Databricks Advanced Data Engineering -- Production Pipelines

Module 1: Production Engineering Mindset

• Production vs development: reliability, SLAs, and on-call responsibilities
• Databricks architecture deep-dive: control plane, data plane, and network topology
• Cost optimisation levers: spot instances, autoscaling, and photon engine selection
• Indian cloud region considerations: latency, data residency, and DR planning

Module 2: Delta Live Tables -- Advanced Patterns

• Medallion architecture with DLT: Bronze, Silver, Gold pipeline implementation
• Streaming vs batch DLT pipelines: hybrid processing for payment event streams
• Data quality expectations: quarantine patterns for rejected records
• DLT pipeline monitoring: tracking data freshness and expectation pass rates

Module 3: Performance Tuning & Optimisation

• Spark performance deep-dive: skew, spill, and shuffle optimization strategies
• Photon engine: when it accelerates queries and when it doesn't
• Adaptive Query Execution (AQE): automatic plan optimisation for complex joins
• Benchmarking pipelines: measuring throughput improvements with Indian logistics data

Module 4: Streaming Data Engineering

• Structured Streaming internals: checkpoints, watermarks, and state management
• Kafka integration: consuming real-time stock market feeds from NSE/BSE
• Stateful aggregations: sessionization of user behaviour on Indian OTT platforms
• Exactly-once semantics: guaranteeing no duplicates in payment processing pipelines

Module 5: Orchestration & Workflow Engineering

• Databricks Workflows advanced features: conditional tasks and repair runs
• Integrating with Apache Airflow: triggering Databricks jobs from external DAGs
• Event-driven pipelines: S3/ADLS file arrival triggers and webhook-based scheduling
• SLA monitoring: alerting when ETL pipelines breach agreed completion windows

Module 6: CI/CD for Data Pipelines

• Git integration: Databricks Repos and branch-based development workflows
• Testing strategies: unit tests for transformations using pytest and nutter
• GitHub Actions pipelines: automated deployment of notebooks and DLT definitions
• Environment promotion: dev -> staging -> prod with environment-specific configs

Module 7: Observability & Incident Management

• Databricks system tables: querying cluster usage and job run history
• Custom metrics: emitting pipeline KPIs to Grafana for real-time dashboards
• Alerting playbooks: PagerDuty integration for critical pipeline failures
• Root cause analysis: post-incident review process for data engineering teams

Module 8: Capstone -- Enterprise Pipeline Architecture

• Architecture design session: multi-zone data platform for Indian insurance company
• Implementation sprint: building the complete pipeline with DLT, Workflows, and Unity Catalog
• Code review workshop: peer review of production-grade pipeline implementations
• Professional Data Engineer exam prep: advanced scenario-based question analysis

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.