Many of today's most pressing business and analytic problems come back to indexing. This is because we have a fundamental problem around how data is organized.
Historic techniques have failed to meet our access and scalability needs. They rarely, if ever, provide the capability teams need to move gracefully around data sets that are growing at incredible rates on the daily.
Every person working with data knows there is no single solution to this. Today's technologies are often too slow and resource intensive to support real-time access. At scale, this issue only grows exponentially.
Relational databases were designed for the world of a single computer, organizing data sets in tables with rows and columns and with pre-defined relationships between them. This is a powerful technology; as most digital transactions in the world are powered by a relational database. Yet, it fails at a certain scale due to the costs and latency of updating and maintaining indexes.
Because the cost and latency of maintaining indexes is so high for relational databases and because many are used for mission critical workloads, analytical queries often get moved off to other systems where the data must be extracted and loaded. In many architectures, the data simply gets backlogged for eventual ingestion and processing for analysis. This can take so long that it's too late to make use of your newfound insights – leading to detrimental losses. All because relational databases can't scale to support both high transaction rates and analytical queries.
Index free is a new movement and attempt to shorten the time it takes for data to be available for analysis. This movement exists because of the lack of a technology to instantly index data. It uses brute force massively parallel processing - which is a big term that describes the use of lots of computers to resolve a single query. The idea is to forgo organizing the data and instead perform a full scan of the data. This means if your data set is 100 petabytes large, you need to scan 100 petabytes for every single query. One could imagine how incredibly inefficient and cost intensive this method is – A full table scan can take days to resolve. When teams need to operate in real-time and on very large data sets, these technologies simply can't support them.
There are numerous other limitations we could discuss: Solutions that use brute force methods will never support true ad-hoc query at scale. Data ingestion rates are slow and transforming this data is extremely time consuming. For business cases such as threat hunting, fraud detection, and IT observability where anomalies are a constant, current methods can't support the timely access at scale needed for analysis and respective business action.
This is what the world looks like today.
We need tech that enables data to be organized instantly for real-time analysis. We need new math.
A charitable description of where data technology is today would be that we are in the first inning of a baseball game. A non-charitable description would be that we are still in the stone ages, trying to carve a wheel out of stone.
The answer to solve for some of the most pressing issues in big data is to continue to adopt and build on innovation.
Throughout the history of computer science, R&D has been done on how to solve for the issues described above with little to no avail.
With this said, we think we've solved for it at Craxel. And we're releasing our innovation to the world.
Our team has experienced firsthand the inefficiencies of today's vendors and have come up with a new solution to these issues. Our Black Forest data infrastructure is built on new and better math that solves for the issue of how to instantly organize data for high performance and efficiency at any scale. We've essentially discovered a way to maintain an exquisite map of your data at a speed and cost that is unprecedented in the history of computing. This makes it possible to organize data at the speed it is generated at incredible scale and with incredibly low cost. It radically reduces time to insight, ad-hoc query latency, and marginal cost of query. It also allows data to be organized in a way where relationships within data sets can be quickly and efficiently analyzed and understood.
If you're experiencing any of the pains discussed here, we'd love to discuss these with you and see if we can provide a solution. Please reach out to firstname.lastname@example.org to get in contact.