For anyone interested in how the graphs were made: https://oss.oetiker.ch/rrdtool/ Happy to see people still using RRD after all these years. - Source: Hacker News / over 1 year ago

(I actually implemented the firmware for this... Using the Arduino port for ESP8266 because I was impatient, but discovered that either my DHT11 modules are junk or I'm misusing them, because the humidity measurement drifts as they keep running. I need to write the Rust+rrdtool app meant to receive the reports and then do some comparative tests between the DHT11 and some BME280s, and between the current firmware... Source: over 2 years ago

Nice! This is giving me some ideas. Here's what my old school rrdtool-based system looks like:. Source: over 2 years ago

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago

I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago

This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: about 2 years ago

Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago

Check out https://aws.amazon.com/emr/. Source: about 2 years ago