It's a Really Big Picture - See the Whole Thing
The financial services industry sits at the center of an almost unimaginably large and dynamic set of interactions. Billions of individuals and millions of organizations interact daily with each other and with trillions of business processes, goods, services, and a myriad of financial activities that facilitate these interactions.
Gathering accurate, timely, and cost-effective insights from the data describing these activities is critical; yet doing so for even a tiny fraction of the trillions of relevant timelines and zettabytes of data is rapidly moving beyond the capabilities of traditional technologies.
Craxel's Black Forest quickly and efficiently makes data available for complex query of timelines of data and the relationships they contain at extraordinary scale. This makes it not only possible, but practical and cost effective to decouple your business from the limitations of a generation of computer science that cannot extract the value you must have from your data.
Delivering Speed at Any Scale
In this era of exponential data growth, we need a faster and more scalable approach to extract the immense value of data and artificial intelligence (AI).
With Craxel, the way insight is extracted from data is fundamentally different:
- Data is organized as time series graphs
- Records are indexed immediately as they are ingested and readily available for use
- Sub-second query times over billions of records while using 90% fewer servers
- Time to insight is pivotally reduced for ALL types of data
Unprecedented Price/Performance for Financial Market Data Analytics At Any Scale
Underlying financial markets are millions of interconnected timelines that describe the price evolution of securities in rich detail. These timelines are influenced by complex relationships with correlated assets and with a wide range of external factors that provide important context about the conditions in which they evolved.
Gathering actionable insights from all of this is already notoriously challenging, yet new factors are making it even more difficult to find signals within the noise. Alternative data sources are revealing novel interactions with exogenous events. Marketplaces are developing new tradeable assets and data products. Market participants are generating orders and executing transactions at unprecedented and accelerating scales. The information needed to generate attractive risk adjusted returns, understand portfolio risk, and ensure appropriate levels of oversight, compliance, and customer service exists; but it is increasingly lost in a flood of complex data.
Craxel has applied its breakthroughs in multi-dimensional constant time algorithms so that Black Forest supports the creation of massively scalable, highly efficient graphs of interconnected timelines. These time series graphs provide accurate, efficient, and fast access to any source or type of data at virtually any scale while ensuring that the relationships contained can be easily represented and understood. This offers a simple, yet incredibly effective, way to turn an accelerating challenge into a sustained advantage.
Time Series Graph: Connecting the Dots at Any Scale
Black Forest facilitates connecting the dots in massive quantities of information by organizing it as time series graphs. A single timeline in a time series graph can contain records of many different types. This allows everything about a given person, place, or thing to be stored and accessed together. Instead of keeping all of those records in different stove pipes or even different tables, they can now all be co-located.
Because each timeline of the time series graph can also store relationships with other timelines, humans and algorithms can quickly and efficiently extract insight by "walking the graph."
Craxel's breakthrough algorithms enable massive quantities of information to be organized as these time series graphs.
- Organize massive quantities of data at line speed in a way that facilitates connecting the dots
- Fuse multiple data types and data sources into a single time series graph as the data is ingested
- Store petabyte-scale time series graphs in hyperscale storage
- Queries across trillions of data points are extraordinarily fast and take very little compute
