The Synnada glossary explains key terms and concepts in data science, machine learning, AI, and analytics. Learn about popular ML algorithms, data engineering, statistics and more from our comprehensive tech glossary.
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 key-value store is a type of NoSQL database optimized for storing, retrieving and managing associative arrays of key-value pairs.Read more ->
A data warehouse is a centralized data management system designed to enable business reporting, analytics, and data insights.Read more ->
A message broker is a software system that facilitates communications between distributed applications and services by transferring messages in a reliable and scalable manner.Read more ->
A time-series database (TSDB) is a database engineered and optimized for handling time-series data, where each data point contains a timestamp.Read more ->
A relational database is a type of database that stores and provides access to data according to relations between defined entities organized in tables.Read more ->
A data processing engine is a distributed software system designed for high-performance data transformation, analytics, and machine learning workloads on large volumes of data.Read more ->
A data lake is a scalable data repository that stores vast amounts of raw data in its native formats until needed.Read more ->
Document store database manages collections of JSON, XML, or other hierarchical document formats, providing querying and indexing on document contents.Read more ->
A spatial database is a database optimized to store, query and manipulate geographic information system (GIS) data like location coordinates, topology, and associated attributes.Read more ->
An RDF store is a graph database optimized for storing and querying RDF triple data to represent facts and relationships.Read more ->
A data orchestrator is a middleware tool that facilitates the automation of data flows between diverse systems such as data storage systems (e.g. databases), data processing engines (e.g. analytics engines) and APIs (e.g. SaaS platforms for data enrichment).Read more ->
A vector database is designed to efficiently store and query vector representations of data for applications like search, recommendations, and AI.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 ->
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.