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Databricks Lakehouse

⬢ LIVELLO 2Tecniche
Alto
Impatto sullo stipendio
4 mesi
Tempo di apprendimento
Difficile
Difficoltà
2
Carriere
In sintesi

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).

Cos'è 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.

🔧 STRUMENTI ED ECOSISTEMA
Databricks workspaceDelta LakeApache Spark (PySpark, Scala, SQL)Databricks SQLDatabricks JobsUnity Catalogdbt (transforms)Python/ScalaSQL for queryingMLflow (ML tracking)

📋 Prima di iniziare

Python

💰 Stipendio per regione

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

🎯 Carriere che usano Databricks Lakehouse

Data Architect
Data Scientist

❓ Domande frequenti

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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