DocRaptor VS Jupyter
Compare DocRaptor VS Jupyter and see what are their differences
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DocRaptor
pdflayer
PDFCrowd
PDFShift
cloudlayer.io
HTML PDF API
Api2Pdf
PDF my URL
Jupyter
Databricks
Looker
Google BigQuery
Presto DB
Rakam
Informatica
Concurrent
Only DocRaptor's HTML-to-PDF API has these advanced styling and layout capabilities:
Instead of a separate HTML file, DocRaptor headers and footers are part of your document HTML. And easily show (or hide) different headers and footers for different pages.
DocRaptor lets you control the style, sizing, headers, and layouts of individual pages in your document. You can even style left and right pages differently, or the first and last pages.
DocRaptor lets you make PDFs with advanced CSS layout tools, including flexbox. You won't need to radically adjust your website to get a great PDF.
Create more accessible PDFs by using PDF profiles PDF/A-1a, PDF/A-3a, or PDF/UA-1. Tagged PDFs optimize the reading experience for assistive technology such as screen readers.
Our rendering engine was built specifically for making PDFs and we fully support CSS3 Paged Media. This allows much greater control over page breaks, especially when dealing with tables and images.
Add crop marks, specify PDF bookmarks, or create standards-compliant documents.
We back our API with a 99.999% uptime guarantee. If you need reliability, DocRaptor is the service you can trust. We also have no limits on document input or output size.
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I've been using it for a while. It's great to create contracts.
We wanted an app that would allow for custom branding and layout, the font of our choice, and merge fields across our main SF objects. Previously we used DocGen, which led to a morass of configuration to put fields in exactly the place they needed to be for the tables, as well as a bunch of SOQL queries to manage conditional logic. The VF doc generator can't accommodate the fonts we use in our branding. And so DocRaptor has been the perfect solution.
Our developer built the contracts, and we went live within weeks with complete branding, flexibility in the data merges (we were able to remove a ton of bad config) and it's easy to manage.
I have been using DocRaptor for 6 years, both for my professionnal and personnal projects. After trying several free and/or open source HTML to PDF solutions, I was happy to find this service. It's the most efficient solution, which generates the most accurate PDF documents.
Since it's a SaaS service, there is nothing to install, no library dependencies nor experimental software that you're not sure it will be supported in the future.
There is a lot of options and CSS rules to dig in if you want to get PDF files that exactly matches what you want. But the other solutions I tried didn't have these options, and the result was not good enough.
Based on our record, Jupyter seems to be a lot more popular than DocRaptor. While we know about 206 links to Jupyter, we've tracked only 4 mentions of DocRaptor. 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.
It sounds like this is as advanced as DocRaptor[1]. They have what I consider to be the best PDF generation API, giving complete control over the documents you need to create. The pricing is similar. If you'd rather do it for free weasyprint[2] is the best open source alternative. Another more affordable option you might want to consider is Urlbox[3]. (Disclosure: I work on this) Urlbox's rendering engine is based... - Source: Hacker News / 3 months ago
We built the DocRaptor API to let developers have affordable access to the commercial Prince PDF engine. We have Node code examples throughout the documentation. Source: over 1 year ago
I'd argue our service, DocRaptor, is the best because it's the only one powered by the Prince PDF engine. Unlike open-source, browser-based conversion engines, Prince was custom-built just for converting HTML into PDFs and offers a lot of unique functionality for making more complex PDFs. Source: about 2 years ago
I work for https://docraptor.com, which is an HTML to PDF API. We have a C# agent. Source: almost 3 years ago
Interesting, I would have guessed you had used something jupyter-like: https://jupyter.org/ https://explorabl.es/all/. - Source: Hacker News / 21 days 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 / about 1 month ago
Jupyter Lab web-based interactive development environment. - Source: dev.to / about 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 / about 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 / 3 months ago
pdflayer - Free, powerful HTML to PDF API supporting both URL and raw HTML conversion. Unlimited document size, lightning-fast and compatible PHP, Python, Ruby, etc.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
PDFCrowd - Pdfcrowd is a Web/HTML to PDF online service. Convert HTML to PDF online in the browser or in your PHP, Python, Ruby, .NET, Java apps via the REST API.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
PDFShift - Convert any HTML documents to high-fidelity PDF using a single POST request
Google BigQuery - A fully managed data warehouse for large-scale data analytics.