A vector database is a specialized database optimized for storing and querying vector representations of data. Vectors represent data objects like documents, images, or user profiles as numeric arrays of hundreds or thousands of dimensions. This allows running mathematical similarity operations between objects.
Vector databases are well-suited for AI applications like search, recommendations, and natural language processing where finding related pieces of data is key. The vector representations can be easily indexed for fast similarity lookups and retrievals. Vector databases provide much higher performance and scalability over traditional relational databases for vector storage and operations.
A vector database ingests vectors and associated metadata, then indexes the vectors for efficient distance calculations like cosine similarity. This allows fast retrieval of vectors similar to a query vector.
They utilize indexing algorithms tailored for vector data, like graph-based indexes, tree-based indexes, to optimize storage and retrieval. Purpose-built vector serialization formats like Apache Arrow are used to store vectors efficiently.
Vector databases unlock large-scale ML use cases like visual search, document classification, product recommendations that rely on fast nearest neighbor retrieval across large vector datasets.
Use cases include image search based on extracted feature vectors, product recommendation models using customer vector embeddings, chatbots using vectored text corpora. Vector databases are key for productionalizing ML models by integrating them with graph databases and document stores.
Unlike generic NoSQL databases, vector databases are specialized for storing machine learning vectors and run vector-oriented operations like similarity search, clustering, classification needed for ML model serving.
Vector databases excel at fast similarity searches across vector datasets and are ideal for:
However, vector databases come with their own complexities around scale, tuning, integrations:
A graph database stores data in a graph structure with nodes, edges and properties to represent and query relationships between connected data entities.Read more ->
A search engine database is designed to store, index, and query full text content to enable fast text search and retrieval.Read more ->
Document store database manages collections of JSON, XML, or other hierarchical document formats, providing querying and indexing on document contents.Read more ->
The data ecosystem is rapidly expanding and fragmenting, posing integration challenges industry-wide. Many companies fall into a "data chasm", needing to abruptly scale their tools from 2-4 to 15-20, exacerbating complexity. Some organizations pioneered methodologies to cross this chasm and extract value. How can others navigate this data chasm?
Windowing queries in stream processing play a pivotal role in handling time-series data. This post unravels how to harness streaming-friendly window functions in queries with just using ANSI-SQL, emphasizing the importance of ordering for achieving optimal results in streaming datasets.
The Sliding Window Hash Join (SWHJ) algorithm joins potentially infinite streams while preserving the order by building hash tables incrementally, storing only relevant rows from the build side that fall within a sliding window, allowing efficient processing of streams without materializing all data.