Are you struggling with ingesting and storing an enormous amount of time-series data?
Does data lag prevent you from generating timely, actionable insights?
Do you need real-time data analytics such as Fast Fourier transform or orderbook rebuilding?
If you encounter any of those issues mentioned above, Quasar is your ideal real-time data platform.
Quasar captures, normalizes, transforms, and stores an unlimited volume of events in the most efficient way possible with low latency, delivering real-time insights into an unlimited volume of time-series data.
Timeseries data is typically large as it is cumulative.
When data volumes increase, there are two main approaches: the data lake approach and the data warehouse approach.
Both approaches have inherent limitations and ask you to make tradeoffs either with ingestion (in volume or lag) or querying (speed or capabilities).
Quasar has been built from the ground up to be without compromises and deliver on:
In certain use cases, the volume of data is so large that you need to aggregate it as it comes because response time matters the most.
For example, you might be interested only in the max value of a sensor over the last minute and don't need to keep multiple updates per second, or you only need an update on the location of a cell phone user every five minutes.
Doing this used to require using multiple products working in concert with varying degrees of success and performance.
Quasar can aggregate data in memory as it comes and store the result on disk, resulting in several orders of magnitude speed and storage gains.
And if you need to keep the raw data for reference, Quasar can do that too.
Quasar highly optimizes your storage capacity by combining extremely efficient compression and optimized I/O.
Quasar's Delta 4C compression suite. Delta 4C features adaptive, lossless compression. Compression is faster than writing and can yield tremendous disk space savings.
Are you making incorrect decisions because of low-resolution data?
In predictive maintenance, for example, you want to be able to detect false brinelling caused by small movements in the bearing during startup and shutdown. This requires capturing the full waveform of vibration data, which is tens of thousands of times larger than traditional.
In finance, level II market data, which is several orders of magnitude bigger than tick data, enables traders to understand better the supply and demand dynamics of a given security, such as market depth, sentiment, and order imbalance.
However, working on the full-resolution picture used to require a significant investment in technology and resources, limiting its availability or practicality.
Quasar has been designed to solve this problem in a comprehensive, fully integrated platform, drastically lowering the barrier to entry.
Quasar's brain is QuasarDB, a high-performance, distributed, column-oriented timeseries database management system designed from the ground up to deliver real-time on petascale use cases.
Learn more about QuasarDB in our technological white paper.
Quasar has a unique data model based on tags, giving you raw speed and extreme flexibility.
Time-series data is organized in tables. Tables are so inexpensive that you can have millions of them. Use SQL to query them.
Tag your tables, query based on tags. Or tables. Or both. The possibilities are endless.
Tags can be changed at any time and can be recursive. Reorganizing data is instant and effortless.
Quasar writing capabilities scale linearly with the number of nodes in the cluster.
Supports the most exotic data source. Quasar has a flexible API that accommodates even the rarest data source.
You need more than just a database.
Queries often come short when it comes to getting valuable insights from your data, however, these transformations can be very costly and require moving the data out of your platform.
That's why we infused Quasar with powerful analytical capabilities.
Quasar goes beyond trivial data transformations and has extended windowing capabilities, pivoting, and domain-specific capabilities such as FFT and order book rebuilding. Native time-series joins such as ASOF joins are supported to align sensors timestamps.
Quasar has a dashboard framework that allows for constructing bespoke web dashboards.
The Quasar framework is perfect for a project that has precise requirements in terms of visualization and reporting.
Quasar has numerous dedicated connectors for users already happy with an existing visualization framework. It integrates perfectly with Microsoft Excel, Microsoft Power BI, Tableau, SAS Vaya, Grafana, and many more!
Integrating with an existing analytics toolset is the best way to ensure rapid adoption and a smooth transition.
Quasar is available on-premises, on the edge, and in the cloud. Learn more about our integrations in the partner's page.
Quasar supports Intel x64 and ARM64 architecture for server deployments. Attached or detached storage architectures are both supported.
Docker and Kubernetes are fully supported.
Quasar supports Microsoft Azure, Amazon Web Service, and Google Cloud Platform.
You can deploy Quasar on embedded devices for scenarios requiring a data infrastructure close to the source. ARM32, ARM64, and Intel-compatible architectures are fully supported.
