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What's new in Databricks in 2026?

Aleksander Fegel · 20 March 2026 · 4 min read

Data platform

What's new in Databricks in 2026?

Ailio

Innovations at Azure Databricks: The journey to the intelligent lakehouse for the era of AI agents

Databricks is solidifying its position as a leading platform through a series of strategic innovations aimed at integrating data engineering, analytics and AI even more closely. The focus is on the democratization of data acquisition, a new operational database for AI applications and deep integrations into the Microsoft 365 environment.

In this article we take a detailed look at the most important innovations.

1. Lakeflow Connect: Free entry into modern data ingestion

A reliable data flow is the foundation of every analysis. With the introduction of the Lakeflow Connect Free Tier, Databricks is making it easier than ever for companies to mirror data from a variety of sources into the lakehouse.

The highlights of the Free Tier:

  • 100 free DBUs per day: Each workspace receives a daily quota that can process approximately 100 million records for free.
  • Wide Connectivity: Lakeflow supports mirroring of nine of the most widely used databases (including SQL Server, Oracle, PostgreSQL, Snowflake, and BigQuery), as well as popular SaaS applications such as Salesforce, Dynamics 365, and Workday.
  • Seamless integration: Data is written directly to Azure Data Lake Storage (ADLS) open storage and managed via the Unity Catalog.

By combining ingestion, orchestration, and transformation, Lakeflow enables teams to build pipelines up to 25x faster while massively reducing ETL costs.

2. Azure Databricks Lakebase: The database for the agentic era

One of the most significant announcements is the general availability of Azure Databricks Lakebase. This is a managed, serverless Postgres service designed specifically to meet the needs of modern AI agents.

AI agents need a transactional system to manage states, log actions, and control workflows. Lakebase closes the gap between analytical lakehouse and operational database.

Core features of Lakebase:

  • Serverless Postgres: Leverage familiar Postgres features including extensions like pgvector for AI vector searches.
  • Efficiency: Thanks to the separation of processing power and storage, Lakebase offers sub-second startup and scale-to-zero pricing.
  • Developer Features: Features like branching and instant restore make modern development workflows much easier.

Available now in 14 Azure regions worldwide, Lakebase is ideal for real-time personalization, feature serving, and AI agent state management.

3. Databricks in the Microsoft 365 ecosystem

Data is most valuable when it is available where decisions are made: in Excel and Teams. Databricks is significantly expanding its presence within Microsoft 365.

The new Excel add-in (public preview)

The Azure Databricks Excel Add-in allows users to access tables and metric views in the Unity Catalog directly from Excel. Instead of relying on insecure data exports, users can now:

  • Use governed data directly in pivot tables.
  • Filter and analyze data without requiring SQL knowledge.
  • Work across platforms on Windows, macOS and the web.

In addition, the integration into Microsoft Teams and the M365 Copilot will be deepened so that employees can receive answers to complex data questions directly in their usual work environment via AI interfaces such as Genie.

4. Genius: From Simple Query to AI Analyst

The AI-powered analytics tool Genie has evolved from a simple chat interface to a sophisticated partner for data teams. There are three crucial innovations here:

Genie Agent Mode

Unlike traditional chatbots, Agent Mode uses multi-level logical thinking and hypothesis testing. It independently generates research plans, executes multiple queries one after the other and refines its analysis based on intermediate results. Genie not only provides data, but also well-founded answers to the “why” behind the numbers.

Genius code

For data engineers and data scientists, Genie Code acts as a specialized agent within the Databricks workspace. He understands the context of the company (via Unity Catalog), helps debug pipelines, creates SQL queries and automates the monitoring of workflows.

Databricks One & Mobile

Databricks One creates a centralized, multi-agent chat experience. Users no longer need to know which “space” their data is in – the AI ​​finds and combines the information automatically. Thanks to the new Mobile App (iOS/Android), these insights can now be accessed safely while on the go.

Conclusion: A unified ecosystem for the future

The latest updates to Azure Databricks show a clear trend: the boundaries between data engineering, operational databases and business intelligence are blurring through the use of AI.

By lowering entry barriers to data ingestion (Lakeflow Connect), providing a robust foundation for AI agents (Lakebase) and seamless integration into everyday work (Microsoft 365 & Genie), Azure Databricks offers one of the most powerful and cost-effective platforms for modern companies.

The future of data analysis on Azure is not only integrated and secure, but above all: intelligent.

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