Based on our record, Jupyter seems to be a lot more popular than Amazon Kinesis Firehose. While we know about 206 links to Jupyter, we've tracked only 6 mentions of Amazon Kinesis Firehose. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Interesting, I would have guessed you had used something jupyter-like: https://jupyter.org/ https://explorabl.es/all/. - Source: Hacker News / about 1 month ago
JupyterLab: JupyterLab is an interactive development environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It's particularly well-suited for data science and research-oriented projects. - Source: dev.to / 2 months ago
Jupyter Lab web-based interactive development environment. - Source: dev.to / 2 months ago
Choosing IDE: Selecting a suitable Integrated Development Environment (IDE) is crucial for efficient coding. Consider popular options such as PyCharm, Visual Studio Code, or Jupyter Notebook. Install your preferred IDE and ensure it's configured to work with Python. - Source: dev.to / 2 months ago
Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / 4 months ago
First, you may not know the Kinesis Data Firehose service. Here's the AWS definition: Amazon Kinesis Data Firehose is an Extract, Transform, and Load (ETL) service that captures, transforms, and reliably delivers streaming data to data lakes, data stores, and analytics services. (https://aws.amazon.com/kinesis/data-firehose/). - Source: dev.to / over 1 year ago
As you can see in the diagram, we are feeding all events from Event Bus via a catch-all rule into Kinesis Data Firehose. Firehose is a fully managed service that streams into specific destinations like Data Warehouses or Data Lakes. Unlike it's bigger brother of using Kinesis Data Streams directly, there are no setting up of shards and it's mostly configuration free. We are only defining a buffer interval which is... - Source: dev.to / over 1 year ago
When using EventBridge I always log all events to an S3 bucket for auditing, analytics and debugging purposes. A super easy method to do this is to create a Kinesis Data Firehose stream and create a rule that captures all events that points to the Firehose stream. The Firehose stream can then flush the events on S3 in an interval/size of choice based on configuration. - Source: dev.to / almost 2 years ago
Have you looked at Kinesis Firehose? It was pretty much build for this use case although you will still need to see if you can define a partitioning scheme probably in combination with an S3 Select query to meet your query requirements. https://aws.amazon.com/kinesis/data-firehose/?nc=sn&loc=0. - Source: Hacker News / almost 2 years ago
Is continuous backup important ? e.g. If the stuff fails for one day and you lose that day's upload is that ok? Do you want it to push updates more frequently than once a day? If you want to continuously push updates then Kinesis Firehose might be worth looking into. Source: over 2 years ago
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