Used For

• +**Your Spark jobs fail on large datasets and take weeks to configure** → Auto-scaling clusters handle terabyte loads without manual tuning, jobs run reliably
• +**You're manually moving data between 5 systems for 20 hours per week** → Automated pipelines sync everything, you get your weekends back
• +**Your analysts wait 3 days for IT to run queries on big data** → Self-service SQL interface runs 6x faster than traditional data warehouses
• +Unity Catalog gives you one place to control who sees what data - no more accidentally sharing customer emails with interns
• +ML lifecycle tools let data scientists deploy models without begging DevOps for help

Best For

• >Your data pipelines break every weekend at 3am and someone has to fix them
• >You're processing terabytes of data and your current setup keeps crashing
• >Your data engineers spend more time configuring clusters than actually analyzing data

Not For

• -Teams under 50 people or handling less than 1TB of data — you're paying Ferrari prices to drive to the grocery store
• -Companies without dedicated data staff — this requires Spark knowledge or you'll burn through your budget learning
• -Anyone hoping for simple drag-and-drop analytics — this is a developer tool that assumes you know Python or SQL

Pairs With

• *AWS S3 (where your raw data actually lives before Databricks processes it)
• *Tableau (for pretty dashboards because Databricks SQL interface looks like a terminal)
• *dbt (handles the data transformations that Databricks could do but dbt does more elegantly)
• *Airflow (to orchestrate workflows since Databricks Jobs are good but not great for complex scheduling)
• *Snowflake (as the final destination for your processed data that business users actually query)
• *Unity Catalog (Databricks' own governance tool because someone needs to control who can see the customer data)

The Catch

• !Pricing is usage-based with no public rates - you'll get a 'custom quote' that starts around $100k/year for real enterprise use
• !Idle clusters rack up charges silently - forget to shut down a test cluster and you'll get a $500 surprise bill
• !The learning curve is steep if you're not already familiar with Spark - expect 4-6 weeks before engineers are optimizing performance

Bottom Line

Handles enterprise-scale data processing that would normally require a PhD in Apache Spark and a therapy budget for your engineering team.