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Databricks Lakehouse

⬢ NIVÅ 2Tekniskt
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4 månader
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Svårighetsgrad
2
Karriärer
I korthet

Databricks is a unified analytics platform combining data lake + data warehouse. Delta Lake adds ACID transactions, schema enforcement, time travel to Parquet files. Databricks handles Spark orchestration, simplifying big-data jobs. Senior practitioners earn 20-25% premium because they ship lakehouse systems serving 1000+ analysts and processing petabytes. Learning: 8-12 weeks (requires Spark + SQL + architecture knowledge).

Vad är Databricks Lakehouse

Databricks is a unified analytics platform combining the best of data lakes and data warehouses. Built on Delta Lake (ACID transactions for Parquet files) and Apache Spark (distributed computing), Databricks lets teams build data pipelines, run analytics, and train ML models in one platform. Example: Ingest raw data to S3 (via Spark job) → Store in Delta Lake table → dbt transforms for analytics → BI tools query Databricks SQL → Data scientists train models on Databricks MLflow.

🔧 VERKTYG & EKOSYSTEM
Databricks workspaceDelta LakeApache Spark (PySpark, Scala, SQL)Databricks SQLDatabricks JobsUnity Catalogdbt (transforms)Python/ScalaSQL for queryingMLflow (ML tracking)

📋 Innan du börjar

Python

💰 Lön per region

OmrådeNybörjareMidErfaren
USA$90k$160k$250k
UK£55k£98k£155k
EU€62k€105k€165k
CANADAC$85kC$155kC$240k

🎯 Karriärer som använder Databricks Lakehouse

Data Architect
Data Scientist

❓ Vanliga frågor

What's the difference between Databricks and Snowflake?
Snowflake is a cloud data warehouse (SQL-focused, good for BI). Databricks is a lakehouse (SQL + Spark, good for data eng + ML + analytics). Databricks wins for ETL, data science. Snowflake wins for simple BI.
What's Delta Lake?
Delta Lake adds ACID transactions, schema enforcement, data quality, and time travel to Parquet files. You get data warehouse features (transactions, rollback) on data lake storage (S3, Azure). Best of both worlds.
Why use Databricks instead of raw Spark?
Databricks handles Spark cluster management, optimization, and security. With raw Spark, you manage clusters manually. Databricks simplifies: write code, submit job, Databricks runs it at scale.
How do I ensure data quality in Databricks?
Use Delta Lake constraints (NOT NULL, CHECK). Use dbt tests. Use Databricks expectations (Great Expectations integration). Monitor with Databricks SQL.
Can I mix SQL and Python in Databricks?
Yes. Notebooks mix SQL, Python, R, Scala. Write SQL query, get Python dataframe, transform, store back. Perfect for exploratory work.

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