Snowplow VS Amazon EMR

We’ve also thought about Ops :-). There’s a backend 'Collector' that stores data in Postgres, for instance to use while developing locally, or if you want to get set up quickly. But there’s also full integration with Snowplow, which works seamlessly with an existing Snowplow setup as well. - Source: dev.to / over 1 year ago

Sure thing! Say you run an online store. Your source systems could be the inventory, orders or customer databases. You could also track click/site behavior with something like snowplow. An ERP system is essentially just a combination of what I mentioned previously. Another good example is a CRM such as Salesforce or Zendesk. Hopefully that helps! Source: almost 2 years ago

Well if you have to structure and create Schema and manage Data Warehouses, you need a tool to do that, so in the background you see SnowPlow, which helps you do just that. Make the data into some kind of sensible structure so that later on business analysts can come see whats up. Want to do a quarterly report on how you performed, go to the application that goes to the data warehouse and builds your report for... Source: about 2 years ago

We also have telemetry set up on our Monosi product which is collected through Snowplow,. As with Airbyte, we chose Snowplow because of its open source offering and because of their scalable event ingestion framework. There are other open source options to consider including Jitsu and RudderStack or closed source options like Segment. Since we started building our product with just a CLI offering, we didn’t need a... - Source: dev.to / about 2 years ago

Https://matomo.org That's the only full featured open source competitor I am aware of, so it should be mentioned. https://snowplowanalytics.com/ Somewhat FOSS. There was a story there, but I don't remember the details. - Source: Hacker News / 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: almost 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: almost 2 years ago