Kinetica, the database for time and space, has announced new industry recognitions and awards for business impact and technology innovation. The fastest-growing types of data this decade is real-time geospatial and time series data fueled by the proliferation of location-enriched sensor data. Recent award recognitions reflecting the unique characteristics of fast-moving spatio-temporal data and the innovations Kinetica is delivering to help customers leverage new design patterns, include:
InsideBIGDATA, “IMPACT 50 List for Q1 and Q2 2023” – Kinetica earned placement #24 and #25 for the first and second quarters of 2023, respectively, as one of the most important movers and shakers in the big data industry. Companies on the list have proven their relevance by how they are impacting the enterprise through leading edge products and services.
CRN, “The Coolest Database System Companies of the 2023 Big Data 100” – To make productive use of the ever-growing volumes of data, businesses and organizations need the right database technology to manage all that data and make it available for transactional and analytical applications. Kinetica is recognized as one of the coolest Database System Companies that caught CRN’s attention in 2023, offering a real-time, vectorized database for analyzing and observing time-series and spatial data.
Database Trends & Applications, “DBTA 100 2023: The Companies That Matter Most in Data” – The data landscape continues to increase in size and complexity and is more distributed than ever before. Database Trends & Applications named Kinetica to its DBTA 100 list which includes forward-thinking companies working to enable their customers’ data-driven future. Beyond the list, DBTA presents the View From the Top section where industry executives, including Nima Negahban, Kinetica’s Co-founder and CEO, highlights how they are helping customers handle their data management problems and maximize opportunities.
Kinetica uses native vectorization to significantly outperform other cloud analytic databases. In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This allows the query engine to process multiple data elements simultaneously, resulting in faster query execution and improved performance.