Efficiency and Speed at Any Scale Equals Unprecedented Price/Performance

Today’s digital infrastructure is built on the wrong algorithms. We know this through conversations with executives, product managers, data infrastructure architects, data scientists, and software engineers as well as from our own experience working on incredibly large-scale data problems. The common problem is a lack of speed and efficiency at scale. With exponentially growing data, this problem is only getting worse. The inability of today’s data platforms to provide speed and efficiency at scale manifests itself in:

  • Time to insight measured in hours or days
  • High marginal cost and latency of query
  • An inability to efficiently connect the dots within data sets
  • Unsustainable IT practices

To meet the challenge of exponentially growing data, we need algorithms that are decoupled from the volume of data, so we can operate efficiently at petabyte or even exabyte scale.

Unprecedented Price/Performance

Unsustainable "Index Free" Approaches

Many recent and prominent data platforms have given up on trying to organize data. Instead, they resort to brute force methods for supporting fast queries. Rather than suffering the immense cost of maintaining traditional indexes as data volumes grow, these “index free” platforms simply scan all the data in the relevant columns for every query. This is analogous to throwing away the card catalog at the Library of Congress and trying to find one book by looking at every book.  

Brute force methods use massive amounts of compute power and resources to perform, consuming massive amounts of energy.

So why are brute force or "index-free" methods so prevalent today? Because without Craxel's innovation, the time and cost to index complex data is too high.

Black Forest’s approach to organizing data uses less compute and is exponentially more efficient than O(N).

Decoupling Efficiency From Data Set Size

Craxel's unique O(1) technology decouples performance and cost from data set size. Powered by unique O(1) technology for indexing multi-dimensional data in constant time, Black Forest delivers extraordinarily fast time to insight for high volume, high velocity use cases, enabling both rapid human and automated decision making. The fast query times provided by Black Forest dramatically improve human productivity, while enabling the next generation of algorithmic and AI capabilities. Black Forest achieves both speed and efficiency because it uses a fraction of the compute power required by traditional approaches. Speed and Efficiency = Better Decisions, Faster, At Any Scale.

Decoupling efficiency from data set size
Decoupling storage from compute

Decoupling Storage From Compute

The ability to decouple storage from compute is a critical component for achieving unprecedented price/performance. The reason is that hyperscale storage is much less expensive than splitting data across large clusters of servers running 24/7.  

The problem with most methods of decoupling storage from compute is that very large chunks of data need to be scanned using brute force methods to perform a query.

Black Forest also decouples storage from compute, but our constant time indexing algorithm greatly reduces the amount of data that needs to be loaded from storage, sent across the network, and evaluated by compute. This means that data access can be incredibly fast and efficient.