Why Do Anything?
The previous platform could not process the massive number of events and consolidate the insights for the decision makers.
The South Korean manufacturer was looking for a cost-effective solution that could be integrated easily in their existing ecosystem. They were looking for an optimal solution that could grow up to 3 PB, and which would be able to work with their existing AI/ML tools. SQream supported the monitoring process of quality (PMQ) using acsensorize (collecting sensors); discovery (creating baselines) and alert on time (using ML models). Adopting these methodologies using SQream’s best practices allowed their manufacturing teams to shorten manufacturing time, reduce costs, and reduce malfunction time.
- SQream has a proprietary algorithm to chunk data and thus, cost is significantly reduced.
- SQream can load dozens of terabytes per hour, while automatically optimizing and compressing the data.
- Ingestion volume easily scales up and out.
- Ability to process petabytes of data, thousands of columns.
- Rapid ingestion to thousands of columns.
- Ability to rapidly join many tables with large datasets.