Deephaven Unveils New, Productivity-Boosting 'Streaming-Tables' Data System
- Written by Newsfile
New York, New York--(Newsfile Corp. - September 2, 2022) - Deephaven[1], a data software company, has introduced an innovative technology designed to enhance company productivity. It has invented a new data engine and communication system that enables users to address modern use cases related to real-time data. This, in turn, aids businesses in their analyses, predictions, operational workloads, and AI integrations.
Historically, the data-infrastructure industry has revolved mainly around static data. The industry's term is "batch". Most recent innovations in the space have related to faster processing of this batch data. Software providers have improved their processing capabilities, but importantly, the cloud has allowed data-driven workloads to be split up into many parts, with many servers brought to bear simultaneously. (More units each doing less work speeds things up.)
Fundamental to these batch workloads is the 'data table'. This construct is familiar to most knowledge workers. Excel, relational databases that use SQL, and data science structures in languages like R and Python are all data tables. Tables are cells of data structured in rows and columns. They're intuitive.
To complement static batch data, systems also rely on 'event streams'. The event-stream concept is natural. Events occur one after the other. They are written down, moved between machines, and processed as they arrive. Clicks on websites, heartbeats, and new social posts are all events that can be processed in this way.
Deephaven's invention is the 'streaming table'. As the name suggests it combines attributes natural to both 'data tables' and 'event streams'.
Streaming tables empower users to work on tables in a familiar way, but the tables can change underneath them. Enabling users to work on 'tables that change' is Deephaven's superpower.
Real-time data is valuable. It drives business operations, capital markets, recommendation algorithms, industrial monitoring, social media and logistics. It's a vital part of the story for the future of health care. Enterprises, governments, and communities depend on automation and AI, which are, in turn, propelled by real-time data.
The shift from static to real-time data is already gaining speed; experts predict that by 2025, almost 30% of data[2] generated will be the latter. Almost 90% of all data has been created in the past two years[3].
At the core, Deephaven's streaming tables focus on changes in data (i.e. 'table changes') rather than snapshots of full row-and-column presentations. This allows for faster computations, fewer data moved across networks and the support of compelling experiences for human eyeballs and fingers.
Crucially, all of the components of Deephaven's unique software are open-source, which means it is available to all for free and can be tweaked and edited by anybody across the world. The company is committed to innovation and evolution and knows the best way forward on these themes is to embrace the collaboration of open communities.
"Deephaven exists to make teams productive with data - in real-time and otherwise. Streaming tables is the pathway to achieving this. We compare this invention to snowboarding in the early 1980s. The snowboard combined features of skiing and surfing to produce a completely new sport. In much the same way, we have integrated the best aspects of 'data tables' and 'event streams' to come up with a new and powerful way to work with real-time data," concludes Peter Goddard, CEO of Deephaven.
MEDIA CONTACT: Name: Peter Goddard Email: pgoddard@deephaven.io[4]
To view the source version of this press release, please visit https://www.newsfilecorp.com/release/135839[5]
References
- ^ Deephaven (www.newsfilecorp.com)
- ^ almost 30% of data (www.newsfilecorp.com)
- ^ lmost 90% of all data has been created in the past two years (www.newsfilecorp.com)
- ^ pgoddard@deephaven.io (www.newsfilecorp.com)
- ^ https://www.newsfilecorp.com/release/135839 (www.newsfilecorp.com)