There are SaaS products out there that can help with data collection like incident.io or firehydrant.io to more quickly construct a timeline. Source: about 1 year ago

My new favourite is https://incident.io. Great UI, great product, especially if you also need an incident management tool. Source: about 1 year ago

We did a pretty detailed write-up about a significant incident we had a few months back at incident.io: https://incident.io/blog/intermittent-downtime. Source: over 1 year ago

Co-founder of incident.io here, so I'll avoid throwing my thoughts around for obvious reasons. Source: over 1 year ago

I work at a company that offers a platform for this called https://incident.io/. Source: over 1 year 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: over 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