A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market. With $31 million in venture funding and ...

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...

In this video from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases. In this session we will present an ...

For many years, and quite a long time ago in computer time, Hadoop was seen as the best way to store and analyze mountains of unstructured data. But then workloads and data began shifting to the ...

Maybe, if you need blazing performance extracting data and chewing on it from a relational database, it belongs in a cloud. Because for certain workloads, including vector search and retrieval ...

The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized ...

Graph databases, which explicitly express the connections between nodes, are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. That ...

When Expertise, Monitoring, Systems, and AI AlignBy Amir Malka, CEO, Bioforum The Data Masters | Trial oversight is shifting ...

Traditionally data acquisition has been the bottleneck for large scale proteomics. This has also remained one of the limitations in leveraging mass spectrometry within the clinic. PASEF and short ...