System Design

How would you architect a scalable platform for processing data from millions of IoT devices in real-time?

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Answers

Anonymous

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Use NLB as a load balancer for tcp with ttl termination followed by kinesis stream. Use Separate Channel with shard key to distribute events across multiple partition and consumed by lambda for saving data to nosql database with eventual consistency. Use master and slave with partitioned architecture at nosql databaes to write data. Use Consistent hashing for distribution of data at database. Also Lambda put details in kafka for further processing. Flink consumer follow the events and process for analytics and saving to high available self rebalancing , async write database. Sync this db with BI tools for extensive filter based query on slave nodes.
  • What are the key components and considerations in designing a low-latency IoT data processing system?
  • How would you architect a scalable platform for processing data from millions of IoT devices in real-time?
  • Create a real-time engine for processing data from IoT devices.
  • Develop a real-time processing engine for IoT devices.
  • Design a platform for processing IoT data in real-time.
  • Create a real-time IoT data processing system.
  • Design a system for real-time processing of IoT device data.
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Interview question asked to Machine Learning Engineers, Software Engineers, Technical Program Managers and other roles interviewing at Peloton, SPS Commerce, Wish and others: How would you architect a scalable platform for processing data from millions of IoT devices in real-time?.