Data Engineering

Practical AI for Power BI Developers A year ago, “agentic AI” was mostly hype for Power BI teams. Today, it deserves your undivided attention. For Power BI pros, there is now a real opportunity to reduce repetitive development work, accelerate delivery, and help developers do more, but only when strong DataOps practices are in place to make AI workflows effective. This session is a no-nonsense introduction to effective AI patterns for Power BI and Fabric development. Along the way, we will make sense of the growing pile of terminology, including skills, plugins, hooks, and MCP. You will see examples of how modern AI tooling can help with development tasks across Power BI and Fabric, along with the prerequisites, guardrails, and DataOps principles needed to use it responsibly. Whether you're burned out on AI hype or already using Copilot CLI daily, this session will show you the foundations that are finally making AI-assisted development genuinely useful.

**Total Eclipse of the Chart: School Redistricting Dashboards** In this presentation, we will explore the utilization of maps, sets, and SQL within a Tableau dashboard to help provide “what-if” analyses generated through the use of a mapped dashboard, providing different scenarios and the potential implications of these. While in this use case we are focused on a large school district, the concepts of utilizing existing data to explore the future impacts of modifying different geographic boundaries can be useful to many industries, balancing the mix of maps with visual analytics. **About Our Speaker** Robert Kramer is the Data Systems and State Reporting Coordinator at South Western City Schools in Grove City. He has worked there for 17 years, serving previously in the roles of Data Analyst, Programmer, and Operations Coordinator. Prior to working at South Western, he worked as a Systems Engineer at Pinnacle Data Systems in Groveport. He graduated in 2005 from The University of Toledo with his Bachelor’s in Computer Science and Engineering, and a minor in Mathematics. Robert resides in Obetz with his wife, and their 15 and 12 year-old daughters. He was also just elected to his 3rd four-year term on the Obetz City Council. Thanks to our 2026 sponsors: [Clarivoy](https://www.clarivoy.com), [What Box Consulting Group](https://www.whatboxconsultinggroup.com), [Conductrics](https://www.conductrics.com), and [Piwik PRO](https://piwik.pro) More info at [cbusdaw.com](https://cbusdaw.com)

Customer identity resolution becomes increasingly complex as organizations scale across multiple systems, regions, and data formats. Traditional rule-based approaches often fail to keep up with data variability, require constant manual tuning, and struggle with real-time processing needs. This session presents a practical approach to building a scalable identity resolution pipeline using AWS services and modern AI techniques. The architecture combines data ingestion through Amazon S3 and AWS Glue, transformation pipelines using Spark on EMR, and machine learning models deployed via SageMaker for entity matching and standardization. Graph-based relationship modeling is implemented using Amazon Neptune to improve resolution accuracy by incorporating household and shared attribute context. We will walk through how machine learning models can be used for name and address normalization, how intelligent blocking strategies improve matching efficiency, and how feedback loops can be introduced to continuously improve accuracy. The session also highlights how serverless components such as AWS Lambda can be used for orchestration and real-time processing. **SPEAKER BIO** Mosaic Syed is a Senior Data Engineering and Cloud Solutions Architect with over 20 years of experience designing and delivering scalable, secure, and high-performance data solutions across global enterprise environments. https://www.linkedin.com/in/mosaic-basha-syed-92300856 **CALL FOR SPEAKERS** Learn more: [https://www.awscolumbus.com/get-involved/](https://www.awscolumbus.com/get-involved/) **THANK YOU** *VEEAM* for hosting our meetup! To learn more about *Veeam*, please visit their website: [https://www.veeam.com/](https://www.veeam.com/) **DIRECTIONS** 8800 Lyra Dr #450 · Columbus, OH go to 4th floor. **Want to sponsor the pizza and/or bar tab?** Please contact me if you would like to sponsor this meetup's pizza and/or bar tab: angelo@mandato.com

Join us for a practical, beginner-friendly guide to choosing the right large language model. We’ll compare major models like ChatGPT, Claude, Gemini, and Llama, talk about when to use hosted APIs versus local models, and break down the tradeoffs around cost, speed, quality, privacy, context windows, coding ability, and reliability. You’ll leave with a clearer mental model for picking an LLM based on your actual use case instead of hype, benchmarks, or brand names. No deep AI background required. LOGISTICS AND PARKING: The talk starts at 7:00 PM. The first half hour is reserved for everyone to get set up and mingle. Free pizza and drinks! The cheapest parking option is to find street parking, which will only cost you a few bucks. Otherwise, park in the nearby veteran's museum lot for $8. It's highly recommended you avoid the nearby $15 garage parking.

Join the Columbus AI community for our quarterly gathering — a casual, community-focused evening where everyone has a chance to share, learn, and connect. These open mic–style events give anyone in the community up to **5 minutes** to present a project, share a tool, pose a question, or offer a perspective on the evolving AI space. No slides required — just a welcoming space to exchange ideas and keep the local AI conversation moving. If you’d like to take the stage, message \*\*Chris (the organizer)\*\*with a **title and short description** of what you’d like to share. Whether you’re deep in the field or just getting curious, come connect with others building and exploring AI in Columbus. Sponsored by [Transform Labs](https://www.linkedin.com/company/transformlabs/) Sign up also accessible via [Transform Labs Luma](https://luma.com/transformlabshq)

**We'll have two meetings covering AI - this second one is for Positron** We will review which AI features are linked in for Positron, and how to use them. **Source documents:** the repository is public, at: https://github.com/BarryDeCicco/AARUG_2026_04_09_AI_In_Positron You can download documents, or clone/fork the repository. **Location:** The meetup will be at SPARK's ([https://annarborusa.org/](https://annarborusa.org/)) Ann Arbor site: [SPARK HQ (Ann Arbor)](https://urldefense.com/v3/__https://www.google.com/maps/place/Ann*Arbor*SPARK*Headquarters/@42.2792515,-83.7447708,17z/data=!3m1!4b1!4m5!3m4!1s0x883cae3effe193cb:0x5296a53db2a282bf!8m2!3d42.2792515!4d-83.7447708?hl=en-US__;Kysr!!HXCxUKc!3EEdBNXRJKknP6LCGkZetSuEsdtChFojQnOVitFUC5C0fyilqXEbiMstT9ajBR3Cw-55qoFkrElCjQvjMdTxUw$). There is on street parking, and parking at local structures. **This will be a hybrid meeting - the Zoom session starts at 6:30 PM.** **Time:** The doors will be open at 6:00, with pizza and beverages provided. We will have a meet-and-greet-and-pizza session, and then at 6:30 we'll have a presentation. **Zoom information:** https://us06web.zoom.us/j/6658850479?pwd=Snd1UmZTT3pjZktENlczUXh4SERwUT09&omn=89012971377 Passcode: 940392 **If you can't get in, please call me at: 734 223-3307** **The github repository is at: https://github.com/BarryDeCicco/AARUG_2026_04_09_AI_In_Positron**

**Improving Office in Franklinton** Physical location: Improving Office 330 Rush Alley Suite #150 Columbus, OH 43215 Schedule: 6:00 p.m.: Socialize, eat, and drink. Improving will be providing pizza and beverages. 6:30 to 8:00 pm. Main meeting and presentation(s). Topic: This month John Lairson will share a notebook describing the Alpaca (Paper) Trading API and discuss different algorithms for evaluating stock trades. We meet on the last Monday of each Month. Presentations are given by members and friends of this group. If you would like to do a presentation (small or large) on a python topic, please contact Central OH Python at centralohpython@gmail.com