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Databricks

This section provides comprehensive information on preparing for the Databricks.

Databricks Lakehouse Platform

The Databricks Lakehouse Platform unifies the best elements of data lakes and data warehouses. It enables enterprises to store structured, semi‑structured, and unstructured data in open formats while supporting analytics and AI workloads with full ACID transactions and scalability. The platform is built on Apache Spark, Delta Lake, MLflow, and Unity Catalog, delivering unified pipelines for ETL, machine learning, governance, and BI reporting.(databricks.com)

ETL with Spark SQL and Python

Databricks provides a unified framework for building ETL jobs using Spark SQL and Python. ETL pipelines can be implemented imperatively in notebooks or using Delta Live Tables or Lakeflow Declarative Pipelines. These pipelines support batch, streaming, and auto‑CDC ingestion, with built‑in schema evolution, fault tolerance, idempotence, and orchestration. Tools like Databricks Autoloader simplify ingestion of new files into Delta tables.(docs.databricks.com)

Incremental Data Processing

Change Data Feed (CDF)

Delta Lake’s Change Data Feed enables efficient incremental processing by capturing inserts, updates, and deletes at row-level granularity. CDF records metadata about each change, including operation type, timestamp, before/after state, and table version. It must be explicitly enabled on Delta tables to work. CDF-based pipelines process only the changed rows since last execution, reducing compute and storage overhead.(coditation.com)

Lakeflow Incremental Flows

Lakeflow Declarative Pipelines support both streaming and batch incremental ingestion. “Append flows” add new data incrementally; “Auto CDC flows” manage change-capture ingestion. Materialized views can also use incremental refresh semantics if run on serverless pipelines.(docs.databricks.com)

Incremental ETL reduces latency, improves cost effectiveness, supports atomic updates, and enables multiple dataset tiers (bronze/silver/gold). It also simplifies tracking pipeline state and recovering from failures.(databricks.com)

Production Pipelines

Production pipelines should follow data engineering best practices:

  • Use appropriate partitioning and file sizing to optimize joins and query performance.
  • Configure state checkpointing and RocksDB where needed in streaming workloads.
  • Observe structured streaming considerations, including schema validation and failure recovery.
  • Employ observability tools to monitor job status, data quality metrics, and lineage via Unity Catalog.
  • Automate deployment using CI/CD and DevOps workflows for reliability and consistency.(Microsoft Learn, Microsoft Learn)

Data Governance

Unity Catalog provides centralized governance for tables, schemas, and fine‑grained access control across clouds. It integrates with Lakehouse Federation to govern external SQL sources, supports catalog federation, and tracks schema lineage. Delta Sharing enables secure live data sharing outside the platform.(docs.databricks.com)

Enhanced Preparation Content Summary

Area Enhancement Highlights
Lakehouse Platform Emphasis on openness, ACID support, unified engine, and AI/BI convergence.
ETL (Spark SQL/Python) Addition of Delta Live Tables, Autoloader, and declarative pipelines with schema evolution.
Incremental Processing Detailed on CDF, materialized view refresh, auto-CDC flows, architecture of incremental pipelines.
Production Pipelines Best practices: partitioning, checkpointing, observability, CI/CD integration.
Governance Unity Catalog details: federated sources, lineage, access control, and Delta Sharing capabilities.

Prerequisites

  • SQL: proficiency in basic DDL and DML statements.
  • Python: familiarity with Spark DataFrame API, notebook execution, and pipeline development.
  • Basic understanding of data engineering concepts: partitioning, CDC, file-based storage, batch vs streaming, and pipeline orchestration.

Prepared enhancements ensure coverage of modern Databricks platform capabilities, recommended architecture patterns, and production-grade pipeline designs with up‑to‑date references.