Run SQL on Spark for Big Data Analytics and Machine Learning with Windjammer to accelerate processing runtime and dramatically reduce infrastructure costs.
Use with existing hardware resources on-premise or in the cloud with 100% compatibility.
What would you do with 4X more?
— Business Intelligence results ready in 25% time.
— Data Scientists run 4X more models on the same hardware.
Every team member continues to use Apache Spark.
— No data copying, use data where it lives
— Use 100% existing Spark tools
— Future-proof your investment with the Spark ecosystem
The boost in throughput by Windjammer wouldn't go unnoticed - and that's a beautiful thing.
Accelerate time-to-insights for business intelligence. Put infrastructure investment to better use: run more ML models, run with bigger data, or shrink compute!
Keep the ever-growing data volumes in scalable storage or data lake in the Cloud while avoiding data locality penalties on Spark.
Use significantly less reserved or spot instance capacity. Put savings into executing additional ML models or simply keep the CFO happy!
Our customer advisory team shares the top 10 practices to harness Big Data with Spark effectively, whether your data is in Terabytes or Petabytes.
If you are a Data Leader, Data Scientist, or Data Engineer, use our checklist to compare and discuss techniques that empower your team to do more and allow your organization to make smarter decisions faster.
Founder and CEO, Windjammer Technologies
Data analysis of exponentially growing data is critical for businesses to maintain a competitive edge.
At Windjammer, we are building technology to tame the data analysis needs of the ever-increasing volumes of data and bring faster analysis speed and lower operational costs to our customers.
Our expert team is always on hand to arrange a Proof of Concept (PoC) in AWS and GCP, or help answer your questions.
Email us at info@windjammertechnologies.com
