easy-to-use stream processing for IoT

Combine raw data (sensor, network, transaction records, social media) and stored data (personal history, network and security data, demographics, research data) to provide real-time insight into the performance of sensors, consumer devices and industrial machinery. Optimize operations by maximizing efficiency and processing high volumes of data to detect system threats, monitor conditions and automatizing actions. 

 

Case study: PRIMEX

Many IoT companies providing home connected devices are losing money due to the high cost of moving and managing data to the cloud.  Primex found themselves in this situation, relying on a pay-as-you-go, serverless architecture that proved to be inefficient and expensive when deployed at Internet Scale on a continuous event stream.

SQLstream turned the tables by reimplementing their AWS Lambda functions as continuous SQL queries and reducing infrastructure and processing costs by 2/3rds, restoring Primex’s weather monitoring service to profitability.

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SQLstream solves challenges of wrangling, analyzing, and acting upon IoT data in real time

Capitalizing on the richness of IoT data is compelling, but in order to yield actionable insight, data needs to be collected, ingested, analyzed, and pushed into action in continuously, and in real time. Building and managing a system able to support this kind of performance, productivity, and responsiveness is more often than not a non-secure, expensive, and slow exercise. And when the parameters change, it’s back to square one. 

 

the challenges of wrangling, analyzing, and acting upon IoT data in real time

Real-time IoT data wrangling

  • Many data sources
  • Collection and data transport not secure
  • Many data formats
  • Variable bandwidth conditions
  • Data volumes are volatile
  • Volume too high for real-time responsiveness
  • Not all data is relevant
  • Interruptions 

Streaming IoT data analytics

  • Results take too long
  • Results are not easy to access
  • Results are accessible to the deeply technical
  • Development takes a long time
  • Development is dependent on skill sets
  • Data manipulation cannot be done on-the-fly

Real-time ACTIONS

  • Data visualization is not real-time
  • Visualizations are not easily consumable
  • Not able to trigger alerts or actions in real-time
our solution

A COMPLETE IoT STREAMING SQL processing platform

SQLstream Blaze supports IoT stream processing at all levels of the data lifecycle through a SQL-based data wrangling, streaming analytics and automated actions platform that is easy to use by developers and analysts alike. 

SQLstream Blaze combines raw IoT data (sensor, network, transaction records, social media) and stored data (personal history, network and security data, demographics, research data) to optimize operations by detecting system threats, monitoring and integrating all processes in real time, and automatizing actions. In parallel, revenue can be increased through support of multiple applications, millions of users and subscription-based aggregated feeds.

RELATED RESOURCE: Building a Streaming Application in minutes using GPS Data (DEMO)

READY TO GIVE IT A TRY? 

SQLstream Blaze is free to download and use up to 1GB/day (after which it throttles)