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Databricks Learning Path

Laatst bijgewerkt: 9 september 2025
Leestijd: 20 minuten
Databricks, Unity Catalog, SCD, Medallion Architecture, Delta Lake, data engineering

Een complete learning path voor Databricks: van beginner tot expert met focus op Unity Catalog, SCD implementaties en Medallion Architecture (Bronze-Silver-Gold).

Beginner → Intermediate

Core Fundamentals

Delta Lake, Spark basics, Databricks workspace, notebooks

Duur: 4-6 weken

Intermediate → Advanced

Data Engineering

Unity Catalog, Medallion Architecture, Workflows, SCD patterns

Duur: 6-8 weken

Advanced → Expert

Production & Optimization

Performance tuning, ML integration, governance, monitoring

Duur: 8-12 weken

Stapsgewijs Leerpad

1

Fase 1: Databricks Fundamentals (4-6 weken)

Bouw een solide basis in Databricks en Delta Lake

Week 1-2: Delta Lake Basics

  • Delta Lake vs Parquet/CSV
  • ACID transactions in data lakes
  • Time travel en schema evolution
  • Basis DML operaties (INSERT, UPDATE, MERGE)

Week 3-4: Databricks Workspace

  • Notebooks (Python, SQL, Scala)
  • Cluster management en configuratie
  • Repos en Git integration
  • Jobs en scheduled workflows
2

Fase 2: Unity Catalog Mastery (2-3 weken)

Beheers unified governance en data discovery

Metastore

Top-level container voor metadata

Catalog

Eerste organisatie niveau

Schema

Database / namespace

Table/View

Actuele data assets

-- Unity Catalog voorbeeld: Catalog aanmaken en gebruiken
CREATE CATALOG IF NOT EXISTS production_catalog;

-- Grant permissions aan gebruikers
GRANT USAGE ON CATALOG production_catalog TO `data-engineers`;

-- Schema aanmaken in catalog
CREATE SCHEMA IF NOT EXISTS production_catalog.sales_data;

-- Table aanmaken met Unity Catalog
CREATE TABLE production_catalog.sales_data.customers (
  customer_id STRING,
  name STRING,
  email STRING,
  created_date TIMESTAMP age INT
)
COMMENT "Customer dimension table";

-- Fine-grained access control
GRANT SELECT ON TABLE production_catalog.sales_data.customers TO `analysts`;

Unity Catalog Key Features:

  • Centralized Governance: Één plek voor alle metadata
  • Fine-grained Access Control: Row/column level security
  • Data Lineage: Volledige audit trail van data flow
  • Data Discovery: Business glossary en data catalog
  • Audit Logging: Compliance en security monitoring
3

Fase 3: Medallion Architecture (3-4 weken)

Implementeer Bronze-Silver-Gold data pipeline patterns

Bronze Layer

Raw Data

  • As-is data ingestie
  • Schema enforcement disabled
  • Minimal transformation
  • Append-only pattern

Silver Layer

Cleaned & Enriched

  • Data cleaning & validation
  • Schema enforcement
  • Data quality checks
  • Business entity view

Gold Layer

Business Ready

  • Aggregated data
  • Business metrics
  • Optimized for querying
  • ML features ready
-- Medallion Architecture pipeline voorbeeld
-- BRONZE: Raw data ingestie
CREATE OR REFRESH STREAMING LIVE TABLE sales_bronze
COMMENT "Raw sales data from source"
AS SELECT * FROM cloud_files(
  "s3://raw-data/sales/",
  "json"
);

-- SILVER: Cleaned & validated data
CREATE OR REFRESH STREAMING LIVE TABLE sales_silver
COMMENT "Cleaned sales data with quality checks"
AS SELECT
  customer_id,
  TRIM(product_name) AS product_name,
  CAST(amount AS DECIMAL(10,2)) AS amount,
  DATE(timestamp) AS sale_date,
  -- Data quality checks
  CASE 
    WHEN amount > 0 THEN 'valid'
    ELSE 'invalid'
  END AS data_quality
FROM STREAM(live.sales_bronze)
WHERE timestamp IS NOT NULL;

-- GOLD: Business aggregates
CREATE OR REFRESH LIVE TABLE daily_sales_gold
COMMENT "Daily sales aggregates for reporting"
AS SELECT
  sale_date,
  COUNT(*) AS total_transactions,
  SUM(amount) AS total_revenue,
  AVG(amount) AS avg_transaction_value
FROM live.sales_silver
WHERE data_quality = 'valid'
GROUP BY sale_date;
4

Fase 4: SCD (Slowly Changing Dimensions) (2-3 weken)

Master Type 1 en Type 2 dimension management

SCD Type 1

Overwrite History

  • Gebruik: Non-critical dimensions
  • Implementatie: UPDATE statements
  • Voordeel: Simpel, minder storage
  • Nadeel: Geen historie behoud
-- SCD Type 1: Simple update
MERGE INTO customers_target t
USING customers_source s
ON t.customer_id = s.customer_id
WHEN MATCHED THEN
  UPDATE SET *
WHEN NOT MATCHED THEN
  INSERT *;
SCD Type 2

