Find out here: How do we optimize IoT data processing? We reduced infrastructure costs and improved scalability to handle millions of requests daily.

Challenge: Optimizing High-Volume IoT Data Processing

Amatis was struggling to efficiently process and store data from their IoT platform, which was handling 14 million requests per day from 88,000 devices using MySQL as the database. This setup was becoming increasingly inefficient and costly as the number of devices and data volume grew.

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The existing MySQL-based system was unable to efficiently handle the large volume of time-series data generated by the IoT devices. This led to increased infrastructure costs and potential scalability issues as the platform continued to expand.

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 Solution

Melt Studio implemented a comprehensive solution to address these challenges...

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• Migrated the database from MySQL to InfluxDB, a time-series database optimized for IoT data.

• Redesigned the data processing pipeline to efficiently handle the high volume of requests.

• Implemented data distribution strategies to balance the load across the system. 

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The Outcome:  The migration to InfluxDB and the optimization of the data processing pipeline resulted in a 50% reduction in infrastructure costs, also:

• Significantly improved system efficiency in handling the 14 million daily requests.

• Enhanced scalability to support future growth in the number of connected devices.

• Improved data retrieval and analysis capabilities for time-series IoT data​.

💡 At MeltStudio, we solve real challenges for businesses facing high data volumes or scalability issues.

If you're a CEO or CTO looking for smarter, cost-effective solutions to scale your technology, let’s connect. We’ll find the right approach to help your business thrive.

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Thx for reading. With Love, MS.