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About This Course
This is India’s most comprehensive Databricks Data Engineering Training — designed for professionals who want to build real-world data pipelines, master the Lakehouse architecture and crack the Databricks Certified Data Engineer Associate exam.
You will work hands-on with real Databricks workspaces, build production-grade pipelines using PySpark, Delta Lake, Auto Loader and Delta Live Tables — guided by an instructor with 10+ years of industry experience at top MNCs.
What You Will Learn
- ✅ Build end-to-end data pipelines using PySpark and Delta Lake
- ✅ Master Medallion Architecture — Bronze, Silver and Gold layers
- ✅ Implement SCD Type 1 & Type 2 with Delta Merge
- ✅ Build real-time streaming pipelines using Kafka + Structured Streaming
- ✅ Work with Auto Loader for incremental file ingestion from S3 / ADLS
- ✅ Create Delta Live Tables (DLT) pipelines with data quality expectations
- ✅ Master Unity Catalog — data governance, lineage and access control
- ✅ Optimise Spark jobs — AQE, broadcast joins, skew handling, Z-ordering
- ✅ Integrate Databricks with AWS (S3, IAM) and Azure (ADLS, ADF)
- ✅ Deploy CI/CD pipelines using Databricks Asset Bundles + GitHub Actions
- ✅ Clear the Databricks Certified Data Engineer Associate exam
- ✅ Crack data engineering interviews at top MNCs
Who Is This Course For?
- 👨💻 Software developers wanting to transition into Data Engineering
- 📊 Data Analysts looking to move into Data Engineering roles
- ☁️ Cloud professionals wanting to specialise in Databricks and Spark
- 🎓 Fresh graduates targeting Data Engineer roles at MNCs
- 🔄 ETL / SQL developers upgrading to modern data stack
- 🏆 Working professionals preparing for Databricks certification
Requirements / Prerequisites
- Basic knowledge of Python (variables, loops, functions)
- Basic SQL knowledge (SELECT, JOIN, GROUP BY)
- No prior Spark or Databricks experience needed
- A laptop with internet connection — all labs run on cloud
Course Highlights
- 🕐 80+ Hours of live instructor-led training
- 🛠️ 100% Hands-On — real Databricks workspace practice
- 📁 5 Real-World Projects you can add to your resume
- 📝 20 Modules covering everything from basics to advanced
- 🏆 Certification Prep — Databricks DE Associate exam ready
- 🎥 Lifetime Access to all recorded sessions
- 💬 WhatsApp Group Support + weekly doubt-clearing sessions
- 📄 Resume Review + mock interview preparation
- 🤝 Placement Support with 200+ hiring partners
- 📅 Flexible Batches — weekday, weekend and fast-track
Tools & Technologies Covered
- ⚡ Apache Spark & PySpark
- 🔥 Databricks Lakehouse Platform
- 🏔️ Delta Lake — ACID, Time Travel, Z-Ordering
- 📥 Auto Loader & Delta Live Tables (DLT)
- 🔄 Apache Kafka & Structured Streaming
- ☁️ AWS — S3, IAM, Glue
- 🔷 Azure — ADLS Gen2, ADF, Azure Databricks
- 🗄️ Databricks SQL — Warehouses, Dashboards
- 🔐 Unity Catalog & Data Governance
- 🚀 CI/CD — Git, GitHub Actions, Databricks Asset Bundles
Career Opportunities After This Course
- 💼 Data Engineer — ₹8 LPA to ₹25 LPA
- 💼 Senior Data Engineer — ₹15 LPA to ₹40 LPA
- 💼 Databricks Specialist — ₹20 LPA to ₹50 LPA
- 💼 Cloud Data Engineer (AWS / Azure) — ₹12 LPA to ₹35 LPA
- 💼 Big Data Engineer — ₹10 LPA to ₹30 LPA
Our alumni work at Amazon, Microsoft, TCS, Infosys, Wipro, Deloitte, Accenture, Capgemini and 200+ other companies.
Why Learn From Us?
