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Elastic Minneapolis User Group

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About us

We’re delighted to have you join the Elastic global community!

This group is for anyone interested in making data usable in real-time and at scale for use cases like security, observability, enterprise search, and many more, using our Elasticsearch, Kibana, Logstash, and Beats products (https://www.elastic.co/products/).

Speak at a meetup:

The Community wants to hear from you! Present about your Elastic Stack stories, be it a 5-15 minute lightning talk or a detailed 25-45 minute technical presentation with Q&A. Our Speaker Guide is full of tips on giving a stellar presentation. If you’re interested, visit the elastic/call-for-meetups repo or send us an email at meetups@elastic.co.

Elastic Community Youtube Channel

All meetups are recorded and uploaded to YouTube within a few days of the event. Subscribe here to stay up to date!

Local community organizers wanted!

We’re always on the lookout for committed local organizers who will help the group thrive. If this sounds interesting to you, the Community Team at Elastic is here to support you! Email us at meetups@elastic.co.

We know that people enjoy giving back to the communities they care about, so we’ve created an Organizer guide to help you get started on your path to leading an Elastic user group.

Learn more about Elastic

Elastic Contributor Program: https://www.elastic.co/community/contributor

Elastic Community: https://www.elastic.co/community

Community Newsletter: https://www.elastic.co/community/newsletter

Discussion Forums: https://discuss.elastic.co/

Elastic Cloud Free Trial: https://www.elastic.co/cloud/elasticsearch-service/signup

Code of Conduct

This Meetup community adheres to the Elastic Community Code of Conduct. Attendance to events run as part of this Meetup group means you agree to be an awesome human and engage by these rules.

Upcoming events

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Streaming Context: Powering Semantic Search with Kafka and Elasticsearch
Wed, May 27 · 5:30 PM CDT
Improving, 3033 Excelsior Blvd #180, Minneapolis, MN, US
The Elastic Developer Relations team will be in town sponsoring Open Source North and will be hosting a joint meetup with the Twin Cities Apache Kafka® Meetup by Confluent ahead of the conference on Wednesday, May 27

We'll have presentations from Justin Castilla (Sr. Developer Advocate at Elastic) and Sandon Jacobs (Confluent), followed by networking, pizza, and refreshments.

Thank you to Improving for hosting our meetup. Improving is a modern digital services consulting firm focused on solving business problems with innovative technical solutions.

📅 Date and Time:
Wednesday, May 27th, from 5:30-7:30 pm CDT

📍Location:
Improving Minneapolis: 3033 Excelsior Blvd #180, Minneapolis, MN 55416

🚗 Parking:
Details coming soon!

🪧 Arrival Instructions:
Details coming soon!

⚡️ Interested in giving a talk? ⚡️
Are you interested in presenting your Elastic use case? We welcome 5-10 minute lightning talks, 45-minute deep dives, and everything in between. If you're interested, please send us an email at meetups@elastic.co.

📝 Agenda:
- 5:30 pm: Doors open
- 6:00 pm: Streaming Context: Powering Semantic Search with Kafka and Elasticsearch - Sandon Jacobs (Confluent)
- 6:30 pm: Talk # 2 - Justin Castilla (Sr. Developer Advocate at Elastic)
- 7:00-7:30 pm: Networking
💭 Talk Abstracts:
Streaming Context: Powering Semantic Search with Kafka and Elasticsearch - Sandon Jacobs (Confluent)
As the de facto platform for event streaming, we’ve seen how data from Apache Kafka can easily empower basic keyword search. But let's be real, modern semantic search applications need context to interpret the real meaning and intent of a user's query. We could wait around for the next batch job to kick off and provide that context... or we could serve it up in near real-time right from our data streams. In this talk, we'll discuss how to leverage data streams from Kafka to power much smarter semantic search with Elasticsearch. We’ll follow a simple event from a Kafka topic and see how it transforms into a powerful vector embedding. Then, we’ll see this whole flow in action using a k-Nearest Neighbor search to instantly find similar items in milliseconds. While my code examples will be in Java or Kotlin using the Spring Boot and Spring AI ecosystem, FEAR NOT! The core concepts—getting data into a vector format via a stream—are totally language agnostic. Join us to grab a few practical ideas you can take and use in your next project!
2 attendees