cleansing and standardization
- Dimensional modeling concepts
- Fact and dimension processing
- SCD Type 1 and Type 2 implementation concepts
- Reusable pipeline design
07. Structured Streaming & Real-Time Data
STREAMING
- Batch versus streaming architecture
- Structured Streaming fundamentals
- Streaming DataFrames
- Streaming sources and sinks
- Checkpointing
- Output modes
- Watermarking
- Late-arriving data
- Streaming joins
- Fault tolerance and recovery
- Streaming with Delta Lake
08. Lakeflow Pipelines & Data Pipeline Development
PIPELINES
- Modern Databricks pipeline architecture
- Lakeflow pipelines concepts
- Streaming tables
- Materialized views
- Declarative pipeline development
- Data quality expectations
- Pipeline configuration
- Pipeline monitoring
- Pipeline troubleshooting
- Publishing governed data through Unity Catalog
09. Lakeflow Jobs & Workflow Automation
ORCHESTRATION
- Jobs and tasks
- Notebook tasks
- SQL and pipeline tasks
- Job parameters
- Task dependencies
- Schedules and triggers
- Retries and failure handling
- Control flow and branching
- For-each task patterns
- Job monitoring and run history
- Production workflow design
10. Azure Data Lake Storage & External Data Sources
AZURE INTEGRATION
- Azure Data Lake Storage Gen2 fundamentals
- Containers and hierarchical namespaces
- Databricks and ADLS architecture
- Unity Catalog external locations
- Storage credentials
- Managed identities
- Accessing Azure storage securely
- JDBC connectivity to relational databases
- Lakehouse Federation concepts
- Data ingestion from Azure and external systems
11. Spark & Databricks Performance Tuning
PERFORMANCE
- Understanding Spark execution plans
- Reading Spark UI
- Partitioning strategies
- Shuffle optimization
- Broadcast joins
- Join strategy selection
- Data skew and mitigation
- Caching considerations
- File size and small-file problems
- Delta optimization
- Photon concepts
- Compute sizing and cost-aware optimization
12. Git, CI/CD & Deployment Automation
DEVOPS
- Git fundamentals for Databricks teams
- Repos and source control
- Branching strategy
- Development versus production workflow
- Databricks CLI concepts
- REST API concepts
- Databricks SDK concepts
- Declarative Automation Bundles
- CI/CD pipeline architecture
- Environment-specific configuration
- Automated deployment concepts
13. Advanced Lakehouse Engineering
ADVANCED DATA ENGINEERING
- Advanced incremental processing
- CDC architecture and implementation patterns
- Medallion architecture at enterprise scale
- Data quality frameworks
- Reusable ingestion frameworks
- Metadata-driven pipelines
- Schema evolution strategies
- Large-scale Delta table management
- Lakehouse design patterns
- Production pipeline reliability
Sample Azure Databricks Development Training Videos
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Frequently Asked Questions
What is Azure Databricks?
Azure Databricks is a cloud-based data and analytics platform built on Apache Spark and integrated with Microsoft Azure services.
Who is this Azure Databricks Development Training for?
The course is suitable for beginners as well as developers, SQL professionals, ETL developers, data engineers, Python developers and Azure professionals who want to develop Databricks skills.
Do I need prior Databricks experience?
No. The curriculum starts with Azure Databricks and Apache Spark fundamentals and progresses toward advanced development topics.
Will I learn PySpark?
Yes. PySpark is a major part of the development curriculum, including DataFrames, transformations, joins, window functions, reusable functions and data-quality practices.
Does the course cover Delta Lake?
Yes. The course covers Delta tables, ACID transactions, schema enforcement, schema evolution, time travel, MERGE, optimization and table management concepts.
Does the training include streaming?
Yes. Structured Streaming, checkpoints, output modes, watermarking, late-arriving data, streaming joins and Delta Lake streaming scenarios are included.
Will Azure Data Lake Storage be covered?
Yes. The curriculum includes Azure Data Lake Storage Gen2, external locations, storage credentials, managed identities and secure access patterns.
Is the training available online?
Yes. The course is designed for online and offline training as indicated in the course information.
Ready to Learn Azure Databricks Development?
Learn Azure Databricks step by step, practice real-world data engineering scenarios and build scalable Lakehouse solutions.
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