How to Build Modern, Smart Search Without Leaving MongoDB
Key Speaker
Technical Lead, Ksolves India Limited
He is a Technical Lead with hands-on experience in MongoDB Atlas Search, full-text search architecture, search relevance engineering, and building scalable, production-ready search experiences for modern applications.
In this session, Romil Bhawsar, Technical Lead at Ksolves India Limited, addresses a challenge that many engineering teams face when their applications begin to outgrow basic database queries: users expect fast, relevant, and flexible search experiences, but adding a dedicated search platform like Elasticsearch introduces separate infrastructure to manage, synchronization pipelines to maintain, and operational complexity that grows with every deployment.
For teams already building on MongoDB, there is a better path. MongoDB Atlas Search brings native full-text search capabilities directly into Atlas, allowing teams to build powerful search experiences without leaving their existing data platform or introducing a separate search system. The result is less infrastructure to manage, no synchronization lag between the database and the search index, and a single platform that handles both data storage and search.
This webinar covers Atlas Search from the ground up, moving through the concepts that matter most in practice. Romil walks through dynamic and static index mappings, how analyzers transform and optimize searchable text, and the full range of search operators including text, compound, autocomplete, phrase, wildcard, and range queries. The session also covers how to improve search relevance through scoring and boosting, how to build faceted search experiences with filters and result counts, how to implement typo-tolerant search and autocomplete for production applications, and the best practices teams need to keep Atlas Search indexes, analyzers, and monitoring performing well at scale.
If you are working with MongoDB or building search-driven applications, this session provides practical insights into creating smarter search experiences without the overhead of a separate search infrastructure.
Key Takeaways
- How to build native full-text search directly within MongoDB Atlas without a separate search platform.
- Understanding dynamic and static search index mappings and when to use each.
- Using analyzers to transform, normalize, and optimize searchable text for better results.
- Working with text, compound, autocomplete, phrase, wildcard, and range search operators.
- Improving search relevance through scoring and boosting strategies.
- Building faceted search experiences with filters and result counts for modern applications.
- Implementing typo-tolerant search and autocomplete that works reliably in production.
- Production best practices for Atlas Search indexes, analyzers, filters, and monitoring.
