Databricks Learning Path
Een complete learning path voor Databricks: van beginner tot expert met focus op Unity Catalog, SCD implementaties en Medallion Architecture (Bronze-Silver-Gold).
Core Fundamentals
Delta Lake, Spark basics, Databricks workspace, notebooks
Duur: 4-6 weken
Data Engineering
Unity Catalog, Medallion Architecture, Workflows, SCD patterns
Duur: 6-8 weken
Production & Optimization
Performance tuning, ML integration, governance, monitoring
Duur: 8-12 weken
Stapsgewijs Leerpad
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
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
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;
Fase 4: SCD (Slowly Changing Dimensions) (2-3 weken)
Master Type 1 en Type 2 dimension management
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 *;
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
);
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:
- Begin met de Associate-level certificering
- Oefen met de officiële exam guides
- Maak gebruik van Databricks Community Edition voor praktijk
- Focus op hands-on experience ipv alleen theorie
- 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