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Not an expert on data, but a potential solution might be to send data from IoT to Kinesis Fire Hose to S3 (using some columnar format such as Parquet) and then use Athena for querying the date from S3 when you need it.
Depending on your queries, you may also want to have some ETL process that runs periodically and aggregates the raw data so that the queries are much faster.
I recommend you to have a look at this blog post about the IoT data ingestion architectural patterns in AWS.
You should consider using Amazon Timestream (See pattern number 4 in the blog). You can push data from IOT core directly to Timestream to have a highly scalable IOT ingestion pipeline.
For more information: Timestream with IOT core integration documentation guide and Ingesting Data into Amazon Timestream with AWS IoT Core video.
Thank you for your advice, Timestream looks like a good solution but i think it's gonna make the costs go up, no ? our goal is a solution that could reduce costs.
I've reviewed your current structure and potential solutions, and I have a few recommendations.
First, I think you should consider using a different database for your IoT data. Postgres is a great database for general-purpose use, but it's not the best choice for storing large amounts of time-series data. A better option would be a database that's specifically designed for IoT, such as InfluxDB or Amazon Timestream.
Second, you could use a different data storage solution. Storing your data in small files on S3 is a good option, but it's not the only one. You could also use a database like DynamoDB or Redshift.
Finally, you could use a different way to process your data. Instead of using a server, you could use a Lambda function or Kinesis. This would reduce the load on your server and improve performance.
Thank you for your answer, for storing data i'm thinking about files in s3 to make the costs more low and kinesis or lambda to handle that data is a good solution.
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Thank you for your answer, this gives me another possibility to do stuff and i will definetly look into it.