Kinetica, the database for time and space, is now easily accessible as a service on Microsoft Azure, providing companies with real-time contextual analysis and location information on huge data sets with reduced computing infrastructure and costs.
Companies in all industries rely on Kinetica’s vectorized database to analyze data from sensors and machines in real time where other technologies cannot compete. For example, one of the largest retailers in the world uses Kinetica to provide dynamic real-time inventory replenishment across its supply chain. several of the largest global telecommunications companies use Kinetica to streamline network planning with visualizations of coverage; and the Department of Defense uses Kinetica to monitor airborne threats over North America.
“Vectorization is ideal for IoT use cases where streamed geospatial and time series data are quickly merged with other static or steamy data. It has historically required exotic hardware and specialized – and rare – skills, making it inaccessible to all but the largest and best-funded organizations or government agencies, ”said Nima Negahban, Founder and CEO of Kinetica. “With Kinetica’s vectorized database, now available as a service on Microsoft Azure, that has all changed. Any company can leverage the power of Kinetica for IoT initiatives – and it can be deployed in minutes. “
Kinetica on Microsoft Azure is fully managed by Kinetica, integrated with Microsoft Azure monitoring and equipped with a modern user interface for ease of use. Deploy in minutes, streamline data ingestion, and deliver seamless analytics for exceptional time-to-value. Consumption-based pricing allows users to pay on demand and choose between vectorized CPU pricing and GPU pricing.
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BREAKTHROUGH THE SPACE-TIME BARRIER
According to IDC, IoT data is expected to reach 73 zettabytes by 2025, while a recent study by Deloitte estimates that 40% of IoT devices will be able to share their location in 2025, up from 10% in 2020. This makes data with both a time series and spatial component the fastest growing category of big data this decade. The takeover of geospatial data is beginning to gain more and more importance throughout the industry. New emerging high value use cases that leverage continuous metrics over time with geocoordinates include environment-based marketing, smart grid operations management, environmental remediation, contact tracing, spatial health outcomes, connected car services, fleet optimization, and others.
Data about time and space pose three fundamental challenges for companies:
- Sensor and machine data is extremely large and moving quickly compared to the first generation of large data sets, which were mostly human-generated interactions with the web
- The value of this data arises from the merging of data for the context through geographic and temporal links compared to traditional primary and foreign key relationships
- Machine learning insights with advanced geospatial and time series analysis capabilities
The current generation of massively parallel processing (MPP) databases for big data analytics, like BigQuery, Cassandra, and Snowflake, simply weren’t designed for the speed, unique data integration needs, and advanced spatial and temporal analysis of data over time and space . Previous approaches to unlocking the value of this type of data have failed, resulting in decisions not being made quickly enough, critical context missing, and under-optimized insights. In addition, the cost is excessive due to inefficiencies in both development effort and computational cost.
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VECTORIZING EXTREME TO MAINSTREAM
Data plane parallelism or vectorization speeds analysis exponentially by performing the same operation on different data sets at the same time for maximum performance and efficiency. Data plane parallelism is particularly useful for functions required to perform advanced calculations over time and space, such as window functions, predicate links, chart resolution, and others. Vectorization supports AI initiatives and high performance computing, but these vectorized computer processors have yet to enter the mainstream for analytical projects such as route optimization, real-time risk assessment, and visual mapping.
Kinetica’s new offering changes that. By leveraging the integrated vectorization capabilities of the latest generation of chips from Nvidia and Intel in the cloud and providing them as a service, Kinetica now offers orders of magnitude faster processing compared to traditional cloud databases.
For example, at a leading financial services company, a 700-node Spark cluster that ran queries in hours on 16 Kinetica nodes took seconds. At a top retailer, 100 nodes of Cassandra and Spark were consolidated into eight Kinetica nodes. A large pharmaceutical company achieved identical performance between an 88-node SQL-on-Hadoop cluster and a 6-node Kinetica cluster in Microsoft Azure.
“Kinetica’s fully vectorized database on the Microsoft Azure Marketplace clearly outperforms conventional cloud databases for big data analysis,” says Jeremy Rader, GM, Enterprise Strategy & Solutions for the Data Platforms Group at Intel, “and is doing it now the same speed as a GPU, but at a fraction of the cost by using parallelism on the data plane with the integrated Advanced Vector Extensions (AVX-512) of our latest scalable 3rd generation Intel® Xeon® processors. “
“We are pleased to be able to offer Kinetica as a fully vectorized database as-a-service on Microsoft Azure,” says Ramnik Gulati, Director Product Marketing Databases at Microsoft. “Kinetica is the database for space and time, and with its introduction to the growing Azure Marketplace ecosystem, it is now available to more customers and markets.”
“Accelerated computing is key to breakthroughs in machine learning, data science, visualization, simulation, and computational design,” said Scott McClellan, senior director at NVIDIA. “Kinetica’s new As-a-Service offering on Microsoft Azure enables companies to accelerate the databases that support their work with NVIDIA GPUs.”
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