Apache Kafka

The Fellowship of the Star Schema: Transforming OLTP Data for Power BI One does not simply build reports on OLTP data. Join us on an epic journey from the depths of raw, normalized tables to the shining halls of a well-modeled star schema fit for Power BI greatness. We will demystify the differences between OLTP and OLAP, walk through star schema vs snowflake (and why Power BI has a clear favorite), and show you the compression differences between normalized and columnar storage that will make you never look at an OLTP model the same way again. We will cover the different types of dimensions, when to use them, and how to handle Slowly Changing Dimensions without losing your mind. You will leave knowing exactly how to identify a bad model, how to convert OLTP data into a clean star schema, and how to forge relationships that rule them all. Whether you are a data wizard, a curious hobbit, or somewhere in between, your models will never be the same.

Salsamante Dance Academy will be at Swerve Every Tuesday Night to share the Rhythm & Energy of Bachata & Salsa. These are Beginner Level lessons to get you comfortable and understand the two dances. Spread the Good News to all. Swerve Dance & Fitness Complex 640 Lakeview Plaza Blvd A, Worthington, OH 43085 Bachata 7pm-8pm Salsa 8pm-9pm $15 - One Lesson $20 - Both Lessons

Come chat about Atlassian tools and best practices at the Nationwide Sunny Street Cafe and Breakfast is on Atlassian! Let's get the day started off with breakfast with your ACE Leaders! We're always excited to talk about Atlassian Products, share our knowledge and hand out swag! Come chat with us about new product news and anything you are working on within the Atlassian product stack. This is a great opportunity to come see what we are all about, hear new business news and share with other Atlassian users your questions and experiences. We'd love to see new and old friends come out to chat and learn about Atlassian! Agenda --- Moderators Kimberly Deal - Columbus Atlassian User Group Leader Kevin Stanley - Columbus Atlassian User Group Leader Hosted By Kimberly Deal, Atlassian User Group Leader Kevin Stanley, Chief Everything Greg Sprowls, Agility Lead --- Global Partner Atlassian (http://atlassian.com) Millions of users globally rely on Atlassian products every day for improving software development, project management, collaboration, and code quality. Partner Atlassian --- For over a decade, Atlassian customers have come together to network, share ideas, solve problems, and find new ways to use Atlassian products. Today, more than 15,000 people take part in Atlassian community events in more than 30 countries. --- Complete your event RSVP here: https://ace.atlassian.com/events/details/atlassian-columbus-presents-rise-and-shine-with-atlassian-downtownnationwide-22/.

**First meetup of the Columbus AI Engineering group.** We’ll kick things off with a practical, beginner-friendly discussion on: **How LLMs like ChatGPT actually work — and how to build real applications with them.** We’ll cover: * what’s actually happening under the hood (at a high level) * why LLMs behave the way they do (hallucinations, prompt sensitivity, etc.) * how people are building real systems with them today * where things break in practice This will be a **short, informal talk (\~15–20 min)** followed by open discussion and Q&A. Whether you’re an engineer, builder, or just curious about how modern AI systems work, you’re welcome to join. No slides, no fluff — just real conversation about real systems. We’ll hang out afterward and get to know each other.

This presentation delves into the potential of integrating LLMs with Apache Spark and Apache Iceberg as part of a Big Data to AI foundational architecture. In this session we’ll explore the potential of combining Iceberg, Spark and LLMs to give you a real world AI architecture that uses your data. We'll build an AI application that allows users to perform data queries and extract insights from massive datasets using natural language. We'll start with understanding the structure and architecture of a large dataset. Then we'll look at options for querying the dataset using Apache Spark and Trino. Finally, we'll use an LLM to query the dataset using natural language. We'll also look at other uses of LLMs as part of an overall solution, and explore the differences between different LLMs. We’ll also discuss where event streaming (Kafka and Flink) fit into this architecture. The design of this architecture is meant to be flexible and give your dev team the ability to choose different technologies for the processing and querying. I’ll leave you with a CONCRETE example that you can run on your laptop and explore the possibilities. Again, this will be an example of a real-world application; the dataset used will be for home sales data for the last 15 years. We will use these technologies: * Apache Iceberg * Apache Spark * Spring AI * Ollama * Various LLMs

Want to be a speaker? submit your talk to our Call for Presenters!!! https://sessionize.com/azure-cbus-2026/