Google's programmable search engine comes to mind: https://developers.google.com/custom-search/. Source: over 1 year ago

Dorking is not only a very useful technique to find not-indexed results and unvoluntarly exposed content, it it also helps to improve beginner's analyst mindset. You can take it as an introduction to basic query language. What I can strongly suggest is to test your skills by creating your own google custom search engine (https://developers.google.com/custom-search/) that will faciltate your onlime search by... Source: over 1 year ago

It looks like is targeted towards website owners and not the general public. https://developers.google.com/custom-search. - Source: Hacker News / about 2 years ago

A functional replica of Google's search page, you can use it for searches. Styled with Tailwind CSS to Rapidly build and look as close as possible to current google search page, the search results are pulled using Googles Programmable Search Engine and it was build using Next.js the react framework. - Source: dev.to / about 2 years ago

There is a programmable search feature [0] that lets you limit search to a defined list of sites. Someone did a ShowHN a few months ago where they had built a programmable search with 200ish common sites that a stereotype HN reader might like (software documentation, wikipedia, reddit, some news and other media, etc), and it was actually pretty good. I've said before, google is now basically what I'd call a... - Source: Hacker News / about 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: 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: about 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