Databricks for Beginners: Data & AI Fundamentals

Module 1: Lakehouse Platform Introduction

• What is Databricks Lakehouse: data lake + warehouse hybrid architecture
• Traditional vs modern data platforms: ETL limitations overcome
• Unity Catalog overview: governance, access control basics
• Free Community Edition setup: account creation and first workspace

Module 2: Workspace Navigation & Clusters

• Databricks workspace interface: notebooks, clusters, SQL warehouses
• Cluster types: All-Purpose vs Jobs clusters comparison
• Starting/stopping clusters: cost management for students
• Cluster configuration: instance types, auto-scaling basics

Module 3: Notebooks & Python Fundamentals

• Multi-language notebooks: Python, SQL, Scala support
• Cell execution: run order, parameters, widgets creation
• Importing datasets: CSV upload, Chennai retail sales examples
• Data exploration: head(), describe(), shape() operations

Module 4: SQL Analytics Mastery

• Databricks SQL editor: query interface and saved queries
• Advanced SQL: CTEs, window functions, pivot/unpivot
• Creating dashboards: charts, filters, scheduled refreshes
• Materialized views: performance optimization techniques

Module 5: Delta Lake Essentials

• ACID transactions on data lakes: reliability guarantees
• Delta table creation: managed vs external tables
• Core commands: MERGE, OPTIMIZE, Z-ORDER clustering
• Time travel: querying historical data versions

Module 6: Apache Spark DataFrames

• Spark DataFrames vs Pandas: distributed processing advantages
• Key transformations: filter, groupBy, join operations
• Spark SQL integration: mixing DataFrame API and SQL
• Caching strategies: when and how to cache datasets

Module 7: Data Cleaning Pipelines

• Handling missing data: drop, fill, imputation strategies
• Outlier detection and removal techniques
• Pandas UDFs: complex Python functions at scale
• Data validation: schema checks and quality assertions

Module 8: Delta Live Tables (DLT)

• Declarative ETL: pipeline-as-code approach
• Bronze-Silver-Gold architecture implementation
• Streaming vs batch processing pipelines
• Data quality expectations and lineage tracking

Module 9: Production Deployment

• Databricks Workflows: job scheduling and orchestration
• CLI basics: automation and CI/CD integration
• Portfolio project: end-to-end Chennai healthcare ETL pipeline
• Associate Data Engineer certification exam blueprint

Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.

Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.

Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.

Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.