Databricks
Integrations
- Apache Spark (OSS)
- Delta Lake (OSS)
- MLflow (OSS)
- Snowflake (Mirroring)
- Databricks Asset Bundles (CI/CD)
- Power BI / Tableau
Pricing Details
- Billed based on Databricks Units (DBUs) consumed.
- Serverless compute, Mosaic AI Model Training, and Vector Search are billed as separate consumption units.
Features
- Unity Catalog Unified Governance (OSS)
- Photon Vectorized Query Engine (C++)
- Mosaic AI Agent Framework & Agent Bricks
- Lakeflow Declarative Pipelines
- Databricks Assistant & DatabricksIQ
- Serverless SQL & AI Workloads
Description
Databricks Data Intelligence Infrastructure Review
The 2026 Databricks environment operates as a Data Intelligence Platform, utilizing DatabricksIQ to embed AI into every layer of the lakehouse. The architecture is centered on Unity Catalog, which has transitioned to an open-source standard for governing tables, files, ML models, and autonomous AI agents 📑.
Core Processing & Vectorized Execution
The platform utilizes the Photon engine, a native C++ vectorized execution layer, to bypass the performance bottlenecks of the JVM for analytical workloads.
- Lakeflow Declarative Pipelines: Input: Batch and streaming data sources → Process: Autonomous orchestration and incremental refresh via Delta Live Tables logic → Output: Optimized Silver/Gold medallion tables with full lineage 📑.
- Photon Engine: Provides up to 8x speedup for complex joins and aggregations by utilizing hardware-level parallelism and vectorized UDFs 📑.
- Serverless SQL Warehouses: Automatically scales compute based on workload patterns; however, the internal predictive heuristics for minimizing serverless cold-start latency remain undisclosed 🌑.
⠠⠉⠗⠑⠁⠞⠑⠙⠀⠃⠽⠀⠠⠁⠊⠞⠕⠉⠕⠗⠑⠲⠉⠕⠍
Mosaic AI & Agentic Orchestration
The 2026 stack features Mosaic AI and the Agent Bricks suite to build and govern autonomous agents grounded in enterprise data.
- Mosaic AI Agent Framework: Input: High-level business intent → Process: Agentic RAG orchestration grounded in Unity Catalog metadata and vector search retrieval tools → Output: Verifiable insights with multi-hop reasoning and source citations 📑.
- Agent Bricks (Auto-Optimization): Automatically optimizes agent quality and cost by selecting the best model-tool combinations for specific task-resolution patterns 📑.
Governance & Open Interoperability
Unity Catalog (OSS) serves as the universal control plane, ensuring that data and AI assets are accessible across different engines and clouds.
- Lakehouse Federation: Enables query pushdown to external systems (Snowflake, BigQuery, Oracle) without data movement; however, cross-cloud egress costs and synchronization delays are not publicly quantified 🌑.
- Universal Data Objects: Supports Delta, Iceberg, and Hudi formats natively through the Unity Catalog REST API, ensuring zero-copy interoperability 📑.
Evaluation Guidance
Technical evaluators should verify the following architectural characteristics:
- A2A Negotiation Latency: Benchmark the handshake overhead when Databricks agents collaborate with external agent ecosystems (e.g., Salesforce Agentforce) via the A2A protocol 🌑.
- Photon DBU ROI: Organizations must validate that the 2x premium DBU rate for Photon-enabled clusters is offset by at least a 3x reduction in execution time for their specific workload portfolio 🧠.
- Unity Catalog Sync Latency: Verify the consistency and propagation delay of fine-grained access policies across multi-region workspace deployments 🌑.
Release History
Year-end update: Release of the Agentic Data Hub. Autonomous agents now proactively manage data quality and suggest pipeline optimizations via Unity Catalog.
Launch of AI Functions in SQL. Allows users to call LLMs directly from SQL queries for sentiment analysis, translation, and classification.
Integration of MosaicML technology. Launch of DBRX, a state-of-the-art open LLM, optimized for enterprise data intelligence.
General availability of Unity Catalog. First unified governance solution for files, tables, and ML models across clouds.
Official unveiling of the 'Lakehouse' paradigm, combining the performance of data warehouses with the flexibility of data lakes.
Introduced Delta Lake (ACID transactions for data lakes) and MLflow (open source platform for the ML lifecycle).
Launched the Unified Analytics Platform, bringing Data Engineering and Data Science together in collaborative notebooks.
Founded by the creators of Apache Spark. Initial focus on providing a managed environment for large-scale data processing.
Tool Pros and Cons
Pros
- Scalable data processing
- Unified data platform
- Collaborative workspace
- MLflow integration
- Delta Lake performance
Cons
- Complex setup
- Potential cost
- Vendor lock-in
