The Value Pillars of Haevek’s Falcon Compute · Pillar 03: Capability
“Haevek is a very innovative capability. We don’t think anyone else can do what they do.”
Chief Engineer · Raytheon
The problem
Cloud data and AI infrastructure costs rise faster than the value the workloads produce. Strategy has to balance increasing the compute budget, cutting projects, or slowing down workloads to fit the budget.
Organizations have to face the reality of finite resources and limit what they work on. If only they could expand their reach, making space for more initiatives and reaching for bigger milestones.
For years, the same cost-to-capability reality has been the norm. Finding efficiencies in pipelines can only go so far. Most performance issues are solved with JAMC: Just Add More Compute. The cost of one initiative reduces capability needed to operate another initiative.
Haevek’s high performance, highly efficient compute transforms capability decisions. A typical workload reaches the same SLA as Databricks or Apache Spark with 75-90 percent fewer resources. Pair that with the tiered licensing and companies have headroom to develop more initiatives using less resources. By resources we mean costs. Budget. Money. That’s right, it does more with less.
Haevek takes on complex workloads with little migration stress and massive reduction in cost.
On prem · Air gapped · Cloud · Edge
Opportunities that require specific security, service levels, or architecture can take advantage of Haevek Falcon. Not a cloud service, Falcon runs Kubernetes-native wherever you need it.
Using tiered rather than consumption “as-a-service” pricing, licensing costs are predictable and fixed even as new workloads come online.
By reducing the cost of operations, those initiatives that were out of bounds can now be put into operation. New lines of business and product innovation speed up without sacrificing core practices.
By putting complex workloads on Haevek, the amount of compute required for performance drops precipitously. More projects get on the plan for THIS quarter, not some future date.
Although offering some expanded capabilities, the syntax of the Falcon Dataset Programming Model maps to Spark (and by extension Databricks). A developer familiar with Spark RDD and DataFrame API will quickly adapt to Falcon, even using the same familiar standard library operators.
Adopting Falcon is not rip and replace. It does not mean retiring your other data compute platforms. Haevek Falcon operates next to solutions like Spark, Big Query, Snowflake, and Redshift without interrupting existing production processes.
As a Kubernetes-native, Falcon can be spun up anywhere Kubernetes runs. Haevek sends you a Helm chart and you are up and running on the compute infrastructure of your choice.
Built-in ML
ML inference embeds directly in the Falcon pipeline.
Case study
Using the same data sources and data lake, a Falcon implementation runs both stream and batch multi-terabyte workloads for 64 percent lower TCO than the previous Databricks solution.
Falcon consistently lowers the cost and improves the performance of sophisticated workloads.
We offer the 2-4 week Falcon Test Flight to pilot Falcon in your environment with your data. No data moved, no lock-in, training included.