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Website | prestodb.io |
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Website | jupyter.org |
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Based on our record, Jupyter seems to be a lot more popular than Presto DB. While we know about 202 links to Jupyter, we've tracked only 6 mentions of Presto DB. 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.
Presto is an open-source distributed SQL query engine, originally developed at Facebook, now hosted under the Linux Foundation. It connects to multiple databases or other data sources (for example, Amazon S3). We can use a Presto cluster as a single compute engine for an entire data lake. - Source: dev.to / almost 2 years ago
Fair point, but I am talking about Athena (not SQL Server), which under the hood uses a distributed query engine. It is capable to deal with huge amounts of data, if the storage is in the right shape. You can read more about the underlying technology here: https://prestodb.io/. Source: about 2 years ago
So there is Presto, which is a distributed SQL engine created by Facebook. Source: about 2 years ago
You can use Athena to run data analytics, with just standard SQL (Presto). - Source: dev.to / over 2 years ago
Presto does this, but I'm honestly uncertain how performant it is. In my experience, centralizing data is the superior approach to attempting to query multiple sources in place. Source: over 2 years 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 / 25 days ago
Note. Nowadays, there are many flavors of notebooks (Jupyter, VSCode, Databricks, etc.), but they’re all built on top of IPython. Therefore, the Magics developed should be reusable across environments. - Source: dev.to / 26 days ago
They make it easy to launch multiple case-by-case data science projects and run your local code right from Jupyter Notebook. - Source: dev.to / about 2 months ago
Talking to some colleagues and friends lately gathering some ideas of a nice Machine Learning project to build, I’ve seen that there’s a gap of knowledge in terms of how do one exactly uses a Machine Learning model trained? Just imagine yourself building a model to solve some problem, you are probably using Jupyter Notebook to perform some data clean up, perform some normalization and further tests. Then you... - Source: dev.to / 3 months ago
This year I decided to commit to a set of tools on day 1 (Polars and Jupyter) and use them for the whole challenge. It seemed silly to do a whole new meandering walkthrough, so instead I'll highlight a few things that stuck out after finishing the challenge and sitting on it for a few days. Here we go! - Source: dev.to / 3 months ago
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