OmniSci for Mac
Analytics at the speed of curiosity
“I’ve never seen an analytics platform on a personal laptop process a billion rows so performantly before. Nothing else comes close. OmniSci running on a MacBook Pro opens up an entirely new world of data exploration, interactivity, and scale for a much larger audience.”Read Full Blog Post
Frequently Asked Questions
Assuming the planet is still here (2020 and all), send us an email. We'll work something out.
That's a secret, but we can tell you that in ⬛⬛⬛⬛⬛ we plan to ⬛⬛⬛⬛⬛ on a ⬛⬛⬛⬛⬛ which should feature ⬛⬛⬛⬛⬛ and provide groundbreaking new ⬛⬛⬛⬛⬛ and ⬛⬛⬛⬛⬛ and at even larger scale. We can't wait!
You can go to our Immerse documentation to learn more, or chat with us under Help. We’re also going to have a series of videos soon on our YouTube channel. Be sure to like, subscribe, and click that notification bell.
Sorry about this! This is a ‘use at your own risk’ experimental build of OmniSci for the Mac. We squashed a lot of bugs to get here, but it is honestly still alpha quality at this time. Bugs aside, it is worth remembering that you can do a lot of things in SQL that are, despite our best intentions, just bad - e.g. running a multi-way self-join on a 100 million row table, or a ‘select *’ query with no limit on a billion row table from SQL editor. The good news is, your feedback and our constant focus on bug bashing will help to make this better!
Try to take over the world. Under the hood is the exact same OmniSciDB that is among the fastest analytical databases in the world. You can access it also via the included SQL Editor. Over the next few weeks, we’ll also show you how to use OmniSci in a Data Science workflow with JupyterLab. Plus, Immerse itself has lots of powerful features such as crossfilter and cohort analytics.
A full fledged analytical SQL engine (OmniSciDB) that is run in production by our largest customers, offering millisecond latencies on billion row datasets. An advanced data visualization solution which you can use to build dashboards over 100 million row datasets on your laptop. Deep data science integration with the PyData stack. All for the price of - $0, for now.
Any Intel Mac (MacBook Pro, iMac or Mac mini) should work, but we recommend more modern machines with these specs:Recommended: 7th generation Intel processor with 4+ cores and 16GB of RAM. Ideal: 32GB of RAM or higher for datasets with 100-250 million rows2019/2020 16” MBP with 64GB easily handles larger datasets up to a billion rows (that’s what Mark ran the taxi rides query benchmark on).OS: macOS Catalina(10.15). If you are on 10.14, try to right click on the file and click on open. For other issues, review this page: https://support.apple.com/en-us/HT202491
We got ourselves a developer transition kit. We’ll keep you posted!
Windows folks - sorry you can’t run OmniSci on your epic gaming platforms...yet. However, you can already run OmniSci (including Nvidia GPU support) on Linux and Docker. In fact you can run OmniSci on your Mac (or Windows) with Docker, but you’ll pay a performance cost due to virtualization.
While we actually have work in flight to render on GPUs we are not running compute on (i.e. the AMD and Intel GPUs present on a modern Mac), rendering on a Mac is trickier than Linux or Windows due to the lack of modern OpenGL support. There are lots of ways you can build scalable geoviz outside this though, using our Python tools, and we are looking at ways of supporting scalable frontend rendering for the Mac offering. Again, if you’re interested, let us know.