Databricks Learning Path

Module 1: Lakehouse Architecture & Databricks Foundations

• Lakehouse paradigm explained: bridging data lakes and warehouses for Indian enterprises
• Databricks workspace tour: notebooks, clusters, repos, and job scheduler
• Community Edition setup: account creation, first notebook, and workspace orientation
• Understanding Unity Catalog: centralized governance for multi-team environments

Module 2: Apache Spark Core Concepts

• Spark distributed architecture: drivers, executors, and partitions demystified
• RDDs vs DataFrames vs Datasets: when to use what
• Spark UI walkthrough: reading DAGs and identifying bottlenecks
• Cluster sizing for Indian SME workloads: cost-aware configuration strategies

Module 3: Spark DataFrames & Transformations

• Reading CSV, JSON, and Parquet: Mumbai e-commerce order data examples
• Core transformations: filter, select, withColumn, groupBy, and agg
• Joins in Spark: broadcast joins vs shuffle joins for large Indian datasets
• Window functions: running totals for Bangalore retail sales analysis

Module 4: Spark SQL & Advanced Analytics

• Spark SQL syntax: seamlessly blending SQL and DataFrame API
• CTEs and subqueries: complex reporting for FMCG supply chain data
• User-Defined Functions (UDFs): applying GST calculation logic at scale
• Optimizing queries with explain plans and partition pruning techniques

Module 5: Delta Lake Essentials

• ACID transactions on data lakes: how Delta Lake solves reliability challenges
• Creating managed and external Delta tables: Chennai logistics warehouse use case
• MERGE command: upsert patterns for daily order reconciliation pipelines
• Time travel: auditing historical data snapshots for compliance requirements

Module 6: Advanced Delta Lake Operations

• OPTIMIZE and Z-ORDER clustering: accelerating queries on large telemetry tables
• Delta table schema evolution: safely adding columns without pipeline downtime
• Streaming ingestion with Delta: real-time payment event processing examples
• Vacuum and retention policies: controlling storage costs on Azure/AWS

Module 7: Data Cleaning & Quality Pipelines

• Handling missing values: drop, fill, and ML-based imputation strategies
• Outlier detection using IQR and Z-score on Indian healthcare datasets
• Pandas UDFs: running complex Python transformations at Spark scale
• Data quality assertions: schema validation and row count checks in pipelines

Module 8: Production Pipelines & Capstone Project

• Databricks Workflows: scheduling multi-task ETL jobs with dependency graphs
• Delta Live Tables introduction: declarative pipeline-as-code approach
• Monitoring and alerting: job failure notifications and SLA tracking
• Capstone: end-to-end Pune manufacturing data engineering pipeline with certification 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.