Glossary

Databricks

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A Pangea Expert Glossary Entry
Written by John Tambunting
Updated Feb 18, 2026

What is Databricks?

Databricks is the Data Intelligence Platform — a unified environment for data engineering, analytics, and AI built on the open-source lakehouse architecture. Founded by the creators of Apache Spark at UC Berkeley, Databricks has grown into one of the most important companies in the data ecosystem, surpassing $4.8 billion in annual revenue run rate while growing 55% year-over-year. The platform runs on all three major clouds (AWS, Azure, GCP) and powers data infrastructure for thousands of enterprises. At its core, Databricks solves the problem that plagued organizations for decades: the need to maintain separate, siloed systems for data lakes, data warehouses, and machine learning platforms.

Key Takeaways

  • Unified data + AI platform with lakehouse architecture built on Apache Spark and Delta Lake
  • $4.8B+ revenue run rate growing 55% YoY — one of the largest private tech companies
  • Runs on AWS, Azure, and GCP with Unity Catalog for unified data governance
  • Open-source foundations: Apache Spark, Delta Lake, MLflow
  • High demand for Databricks engineers and data scientists in fractional and full-time roles

The Lakehouse Architecture Explained

For years, companies ran two separate systems: a data lake (cheap storage for raw data, but slow and unstructured) and a data warehouse (fast queries on structured data, but expensive and rigid). Databricks coined and popularized the lakehouse — a single architecture that gives you the flexibility of a data lake with the performance and reliability of a warehouse. The secret sauce is Delta Lake, an open-source storage layer that adds ACID transactions, schema enforcement, and time travel to raw data stored in cloud object storage. You get the cost benefits of storing everything in one place while still being able to run fast SQL analytics, streaming pipelines, and ML training jobs on that same data. Unity Catalog sits on top, providing fine-grained access control, lineage tracking, and discovery across all data and AI assets.

Databricks vs Snowflake

This is the comparison that dominates the data platform conversation. Snowflake is SQL-first: it excels at structured data analytics, automatic scaling, and BI tool integration. If your primary workload is SQL queries and dashboards, Snowflake's simplicity is compelling. Databricks is Spark-first: it's built for teams that need data engineering, ML/AI, and analytics on a single platform. The lakehouse architecture means you can run Python notebooks, SQL queries, and ML training jobs on the same data without moving it between systems. Databricks also has a stronger open-source story — your data lives in open formats (Delta Lake/Parquet) that you can access with any tool, reducing vendor lock-in. The general pattern: Snowflake for analytics-heavy organizations, Databricks for teams doing serious data engineering and ML alongside analytics.

Databricks in the Remote Talent Context

Databricks expertise is one of the highest-demand skills in the data engineering market. On platforms like Toptal and Upwork, Databricks specialists command premium rates — and the supply can't keep up with demand. The core skill set includes Apache Spark/PySpark for distributed data processing, Delta Lake for storage and versioning, Python/SQL for pipeline development, and familiarity with cloud services (AWS, Azure, or GCP). On Pangea, we see companies hiring fractional data engineers specifically for Databricks implementation and migration projects. The typical engagement: a company wants to consolidate from separate warehouse and lake systems into a unified lakehouse, and they need someone who's done it before. These roles often pay at the top end of the data engineering spectrum.

Pricing Model

Databricks uses consumption-based pricing measured in Databricks Units (DBUs). A DBU is a unit of processing capability, and the cost per DBU varies by workload type and cloud provider. SQL warehousing, data engineering, and ML workloads each have different DBU rates. This model means you pay for what you use, but costs can be unpredictable without careful monitoring. Most organizations start with a Standard tier and graduate to Premium or Enterprise for advanced security, governance, and compliance features. There's a Community Edition (free) for learning and small experiments. For production workloads, expect costs to scale with data volume and compute requirements — Databricks isn't cheap, but the ROI argument rests on consolidating multiple tools into one platform.

The Bottom Line

Databricks has become the platform of choice for organizations building serious data and AI infrastructure. Its lakehouse architecture, open-source foundations, and unified approach to analytics and ML make it the natural pick for companies that have outgrown basic data tools. For companies hiring through Pangea, Databricks experience signals a data engineer or scientist who can handle enterprise-scale data challenges — the kind of expertise that's in short supply and high demand.

Databricks Frequently Asked Questions

Is Databricks only for large enterprises?

No, but it's most cost-effective at scale. Small teams can start with the Community Edition for free. However, production workloads on Databricks typically make economic sense for organizations with significant data volumes or complex ML requirements.

Do I need to know Apache Spark to use Databricks?

Not necessarily. Databricks offers SQL-first interfaces for analysts and BI users. However, data engineers and ML practitioners will benefit significantly from Spark/PySpark knowledge for advanced pipeline development and model training.

How does Databricks handle AI and machine learning?

Databricks provides MLflow for experiment tracking and model management, vector search for RAG applications, and native notebook environments for model development. The platform supports the full ML lifecycle from data preparation to model serving.

Can I use Databricks with my existing data warehouse?

Yes. Databricks can read from and write to most data sources. Many organizations run Databricks alongside existing warehouses during migration, or use it specifically for data engineering and ML while keeping a separate analytics warehouse.
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