Top Emerging Databases and Why They're Hot

🔄 Hybrid Transactional/Analytical Processing (HTAP) Databases

1. TiDB

• Overview: An open-source, distributed SQL database that supports HTAP workloads.
• Key Features:
  • Dual storage engines: TiKV for transactional (row-based) and TiFlash for analytical (column-based) workloads.
  • MySQL compatibility for seamless integration.
  • Horizontal scalability and strong consistency via the Raft consensus algorithm.
• Ideal For: Organizations requiring real-time analytics on transactional data without separate OLTP and OLAP systems.‍

2. YugabyteDB

• Overview: A high-performance, distributed SQL database designed for global, internet-scale applications.
• Key Features:
  • PostgreSQL compatibility with strong ACID compliance.
  • Supports multi-region and multi-cloud deployments.
  • Built-in change data capture (CDC) for real-time data streaming.
• Ideal For: Enterprises seeking a scalable, fault-tolerant database for both transactional and analytical workloads.‍

🤖 AI-Native Vector Databases

3. TurboPuffer

• Overview: A serverless vector and full-text search engine built on top of object storage.
• Key Features:
  • Cost-effective storage leveraging object storage with SSD caching.
  • High scalability with support for billions of vectors and millions of namespaces.
  • Optimized for hybrid search workloads combining vector and full-text search.
• Ideal For: Developers building AI applications requiring scalable and affordable vector search capabilities.

4. ChromaDB

• Overview: An open-source vector database tailored for large language model (LLM) applications.
• Key Features:
  • Embeddings, vector search, document storage, full-text search, and metadata filtering.
  • Supports both Python and JavaScript clients.
  • Designed for retrieval-augmented generation (RAG) systems.
• Ideal For: AI developers and researchers needing an integrated solution for managing embeddings and supporting LLMs.‍

🎯 Final Thoughts

The database landscape in 2025 is evolving to meet the demands of real-time analytics and AI-driven applications. HTAP databases like TiDB and YugabyteDB offer unified solutions for transactional and analytical workloads, eliminating the need for separate systems. Meanwhile, AI-native databases such as TurboPuffer and ChromaDB provide specialized capabilities for managing embeddings and supporting large language models. Selecting the right database depends on your specific use case, scalability requirements, and integration needs.

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What is an HTAP database and why is it important in 2025?

HTAP (Hybrid Transactional/Analytical Processing) databases enable organizations to run real-time analytics on transactional data without the need for separate OLTP and OLAP systems. In 2025, this unified approach reduces data duplication and latency, making it ideal for modern, data-intensive applications.

How do TiDB and YugabyteDB compare for HTAP workloads?

TiDB is known for its MySQL compatibility and dual-engine architecture, making it a solid choice for teams already using MySQL. YugabyteDB, on the other hand, offers PostgreSQL compatibility and excels in globally distributed deployments with features like change data capture and multi-region support.

What are AI-native vector databases and when should I use one?

AI-native vector databases, like TurboPuffer and ChromaDB, are purpose-built to store and search vector embeddings generated by AI models. These tools are ideal for applications such as semantic search, recommendation engines, and retrieval-augmented generation (RAG) systems used in LLM workflows.

How does Galaxy fit into this evolving database landscape?

Galaxy is not a database itself, but it complements HTAP and AI-native databases by serving as a modern SQL IDE with AI-powered query generation and collaboration features. It allows teams to query platforms like TiDB or YugabyteDB more effectively, visualize complex joins, and accelerate development across hybrid and AI-first environments.