Software Alternatives, Accelerators & Startups

Apache Hive VS Qdrant

Compare Apache Hive VS Qdrant and see what are their differences

Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

Qdrant logo 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/
  • Apache Hive Landing page
    Landing page //
    2023-01-13
  • Qdrant Landing page
    Landing page //
    2023-12-20

Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.

Apache Hive

Pricing URL
-
$ Details
Platforms
-
Release Date
-

Qdrant

$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May

Apache Hive features and specs

No features have been listed yet.

Qdrant features and specs

  • Advanced Filtering: Yes
  • On-disc Storage: Yes
  • Scalar Quantization: Yes
  • Product Quantization: Yes
  • Binary Quantization: Yes
  • Sparse Vectors: Yes
  • Hybrid Search: Yes
  • Discovery API: Yes
  • Recommendation API: Yes

Apache Hive videos

Hive vs Impala - Comparing Apache Hive vs Apache Impala

Qdrant videos

No Qdrant videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Apache Hive and Qdrant)
Databases
52 52%
48% 48
Search Engine
0 0%
100% 100
Big Data
100 100%
0% 0
Relational Databases
100 100%
0% 0

Questions and Answers

As answered by people managing Apache Hive and Qdrant.

Why should a person choose your product over its competitors?

Qdrant's answer:

Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

What makes your product unique?

Qdrant's answer:

Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

Which are the primary technologies used for building your product?

Qdrant's answer:

Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

User comments

Share your experience with using Apache Hive and Qdrant. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Qdrant should be more popular than Apache Hive. It has been mentiond 39 times since March 2021. 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.

Apache Hive mentions (8)

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Qdrant mentions (39)

  • How to Build a Chat App with Your Postgres Data using Agent Cloud
    AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 23 days ago
  • Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
    Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / about 1 month ago
  • Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
    I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / about 1 month ago
  • Ask HN: Has Anyone Trained a personal LLM using their personal notes?
    I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / 2 months ago
  • Open-source Rust-based RAG
    There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 3 months ago
View more

What are some alternatives?

When comparing Apache Hive and Qdrant, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Apache Doris - Apache Doris is an open-source real-time data warehouse for big data analytics.

Weaviate - Welcome to Weaviate

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs