Step-by-Step Databricks Data Engineer Study Plan

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Here’s a step-by-step learning plan that smoothly takes you from Associate-level foundations to Professional-level mastery for the Databricks Data Engineer certifications. This path combines theory, hands-on labs, and where to “go deeper” as you progress.

🛤️ Step-by-Step Databricks Data Engineer Study Plan

(Associate ➔ Professional: Fully Linked)

Step 1: Databricks Platform Foundations

  • Understand Databricks architecture (control vs data plane, Lakehouse vision)
  • Familiarize yourself with the Workspace: notebooks, Repos, clusters, DBFS, magic commands
  • Professional deep dive: Learn about REST API, Databricks CLI, advanced cluster configs, and Databricks Connect for remote development

Step 2: Data Ingestion & Connectivity

  • Associate:
    • Read/write data with Spark (DataFrames, SQL)
    • Load from CSV, JSON, Parquet, JDBC
    • Use COPY INTO, Auto Loader for files
  • Professional (expand):
    • Complex data sources (nested JSON, Avro, streaming sources)
    • Ingest from message buses (Kafka, Event Hubs, Kinesis)
    • Handle schema inference and complex error handling

Step 3: Data Transformation & ETL Patterns

  • Associate:
    • DataFrame API basics (filter, join, groupby, aggregations, UDFs)
    • Delta Lake basics: ACID, schema enforcement, time travel, versioning
    • Multi-hop architecture (bronze/silver/gold)
  • Professional (expand):
    • Advanced Spark SQL (window functions, pivots, ranking)
    • Performance tuning: partitioning, bucketing, caching, broadcast joins
    • Skew handling, optimizing large jobs, resource management

Step 4: Pipelines & Orchestration

  • Associate:
    • Databricks Jobs/Workflows: create, schedule, manage jobs
    • Understand job parameters, retries, notifications
  • Professional (expand):
    • Multi-task workflows (DAGs), task dependencies, dynamic pipelines
    • Asset Bundles (DAB) for deployment automation
    • CI/CD integration (GitHub Actions, Azure DevOps), environment promotion

Step 5: Delta Live Tables (DLT) & Declarative Pipelines

  • Associate:
    • Basics of DLT (LIVE tables, simple streaming/batch pipelines)
  • Professional (expand):
    • Advanced DLT features: expectations, error handling, change data capture (CDC), incremental loads, monitoring pipeline health

Step 6: Data Modeling & Optimization

  • Associate:
    • Star/snowflake schema basics, denormalization, partitioning, ZORDER
  • Professional (expand):
    • Advanced data modeling patterns for lakehouse
    • Schema evolution, optimizing for high concurrency, materialized views
    • Deep-dive: table maintenance (VACUUM, OPTIMIZE), Delta clones, updates

Step 7: Streaming & Real-Time Analytics

  • Associate:
    • Basic structured streaming (batch vs streaming, simple pipelines)
  • Professional (expand):
    • Build robust streaming pipelines (stateful aggregations, windowed operations)
    • Watermarking, handling late/out-of-order data, exactly-once guarantees

Step 8: Data Governance & Security

  • Associate:
    • Unity Catalog basics (catalogs, schemas, RBAC), grants/permissions
    • Object storage security, table ACLs
  • Professional (expand):
    • Advanced Unity Catalog: fine-grained access, lineage, audit logs
    • Secure cluster policies, encryption at rest/in transit, workspace isolation

Step 9: Monitoring, Logging, and Troubleshooting

  • Associate:
    • Basic monitoring with Spark UI, job logs, cluster health
  • Professional (expand):
    • Advanced pipeline monitoring, Databricks SQL dashboards, alerting
    • Debugging slow/failed jobs, interpreting logs, job metrics and profiling

Step 10: Testing, CI/CD, and Deployment

  • Associate:
    • Manual workflow deployment, version control basics
  • Professional (expand):
    • Automated testing (unit/integration with pytest, data validation)
    • CI/CD pipelines for notebooks & DLT, rollbacks, canary deployments
    • Full automation using Asset Bundles, REST API, and CLI

🗂️ How To Study:

  • For Each Step:
    • Read official Databricks documentation (docs.databricks.com)
    • Complete hands-on labs in Databricks Free Edition
    • Review relevant Academy course modules
    • Practice real exam questions/scenarios for each section
    • Use the REST API/CLI for Professional-level hands-on
  • After Each Major Step:
    Try to explain the topic or create a mini-project for real understanding.

🏁 Final Review and Practice

  • Review official exam guides and objectives for both Associate and Professional
  • Take multiple practice exams and analyze gaps
  • Build an end-to-end data engineering project using Databricks, covering ingestion, ETL, DLT, streaming, governance, deployment, and monitoring

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