An RDF store, also known as a triplestore, is a type of graph database specialized for storing and querying Resource Description Framework (RDF) data. RDF represents facts as subject-predicate-object triples that form a graph structure.
RDF stores provide mechanisms for storing, indexing, and querying collections of RDF triples to support knowledge representation and reasoning. They allow running SPARQL queries to retrieve facts connected by relationships. RDF stores are commonly used for knowledge graphs, semantic web applications, linked open data, and metadata management.
A vector database is designed to efficiently store and query vector representations of data for applications like search, recommendations, and AI.Read more ->
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 ->
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.