Database Professionals Virtual Meetup Group

Are you uncertain if SQL Server 2025 Vector Search has useful applications? Concerned that it is just more AI hype?

Then join me in this session, where I’ll break down how Vector Search works, where it fits into the AI landscape, and how you can use it today—on-prem and without changing your core architecture.

We’ll cover:

* The foundations of vector embeddings and similarity search
* How SQL Server implements Vector Search, including indexing and querying
* Code demos and practical use case exploration

You’ll leave armed with the knowledge to look beyond the hype and understand how Vector Search can work for you.

📅 February 19th, 2026
Title: SQL Engineering Connection Series
Time: 9:00AM – 10:00AM PT
Speakers:
Anitha Adusumilli / Sudhir Raparla - Modernizing SQL : End to End Migration journey in Azure Arc
Tejas Shah/Amit Khandelwal - New enhancements in SQL Linux
Description:
Hear the latest updates directly from the SQL engineering team, with two expert‑led sessions spotlighting new capabilities and practical guidance for Azure Data Community members.
During this session we will cover:

• Modernizing SQL : End to End Migration journey in Azure Arc
• New enhancements in SQL Linux

Location: ONLINE

Part 1 of a 4-part series

Introduction to Spark and Databricks
Description:
Apache Spark has become the de facto standard for large-scale data processing, powering everything from ETL pipelines to machine learning and real-time analytics. Databricks builds on Spark to provide a unified, collaborative, and production-ready platform for data engineering, analytics, and AI.
In this one-hour introductory session, we’ll demystify what Spark is, how it works, and why it has become so widely adopted. You’ll learn the core concepts behind Spark’s execution model, understand where it fits in a modern data architecture, and see how Databricks simplifies development, collaboration, and operations on top of Spark.
We’ll walk through typical use cases such as data preparation, large-scale analytics, and basic machine learning workflows, and show how Databricks notebooks and workflows make it easier for teams to be productive. By the end of the session, you’ll have a clear mental model of when and why to use Spark and Databricks, and what your next steps should be to get started.
This session is aimed at data professionals, developers, and architects who are new to Spark and Databricks or want a practical, high-level introduction—no prior experience required.

Part 2 of a 4-part series

Azure Databricks for Data Warehousing
Description:
You've seen the feature announcements—Databricks SQL Warehouse, AI/BI Dashboards, Lakeflow—but how do they actually work together and what are the strengths of each one?

With rapid evolution and even some name changes along the way, Databricks has transformed from a big data and AI/ML platform into a data warehousing powerhouse. This session cuts through the marketing buzz to deliver a practitioner's guide to the features, options, and best practices that are key to success in building your data warehouse on Databricks. Whether you're migrating from a traditional warehouse or building greenfield, you'll learn exactly which tools to reach for and when. We'll put particular focus on SQL Warehouse and AI/BI Dashboards for serving up your data to BI users.

What we'll cover:
Intelligent Data Warehousing
Ingest and Transform with Lakeflow
Query with Databricks SQL Warehouse
Visualize with AI/BI Dashboards

By the end of this talk, you'll have a clear decision framework for when to use each component and how your team can leverage Databricks SQL Warehouse and AI/BI capabilities to build a performant, cost-effective data warehouse for your organization!