Matt Aslett's Analyst Perspectives

Rockset Offers Cloud-Based Real-Time Analytics

Posted by Matt Aslett on Aug 30, 2022 3:00:00 AM

I have written recently about increased demand for data-intensive applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. Almost one-quarter of respondents (22%) to Ventana Research’s Analytics and Data Benchmark Research are currently analyzing data in real time, with an additional 10% analyzing data every hour. There are multiple data platform approaches to delivering real-time data processing and analytics and more agile data pipelines. These include the use of streaming and event data processing, as well as the use of hybrid data processing to enable analytics to be performed on application data within operational data platforms. Another approach, favored by a group of emerging vendors such as Rockset, is to develop these data-intensive applications on a specialist, real-time analytic data platform specifically designed to meet the performance and agility requirements of data-intensive applications.

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Topics: Cloud Computing, Data, Streaming Analytics, Analytics & Data, Streaming Data & Events, analytic data platforms, operational data plaftforms

Neo4j Expands Data Science Focus with New Managed Service

Posted by Matt Aslett on Aug 3, 2022 3:30:00 AM

I recently explained how emerging application requirements were expanding the range of use cases for NoSQL databases, increasing adoption based on the availability of enhanced functionality. These intelligent applications require a close relationship between operational data platforms and the output of data science and machine learning projects. This ensures that machine learning and predictive analytics initiatives are not only developed and trained based on the relationships inherent in operational applications, but also that the resulting intelligence is incorporated into the operational application in real time to support capabilities such as personalization, recommendations and fraud detection. Graph databases already support operational use cases such as social media, fraud detection, customer experience management and recommendation engines. Graph database vendors such as Neo4j are increasingly focused on the role that graph databases can play in supporting data scientists, enabling them to develop, train and run algorithms and machine learning models on graph data in the graph database, rather than extracting it into a separate environment.

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Topics: Business Intelligence, Data, AI and Machine Learning, analytic data platforms, Operational Data Platforms

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