Data analytic solution for an IoT system
I'm implementing the real-time statistics/analytic module for an IoT system in which the huge moving devices will send their status (includes GPS,...) to AWS IoT and the analytic module has to process device's status real-time to compute the journey & the total distance in a day for each devices. I don't want to use many solutions from AWS due to the cost, however, using AWS IoT gateway is must. The current my solution is working like that:
The device logs are sent to AWS IoT, AWS triggers ThingShadow Rule Engine to send data to AWS SQS. I installed a Apache Spark cluster and running jobs to get messages from AWS SQS, save them to each thing information on HBase then delete those messages from SQS. And other spark apps to aggregate messages from HBase then compute journey and statistics and send notification to mobile applications (that manages device). I'd like to ask your advice of the following issues:
- Is it possible to scale Spark app (running many instances of a Spark app) to get messages from an SQS?
- Is it possible to save messages on Spark in-memory distributed (without using HBase)?
- If the issue 2 is impossible how can I store list messages of each device in a period of time (because I get messages from many devices parallel and I want to category message to each ThingName for the computation)?
- My data flow is: device -> AWS IoT -> Rule Engine -> SQS -> Spark SQS Consumers & ETL (scalability) -> HBase -> Spark Computation. Is it right? Can you suggest me the better solution or idea?
Thank you very much in advance!
amazon-web-services apache-spark hbase analytics iot
asked Jan 20 at 15:04
raycadraycad
6
add a comment |
I'm implementing the real-time statistics/analytic module for an IoT system in which the huge moving devices will send their status (includes GPS,...) to AWS IoT and the analytic module has to process device's status real-time to compute the journey & the total distance in a day for each devices. I don't want to use many solutions from AWS due to the cost, however, using AWS IoT gateway is must. The current my solution is working like that:
The device logs are sent to AWS IoT, AWS triggers ThingShadow Rule Engine to send data to AWS SQS. I installed a Apache Spark cluster and running jobs to get messages from AWS SQS, save them to each thing information on HBase then delete those messages from SQS. And other spark apps to aggregate messages from HBase then compute journey and statistics and send notification to mobile applications (that manages device). I'd like to ask your advice of the following issues:
- Is it possible to scale Spark app (running many instances of a Spark app) to get messages from an SQS?
- Is it possible to save messages on Spark in-memory distributed (without using HBase)?
- If the issue 2 is impossible how can I store list messages of each device in a period of time (because I get messages from many devices parallel and I want to category message to each ThingName for the computation)?
- My data flow is: device -> AWS IoT -> Rule Engine -> SQS -> Spark SQS Consumers & ETL (scalability) -> HBase -> Spark Computation. Is it right? Can you suggest me the better solution or idea?
Thank you very much in advance!
amazon-web-services apache-spark hbase analytics iot
asked Jan 20 at 15:04
raycadraycad
6
add a comment |
I'm implementing the real-time statistics/analytic module for an IoT system in which the huge moving devices will send their status (includes GPS,...) to AWS IoT and the analytic module has to process device's status real-time to compute the journey & the total distance in a day for each devices. I don't want to use many solutions from AWS due to the cost, however, using AWS IoT gateway is must. The current my solution is working like that:
The device logs are sent to AWS IoT, AWS triggers ThingShadow Rule Engine to send data to AWS SQS. I installed a Apache Spark cluster and running jobs to get messages from AWS SQS, save them to each thing information on HBase then delete those messages from SQS. And other spark apps to aggregate messages from HBase then compute journey and statistics and send notification to mobile applications (that manages device). I'd like to ask your advice of the following issues:
- Is it possible to scale Spark app (running many instances of a Spark app) to get messages from an SQS?
- Is it possible to save messages on Spark in-memory distributed (without using HBase)?
- If the issue 2 is impossible how can I store list messages of each device in a period of time (because I get messages from many devices parallel and I want to category message to each ThingName for the computation)?
- My data flow is: device -> AWS IoT -> Rule Engine -> SQS -> Spark SQS Consumers & ETL (scalability) -> HBase -> Spark Computation. Is it right? Can you suggest me the better solution or idea?
Thank you very much in advance!
amazon-web-services apache-spark hbase analytics iot
asked Jan 20 at 15:04
raycadraycad
6
I'm implementing the real-time statistics/analytic module for an IoT system in which the huge moving devices will send their status (includes GPS,...) to AWS IoT and the analytic module has to process device's status real-time to compute the journey & the total distance in a day for each devices. I don't want to use many solutions from AWS due to the cost, however, using AWS IoT gateway is must. The current my solution is working like that:
The device logs are sent to AWS IoT, AWS triggers ThingShadow Rule Engine to send data to AWS SQS. I installed a Apache Spark cluster and running jobs to get messages from AWS SQS, save them to each thing information on HBase then delete those messages from SQS. And other spark apps to aggregate messages from HBase then compute journey and statistics and send notification to mobile applications (that manages device). I'd like to ask your advice of the following issues:
- Is it possible to scale Spark app (running many instances of a Spark app) to get messages from an SQS?
- Is it possible to save messages on Spark in-memory distributed (without using HBase)?
- If the issue 2 is impossible how can I store list messages of each device in a period of time (because I get messages from many devices parallel and I want to category message to each ThingName for the computation)?
- My data flow is: device -> AWS IoT -> Rule Engine -> SQS -> Spark SQS Consumers & ETL (scalability) -> HBase -> Spark Computation. Is it right? Can you suggest me the better solution or idea?
Thank you very much in advance!
amazon-web-services apache-spark hbase analytics iot
amazon-web-services apache-spark hbase analytics iot
asked Jan 20 at 15:04
raycadraycad
6
asked Jan 20 at 15:04
raycadraycad
6
asked Jan 20 at 15:04
raycadraycad
6
asked Jan 20 at 15:04
raycadraycad
6
asked Jan 20 at 15:04
raycadraycad
6
6
add a comment |
add a comment |
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