![]() ![]() While on one hand there are a number of very mature industry standard solutions (e.g. The data cleansing or transformation category is quite a mixed bag.RabbitMQ or Kafka) in the message queues or streams category, it turned out that there a lot of new open source stream analytics solutions (Impala, Flink or Flume) in the market and almost all of the big four (Microsoft, Google, Facebook, Amazon) offer their own approaches. While there are some quite old and mature open source solutions (e.g.While there are excellent open source logging services like graylog or logstash, it can sometimes save a lot of time to use those pricey saas solutions because people have solved all the quirks and tiny problems that open source solutions sometimes have.For social media data, the data comes either from the platforms themselves via an API (which is probably the default for most projects), but there are some convenient data providers out there that allow you to ingest social media data across all platforms.We have been experimenting quite a bit with IoT devices and analytics, and it turns out that there seems to be quite a few integrated data-collection and analysis software as a service solutions out there, although you are always able to to use your own (see later) solutions.While there is quite a few software as a service solutions with slightly different focus, capturing website data in most cases is done via google analytics, although Piwik offers a nice on-premise alternative.For scrapers, there are actually quite a lot of open source projects out there that work really well, probably because those are used mostly by developers.What solutions are out there that try to integrate the data sourcing, data storage, analysis and visualization in one package? What solutions BI solutions are out there for big data? Are there platforms/solutions that offer more of a flexible data-scientist approach? My observations when compiling the list: Data Sources ![]()
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