Traditional data warehouses are slow, expensive, and locked behind proprietary systems. They demand constant tuning and create friction for analytics teams that need speed and scale, and slow down decisions in finance, operations, and product teams. Databricks SQL (DBSQL) removes these limitations. It is 5x faster on average, runs serverless and adheres to open standards. It is not locked behind the default Performance Intelligence premium tiers.
Over 60% of the Fortune 500 use DBSQL For analytics and BI on Databricks Data Intelligence Platform.
In 2025, DBSQL continued to provide functionality that improved performance, AI, cost management, and Open SQL capabilities. This roundup highlights the updates that made the biggest impact for data teams this year.
Performance that automatically improves
Fast queries without tuning
By 2022, DBSQL Serverless has delivered a Average 5x performance improvementDashboards that previously took 10 seconds now load in about 2 seconds, without the need for index management or manual tuning,
Performance improves again in 2025:
Because Databricks is built on the Data Intelligence Platform, this intelligence is available to every customer by default, not locked behind premium tiers or the highest priced offerings.
Better visibility with query profiles
To help teams understand performance patterns Update Query Profile View Now it includes:
- A visual summary of read and write metrics
- A “Top Operators” panel to identify expensive parts of a query
- Clear navigation through performance graphs
- Filters to focus on specific metrics
This helps teams diagnose slow dashboards and complex models more quickly, without relying on guesswork.
AI built directly into SQL workflows
AI is now part of everyday analytics. In 2025, DBSQL is introduced native AI functions Analysts can therefore access large language models directly in SQL. Some of the new capabilities include:
- ai_query For summarization, classification, extraction and sentiment analysis
- ai_parse_documentCurrently in beta, converts PDFs and other unstructured documents to tables
These functions run on Databricks-hosted models, such as Meta Llama and OpenAI GPT OSS, or custom models you provide. They are optimized for scale and are up to 3x faster than alternative methods.
Teams can now summarize support tickets, extract fields from contracts, or analyze customer feedback directly inside reporting queries. Analysts live in SQL. Workflow moves faster. No more tool switching or coding in Python.
Automated performance management with predictive optimization
As data grows and workload changes, performance often degrades over time. Predictive optimization directly addresses this problem.
In 2025, Automated Statistics Management Became generally available. This removes the need to run the ANALYZE command or manually manage optimization tasks.
Now, predictive optimization automatically:
- Collects optimization statistics after data is loaded
- Selects data skipping index
- Continuously improves execution plans over time
This reduces operational overhead and prevents the serial performance drift that many warehouses struggle with.
Open SQL features that simplify migration
For many customers, stored procedures, transactions and proprietary SQL constructs are the hardest part of leaving legacy warehouses. But, many companies want to migrate from legacy systems like Oracle, Teradata, and SQL Server for TCO and innovation reasons. DBSQL continued its investment in open, ANSI-compliant SQL features to reduce migration effort and increase portability.
New capabilities include:
- stored procedures (Public Preview) with Unity Catalog Governance
- SQL Scripting (Generally Available) For Loops and Conditionals in SQL
- recurrent cte (Generally available) for hierarchical queries
- Combination (public preview) for language-aware sorting and comparison
- temporary tables (Public preview for all customers in January) To remove the burden of managing intermediate tables or tracking residual data
These features follow open SQL standards and are available in Apache Spark. They make migration easier and reduce dependency on proprietary builds.
DBSQL also added Spatial SQL With geometry and geography types. Over 80 functions like ST_Distance and ST_Contains support large-scale geospatial analysis directly in SQL.
Cost management for large scale workloads
As SQL adoption grows, teams struggle to explain the increasing spend on warehouses, dashboards, and tools. DBSQL introduces new tools that help teams monitor and control spend at the warehouse, dashboard, and user level.
Major updates include:
- Account Usage Dashboard Identifying rising costs
- Tags and Budget To track expenses by team
- system tables For detailed query level analysis
- granular cost monitoring dashboard and physical view (Private Preview) for alerts and cost driver tracking
These features make it easy to understand which queries, dashboards, or tools drive consumption.
Warehouse monitoring and access control
As more teams rely on DBSQL, administrators need to monitor concurrency and warehouse health without granting users excessive privileges. DBSQL also added new governance and observation capabilities:
- full query count (GA) to show how many queries finish in a time window, helping to identify concurrency patterns
- can see Permissions so administrators can grant read-only access to monitoring without granting execution rights
These updates make it easier to run secure, reliable analytics at scale.
the outcome
DBSQL continues to improve in 2025. It now offers fast serverless performance, built-in AI, open SQL standards for easy migration, and clear visibility into cost and workload behavior. Because DBSQL runs on the Databricks Lakehouse architecture, analytics, data engineering, and AI all work on a single, governed foundation. Performance improves automatically, and teams spend less time tuning systems or managing handoffs.
DBSQL remains an open, intelligent, cost-efficient warehouse designed for the realities of AI-powered analytics – and 2025 pushes it forward again.
what will happen next
Databricks SQL continues to lead the market as an AI-native, operations-ready warehouse that eliminates the complexity customers face in legacy systems. Upcoming features include:
- multi-statement transactions, That gives teams atomic updates across multiple tables and removes the brittle custom rollback logic many customers create themselves. Multi-statement transactions would also be beneficial to migrate to Databricks.
- Alert V2, Which increases reliability in day-to-day operations, replacing a complex alerting system with thousands of scheduled checks and a simple, scalable model designed for enterprise-grade operational patterns.
- More AI CapabilitiesAnalysts can therefore apply LLM and process documents without leaving their workflow, bridging the gap between warehouse logic and intelligence.
Together, these capabilities move DBSQL toward a unified, intelligent warehouse that handles core transaction logic, operational monitoring, and AI-assisted analytics in one place.
More details on innovations
We hope you enjoy this abundance of innovations in Databricks SQL. you can check it anytime What is the new post in the last three monthsBelow is the full list of launches we blogged about over the past quarter:
launch
Are you ready to transform your data warehouse? The best data warehouse is a lakehouse! To learn more about Databricks SQL, a product tourvisit databricks.com/sql Come explore Databricks SQL and see how organizations around the world are revolutionizing their data platforms.