Preserve History

  • Gebruik: Critical dimensions
  • Implementatie: Versioning met flags
  • Voordeel: Complete historie
  • Nadeel: Complex, meer storage
-- SCD Type 2: Versioning pattern
MERGE INTO customers_scd2 t
USING (
  SELECT *, 
    CURRENT_TIMESTAMP() AS start_date,
    NULL AS end_date,
    TRUE AS is_current
  FROM customers_source
) s
ON t.customer_id = s.customer_id 
  AND t.is_current = TRUE
WHEN MATCHED AND t.email != s.email THEN
  UPDATE SET 
    t.is_current = FALSE,
    t.end_date = CURRENT_TIMESTAMP()
WHEN NOT MATCHED THEN
  INSERT (customer_id, email, start_date, end_date, is_current)
  VALUES (s.customer_id, s.email, s.start_date, s.end_date, s.is_current);

Praktijkvoorbeeld: Customer Dimension SCD Type 2

-- Complete SCD Type 2 implementatie
CREATE OR REPLACE TABLE customer_dimension_scd2 (
  customer_key BIGINT GENERATED ALWAYS AS IDENTITY,
  customer_id STRING,
  customer_name STRING,
  email STRING,
  address STRING,
  start_date TIMESTAMP,
  end_date TIMESTAMP,
  is_current BOOLEAN,
  version_number INT
)
USING DELTA
PARTITIONED BY (is_current);

-- Initial load
INSERT INTO customer_dimension_scd2
SELECT
  customer_id,
  customer_name,
  email,
  address,
  CURRENT_TIMESTAMP() AS start_date,
  NULL AS end_date,
  TRUE AS is_current,
  1 AS version_number
FROM initial_customer_data;

-- Incremental update met SCD Type 2
MERGE INTO customer_dimension_scd2 AS target
USING (
  SELECT 
    *,
    ROW_NUMBER() OVER (
      PARTITION BY customer_id 
      ORDER BY updated_at DESC
    ) AS rn
  FROM customer_updates
  WHERE updated_at > (SELECT MAX(start_date) FROM customer_dimension_scd2)
) AS source
ON target.customer_id = source.customer_id 
  AND target.is_current = TRUE
WHEN MATCHED AND (
  target.customer_name != source.customer_name
  OR target.email != source.email
  OR target.address != source.address
) THEN
  -- Expire old record
  UPDATE SET 
    target.end_date = CURRENT_TIMESTAMP(),
    target.is_current = FALSE
WHEN NOT MATCHED AND source.rn = 1 THEN
  -- Insert new version
  INSERT (
    customer_id, customer_name, email, address, 
    start_date, end_date, is_current, version_number
  )
  VALUES (
    source.customer_id,
    source.customer_name,
    source.email,
    source.address,
    CURRENT_TIMESTAMP(),
    NULL,
    TRUE,
    COALESCE(
      (SELECT MAX(version_number) 
       FROM customer_dimension_scd2 
       WHERE customer_id = source.customer_id), 
      0
    ) + 1
  );
5

Fase 5: Advanced Patterns & Best Practices (4-6 weken)

Production-ready implementaties en performance optimization

Performance Optimization

  • Z-ordering en partitioning
  • Delta Lake optimization
  • Cluster tuning en auto-scaling
  • Query optimization techniques

Data Governance

  • Unity Catalog security policies
  • Data quality monitoring
  • Compliance en audit logging
  • GDPR/AVG compliance patterns

CI/CD & Automation

  • Databricks workflows orchestration
  • Git integration voor notebooks
  • Automated testing frameworks
  • Deployment pipelines

Monitoring & Observability

  • Cluster performance monitoring
  • Job failure alerts
  • Cost optimization strategies
  • Data pipeline observability

Databricks Certificeringen

Data Engineer Associate

Level: Entry-level

Focus: Delta Lake, basic ETL

Voorbereiding: 4-6 weken

Data Engineer Professional

Level: Advanced

Focus: Production pipelines, optimization

Voorbereiding: 8-12 weken

Data Analyst Associate

Level: Analytics-focused

Focus: SQL, dashboards, visualisatie

Voorbereiding: 4-6 weken

Certificering Tips:

  1. Begin met de Associate-level certificering
  2. Oefen met de officiële exam guides
  3. Maak gebruik van Databricks Community Edition voor praktijk
  4. Focus op hands-on experience ipv alleen theorie
  5. Plan je examen na minstens 3 maanden praktijk

Officiële Documentatie

Complete Databricks documentation met tutorials en best practices.

Focus: Reference material, API docs, release notes

Databricks Academy

Gratis online courses van Databricks zelf, inclusief labs en quizzes.

Focus: Hands-on learning, certification prep

GitHub Repositories

Open-source voorbeelden en templates voor verschillende use cases.

Focus: Code examples, project templates

Praktijk Projecten

E-commerce Data Platform

  • Bronze: Raw order data ingestie
  • Silver: Customer dimension SCD Type 2
  • Gold: Sales aggregations & ML features
  • Unity Catalog: Data governance setup

IoT Data Pipeline

  • Bronze: Streaming sensor data
  • Silver: Data validation & cleaning
  • Gold: Predictive maintenance features
  • Delta Live Tables: Real-time processing