- 🏅 India’s #1 rated Databricks training institute
- 👨🏫 Trainer has 10+ years experience working with Databricks at top MNCs
- 🏢 5,000+ students trained — 95% placement rate
- 📚 Course material updated every quarter with latest Databricks features
- 🆓 Free demo class — experience the training before you enroll
Course Features
- Lectures 164
- Quiz 0
- Duration 8 weeks
- Skill level All levels
- Language English
- Students 990
- Assessments Yes
- 20 Sections
- 164 Lessons
- 8 Weeks
Expand all sectionsCollapse all sections
- Module 1: Data Engineering Fundamentals9
- 1.1What is Data Engineering & Role of a Data Engineer
- 1.2OLTP vs OLAP Systems
- 1.3Data Warehouse vs Data Lake vs Lakehouse
- 1.4Batch Processing vs Stream Processing
- 1.5Modern Data Engineering Architecture
- 1.6Data Engineering Lifecycle (Ingestion → Storage → Processing → Serving)
- 1.7Medallion Architecture (Bronze → Silver → Gold)
- 1.8Data Modeling Basics — Star Schema & Snowflake Schema
- 1.9File Formats — CSV, JSON, Parquet, Avro, ORC, Delta, Iceberg
- Module 2: Databricks Platform Fundamentals10
- 2.1Databricks Workspace Overview
- 2.2Databricks Architecture — Control Plane vs Data Plane
- 2.3Workspace Components — Notebooks, Clusters, Jobs, Repos
- 2.4Creating and Managing Clusters
- 2.5Cluster Types — All-Purpose vs Job Clusters
- 2.6Databricks Runtime — Standard & ML
- 2.7Databricks Lakehouse Platform Overview
- 2.8Cluster Policies & Auto-Termination
- 2.9Databricks Utilities (dbutils) — File, Secrets, Widgets
- 2.10Notebook Collaboration & Magic Commands (%sql, %md, %sh)
- Module 3: PySpark Fundamentals8
- 3.1Introduction to Apache Spark
- 3.2Spark Architecture — Driver, Executors, Cluster Manager
- 3.3SparkSession vs SparkContext
- 3.4RDD vs DataFrame vs Dataset
- 3.5Lazy Evaluation & DAG (Directed Acyclic Graph)
- 3.6Spark Execution Plan — Logical vs Physical Plan
- 3.7Transformations vs Actions — Deep Dive
- 3.8Reading Data from DBFS (Databricks File System)
- Module 4: PySpark DataFrame Operations10
- 4.1DataFrame Transformations — select, filter, withColumn, drop
- 4.2Diffferent ways to create DataFrames
- 4.3Reading Data — CSV, JSON, Parquet, Delta
- 4.4Writing Data — Overwrite, Append, Partitioned Writes
- 4.5RDD Transformation & actions
- 4.6Schema Definition — StructType & StructField
- 4.7InferSchema vs Defined Schema — Best Practices
- 4.8Working with Nested complex JSON data & Array Columns
- 4.9Spark Date & Window functions
- 4.10Important functions like explode(), flatten(), struct(), udf
- Module 5: PySpark Data Transformations7
- Module 6: Joins and Window Functions7
- 6.1Types of Joins — Inner, Left, Right, Full
- 6.2Window Functions — row_number, rank, lead, lag
- 6.3Cross Join & Self Join Use Cases
- 6.4Optimize joins: broadcast, sortmerge join
- 6.5Handling Duplicate Records After Joins
- 6.6dense_rank(), ntile(), percent_rank()
- 6.7Running Totals & Moving Averages with Window Functions
- Module 7: Delta Lake Fundamentals9
- 7.1What is Delta Lake & Why It Matters
- 7.2Delta Operations — Update, Delete, Merge (Upsert)
- 7.3Delta Lake Architecture & Transaction Log
- 7.4ACID Transactions (Update, delete)
- 7.5Delta Table Creation & Convert Parquet to Delta
- 7.6Delta Lake vs Apache Iceberg vs parquet— Comparison
- 7.7Managed vs External Delta Tables pros, cons
- 7.8Schema Enforcement vs Schema Evolution
- 7.9Writing Idempotent Pipelines with Delta
- Module 8: Advanced Delta Lake9
- 8.1Time Travel — Query Historical Versions
- 8.2Vacuum — Removing Old Files
- 8.3Delta Table Optimization — OPTIMIZE & Z-Ordering
- 8.4Change Data Feed (CDF) vs Change Data Capture (CDC)
- 8.5Liquid Clustering (Latest Databricks Feature)
- 8.6Deletion Vectors for Faster Deletes
- 8.7Row-Level Concurrency
- 8.8Medallion architecture with Delta (Bronze → Silver → Gold)
- 8.9Auto Loader with Delta — Incremental File Ingestion
- Module 9: Data Engineering Pipelines9
- 9.1Batch Data Pipelines
- 9.2Incremental Data Processing
- 9.3SCD Type 1 & Type 2 Implementation
- 9.4ETL vs ELT
- 9.5Auto Loader — cloudFiles() for S3/ADLS
- 9.6Watermarking for Late-Arriving Data
- 9.7Idempotent & Fault-Tolerant Pipeline Design
- 9.8Full Load vs Incremental Load Strategies
- 9.9Data Quality Checks in Pipelines
- Module 10: Delta Live Tables (DLT)7
- Module 11: Databricks SQL8
- 11.1SQL Warehouses — Serverless vs Classic
- 11.2Running SQL Queries
- 11.3Creating Views & Materialized Views
- 11.4Query Optimization
- 11.5Databricks SQL Dashboards & Visualisations
- 11.6Query History & Query Profile Analysis
- 11.7Databricks SQL Alerts
- 11.8Connecting BI Tools — Power BI, Tableau to Databricks SQL
- Module 12: Spark Performance Optimization11
- 12.1Partitioning Strategy
- 12.2Repartition vs Coalesce – Where to use?
