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Weaviate might be a bit more popular than QuestDB. We know about 28 links to it since March 2021 and only 19 links to QuestDB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models. - Source: dev.to / about 2 months ago
But of course, I want to run a QuestDB instance on my node, which uses two additional TCP ports for Influx Line Protocol (ILP) and Pgwire communication with the database. So how can I expose these extra ports on my node and route traffic to the QuestDB container running inside of k3s? - Source: dev.to / 5 months ago
In this post, I will detail a way in which I recently used annotations while writing an operator for my company's product, QuestDB. Hopefully this will give you an idea of how you can incorporate annotations into your own operators to harness their full potential. - Source: dev.to / 6 months ago
QuestDB is an open source, high performance time series database. With its massive ingestion throughput speeds and cost effective operation, QuestDB reduces infrastructure costs and helps you overcome tricky ingestion bottlenecks. Thanks for reading! - Source: dev.to / 6 months ago
Want to know more? Check out the QuestDB website and the QuestDB documentation. - Source: dev.to / 8 months ago
Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 3 months ago
Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 4 months ago
Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 4 months ago
A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 5 months ago
To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 6 months ago
TimescaleDB - TimescaleDB is a time-series SQL database providing fast analytics, scalability, with automated data management on a proven storage engine.
Qdrant - Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
InfluxData - Scalable datastore for metrics, events, and real-time analytics.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs