[In-Person] September Java Meet-up at Sahaj Bangalore

We're excited to host another morning packed with technical insights in collaboration with MongoDB and Sahaj Bangalore.

Details

Location:

[Sahaj Software](sahaj.ai) 365, 3rd Floor, Sulochana Building, 1st Cross Road, 3rd Block, Santhosapuram, Koramangala 3 Block, Koramangala, Bengaluru, Karnataka 560034

*Note: *** Event from 9.30am to 2:00pm, Snacks Provided **\

-- RSVP ONLY IF HAVE YOU DECIDED TO JOIN -- Its a Free Event\, RSVP before 19th Sept 2026. NO Walk-INs allowed -- GOV ID CARD IS MANDATORY TO ENTER -- LAPTOP IS NOT MANDATORY\, BRING IT ONLY IF YOU WISH TO PRACTICE ALONGSIDE (FULLY CHARGED)

Agenda: ➡️ Registration Opens @ 9.30 am

➡️ Talk #1: Spring AI Vector Search with Java (Hands-on Workshop) Are you a Java Programmer looking to add AI-powered semantic search to your applications using MongoDB and Spring AI? Join us for a hands-on workshop where you’ll build vector search capabilities in Java using Spring AI together with MongoDB Atlas Vector Search.

We’ll start from familiar keyword search patterns and evolve them into meaning-based semantic search, so you can confidently move from traditional text queries to embedding-based similarity search in real-world applications.

Through lab exercises, you’ll connect a Java Spring Boot application to MongoDB, generate embeddings for application data, store vectors in MongoDB collections, and implement end-to-end semantic search workflows. You’ll learn how to shape vector search queries around application patterns, combine metadata filtering with similarity search, and understand how MongoDB’s flexible document model supports AI-ready data access in Java apps. This is a fully hands-on session, so you’ll need your laptop to participate.

By the end, you’ll walk away with practical code samples, a clearer mental model of how vector search works in Java, and skills to bring semantic search patterns back to your own projects.

Prerequisites

• Basic knowledge of Java • Familiarity with Spring Boot fundamentals • Preferred: access to JEDEE in [Instruqt](https://play.instruqt.com/mongo-devrel/invite/y2kiz8ww0ebq) • MongoDB Atlas cluster access provided during the workshop or personal Atlas account

Learning Objectives By the end of this session, attendees will be able to:

1. Implement semantic vector search in Java using Spring AI and MongoDB Atlas Vector Search, including generating embeddings and storing vectors for application data. 2. Execute similarity search queries using Spring AI’s VectorStore abstraction, combining vector similarity with metadata filtering to build practical semantic search workflows.

Time: 10.00 am to 11.30 am Speaker(s): [Sanjay Krishna](https://www.linkedin.com/in/sanjay-k-7973199a/) Sanjay K. is a Core Technical Support Engineer at MongoDB, specializing in scalable architecture, performance engineering, and systems troubleshooting.

➡️ Talk #2: Running a data platform at scale: Data Quality issues and how to spot them Data is the lifeblood of any data platform — but when that data is incomplete, inconsistent, or just plain wrong, everything downstream is impacted: reports, dashboards, and even ML models. At small scale a team might catch issues manually. Once a platform grows to 50+ daily jobs and complex pipelines, data quality has to be taken seriously — and having those practices in place before it scales is what keeps a platform healthy.

This talk shares lessons learned from running a large-scale data platform that handles terabytes of data every day: the practices that saved the team and the mistakes they didn't see coming. It explores why detecting data quality issues early is critical and what can go wrong when it's neglected, then shows how a library like Amazon Deequ enforces data sanity checks and how those checks slot into a pipeline.

Target Audience: Data Engineers - all experience levels, DS folks can also benefit from it. Time: 11.45 am to 12.30 pm Speaker(s): [Denny Sam](https://www.linkedin.com/in/itsdennian/) Denny has…