- 12.3Broadcast Joins – Different usecases
- 12.4Caching and Persistence
- 12.5Memory Management
- 12.6Executor memory, Driver Memory , cores -properly use
- 12.7Skew Handling — Salting Technique
- 12.8Reading Spark UI — Jobs, Stages, Tasks
- 12.9Spill to Disk — Causes & Fixes
- 12.10File Size Optimization — Small File Problem
- 12.11Predicate Pushdown & Column Pruning
- Module 13: Structured Streaming9
- 13.1Introduction to Streaming Concepts
- 13.2Batch vs Streaming Architecture difference
- 13.3Reading Streaming Data — with Kafka, Files
- 13.4Writing Streaming Data — Delta, Kafka
- 13.5Trigger Modes — Once, Fixed Interval, Continuous
- 13.6Stateful vs Stateless Streaming
- 13.7Checkpointing & Fault Recovery
- 13.8Streaming with Auto Loader
- 13.9Practical: Real-Time Order Processing Pipeline
- Module 14: Databricks Workflow Orchestration9
- 14.1Databricks Jobs & Scheduling
- 14.2Multi-Task Workflows
- 14.3Error Handling & Retries
- 14.4Job Clusters vs All-Purpose Clusters in Jobs
- 14.5Parameterised Jobs with Widgets
- 14.6Task Dependencies — Sequential & Parallel
- 14.7Email & Webhook Notifications on Job Failure
- 14.8Monitoring Jobs via Job Run History
- 14.9Integrating with Apache Airflow (Overview)
- Module 15: Unity Catalog & Data Governance9
- 15.1What is Unity Catalog
- 15.2Data Governance fundamental Concepts
- 15.3Access Control — Table Level & Column Level
- 15.4Three-Level Namespace — Catalog → Schema → Table
- 15.5Row-Level Security with Row Filters
- 15.6Data Lineage Tracking
- 15.7Tagging & Data Classification
- 15.8Audit Logs in Unity Catalog
- 15.9External Locations & Storage Credentials
- Module 16: Databricks with Cloud Platforms7
- 16.1Databricks on AWS — S3 Integration, IAM Roles
- 16.2Databricks on Azure — ADLS Integration, Service Principals
- 16.3Databricks on GCP — Overview
- 16.4AWS Glue vs Databricks — When to Use What
- 16.5Azure Data Factory + Databricks Integration
- 16.6Secrets Management — AWS Secrets Manager / Azure Key Vault
- 16.7Mounting Cloud Storage in Databricks
- Module 17: Real-Time Data Engineering7
- 17.1Kafka Integration
- 17.2Kafka Architecture — Topics, Partitions, Consumer Groups
- 17.3Kafka Vs Confluence kafka
- 17.4Producing & Consuming Messages from Databricks
- 17.5Exactly-Once Semantics with Kafka + Delta
- 17.6Practical: Real-Time Clickstream Analytics
- 17.7Practical: cdata Rest api, Nifi connect Databricks
- Module 18: CI/CD and Production Deployment7
- Module 19: End-to-End Data Engineering Projects5
- Module 20: Databricks Certification & Interview Preparation7
- 20.1Practice Questions — Full Mock Tests
- 20.2Databricks Certified Data Engineer Associate — Exam Overview
- 20.3Interview tips & Resume Preparation
- 20.4Top 50 Databricks Interview Questions & Answers
- 20.5Generative AI (github copilot) for code generation
- 20.6Claude AI for code Generation
- 20.7Linkedin tips to find job & get a job
