With Speeds Already Orders of Magnitude Faster Than Older Technologies, Quasardb Leaps Even Further Ahead.
QuasarDB announces a major new release of its high-performance distributed time series database: version 3.0. Built to handle extreme time series cases in financial applications, QuasarDB combines real-time analytics with long-term storage capable of handling millions of requests per second across billions of rows.
QuasarDB is particularly useful for quantitative analysts at hedge funds and investment banks that need instant access to significant amounts of data for harnessing insights in real-time. However, predicting storage needs and quickly accessing offline data becomes harder over time as the types and volumes of data expand at erratic rates, making storage needs unpredictable and even resulting in lost data.
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This problem is solved by QuasarDB version 3.0, which substantially improves ingestion and aggregation speeds so clients can store, access and analyze staggering amounts of data, using familiar tools such as Python, R, Excel, Spark, and SQL queries.
QuasarDB unifies new and old data for instant access and real-time analysis, which means that all data is useful at all time. When combined with the typically significant cost reduction in data storage, QuasarDB greatly increases ROI on data and data science.
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“In version 3.0, we focused on performance and data compression, but at the same time, we put a lot of effort into improving the user experience,” said Edouard Alligand, founder and CEO of QuasarDB. “We want our users to be immediately productive and comfortable, and version 3.0 delivers enhancements that make the product more intuitive to install and administer, while laying a structural foundation for user-defined functions in Python, which will be available soon.”
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