Modern Data Engineering with Databricks

Introduction to Modern Data Engineering

• Evolution of data platforms
• Traditional vs modern data architectures
• Lakehouse architecture and where Databricks fits
• Traditional architectures vs Databricks Lakehouse
• Role of a Data Engineer in a Lakehouse ecosystem
• Common enterprise data challenges and how Databricks addresses them

Cloud Fundamentals for Databricks

• Cloud service models (IaaS, PaaS, SaaS)
• Databricks on AWS, Azure, and GCP (high-level overview)
• Separation of compute and storage
• Databricks clusters:
  • Interactive vs job clusters
  • Autoscaling and auto-termination
• Batch vs streaming processing with Apache Spark
• Cost and scalability considerations in Databricks

Data Storage & Lakehouse Concepts

• Data lakes, data warehouses, and hybrid architectures
• Databricks Lakehouse architecture
• Table formats and metadata management
• Delta Lake fundamentals:
  • ACID transactions
  • Time travel
  • Schema enforcement and evolution
• Structured, semi-structured, and unstructured data in Spark
• Managed vs external tables
• Introduction to Unity Catalog and centralized metadata

Data Engineering Development Basics

• Working with notebooks and code repositories
• Databricks notebooks (SQL, Python, Scala)
• Notebook best practices and parameterization
• Databricks Repos and Git integration
• Development, test, and production environments
• Data engineering lifecycle on Databricks

Data Ingestion Patterns

• Batch vs incremental ingestion
• File ingestion using Auto Loader
• Database ingestion concepts (JDBC, snapshots)
• Change Data Capture (CDC) fundamentals
• Streaming ingestion with Spark Structured Streaming
• Handling schema drift and late-arriving data

Data Transformation Techniques

• Transformations using Spark SQL
• Programmatic transformations using PySpark
• Working with Delta tables
• Joins, aggregations, and window functions
• Handling nested and semi-structured data
• Data quality checks and error handling

Layered Data Architecture (Medallion Architecture)

• Bronze, Silver, and Gold layers
• Raw vs curated datasets
• Designing incremental transformations
• Reusable transformation logic
• Performance optimization:
  • Partitioning
  • Z-ORDER
  • Caching

Building Data Pipelines

• Imperative pipelines using notebooks and jobs
• Declarative pipelines with Delta Live Tables (DLT)
• Idempotency and reprocessing strategies
• Pipeline configuration and parameters
• Metadata and logging best practices

Workflow Orchestration

• Databricks Workflows
• Multi-task jobs and dependencies
• Scheduling vs event-driven pipelines
• Retry logic and failure handling
• Comparison with external orchestrators

Monitoring & Observability

• Job and pipeline monitoring
• Spark UI and performance diagnostics
• Data freshness and completeness checks
• SLA monitoring and alerting
• Troubleshooting production issues

Data Governance & Security

• Unity Catalog architecture
• Fine-grained access control (table, column, row)
• Data lineage and impact analysis
• Managing sensitive data and PII
• Secure data sharing with Delta Sharing

Designing Production-Grade Databricks Platforms

• Scalable cluster design
• Cost optimization strategies
• High availability and disaster recovery with Delta
• Operational best practices
• Common Databricks anti-patterns

Capstone & Next Steps

• End-to-end Lakehouse pipeline walkthrough
• Real-world use case discussion
• Mapping Databricks skills to data engineering roles
• Certification pathways and learning roadmap
• Advanced Databricks topics overview