Delta Lake – Data Lakehouse

Module 1: Introduction to Delta Lake

• Data reliability challenges in data lakes: no ACID, schema drift, and broken pipelines
• Delta Lake overview: open-source storage layer for reliable data lakes
• Delta Lake architecture: transaction log, checkpoint files, and Parquet data files
• Delta Lake on Databricks vs open-source Delta Lake

Module 2: Delta Lake Fundamentals with Spark

• Creating Delta tables from Spark DataFrames and SQL
• Reading Delta tables: batch and streaming reads
• Writing to Delta: overwrite, append, and partition overwrite modes
• Converting existing Parquet tables to Delta format

Module 3: ACID Transactions and DML Operations

• Delta Lake transaction log: how atomicity and isolation work
• UPDATE and DELETE on Delta tables: predicate pushdown optimisation
• MERGE INTO: upsert patterns for SCD Type 1 and Type 2
• Optimistic concurrency control and conflict resolution

Module 4: Schema Enforcement and Evolution

• Schema enforcement: rejecting writes that violate table schema
• Schema evolution: mergeSchema and overwriteSchema options
• Column mapping: rename and drop columns without rewriting data
• Constraints: NOT NULL and CHECK constraint enforcement

Module 5: Delta Lake Time Travel

• Time travel query syntax: VERSION AS OF and TIMESTAMP AS OF
• DESCRIBE HISTORY: reviewing all table changes and operations
• Restoring tables to previous versions with RESTORE TABLE
• Vacuuming: removing old file versions with VACUUM command

Module 6: Delta Lake Performance Optimisation

• OPTIMIZE: compacting small files for better query performance
• Z-ORDER: co-locating related data for multi-column query acceleration
• Auto-optimise and auto-compaction settings in Databricks
• Predictive I/O and data skipping with column statistics

Module 7: Delta Lake Streaming

• Delta Lake as a streaming source: reading changes incrementally
• Delta Lake as a streaming sink: writing with exactly-once semantics
• Change Data Feed (CDF): tracking row-level changes as a stream
• Structured Streaming with Delta Lake: end-to-end streaming pipelines

Module 8: Capstone Project and Assessment

• Delta Lake lakehouse pipeline: ingestion, MERGE, and OPTIMIZE
• Change Data Feed streaming pipeline project
• Time travel and schema evolution exercise with data validation
• Final assessment and course certification

Experience in the Industry Learn from Delta Lake engineers who have built production lakehouse pipelines with ACID transactions, streaming ingestion, and time travel capabilities on Databricks and open-source Delta Lake.

Backgrounds at the Top Our Delta Lake trainers have delivered data lakehouse implementations at technology companies, financial institutions, and data engineering teams where Delta Lake provides the reliability layer for cloud data lakes.

Clear & Effective Teaching Delta Lake architecture, ACID transactions, DML operations, schema enforcement and evolution, time travel, performance optimisation, and streaming are explained with real lakehouse pipeline examples.

Hands-On Learning Focus Students build Delta Lake pipelines, perform MERGE and time travel operations, implement Change Data Feed streaming, optimise with ZORDER, and manage schema evolution through structured hands-on labs.

Up-to-Date Knowledge Trainers keep content current with the latest Delta Lake releases, Delta Lake 3.0 features, UniForm for Iceberg compatibility, and evolving lakehouse storage best practices.