Surpassing the memory wall requires a wise and well balanced combination of computation and compression, at the right time *and* at the right place. Here is how we are achieving this: medium.com/p/a4351cbc196

Often times you need to think about a problem from the inside-out. This is how we implemented User Defined Functions to re-define computing for a more cost effective usage of your computing facilities: blog.ironarray.io/inside-out-com…

Did you know that ironArray can access #Zarr remote arrays in a transparent way via the Zarr proxy array? See our tutorial here: 👇 ironarray.io/docs/html/tuto…

🎉We are happy to announce that we have moved most of our documentation into blosc.org (see the new navigation bar on top). The doc pages in readthedocs.io have been removed for now. Sorry for the inconvenience. Thanks to Marc Garcia, for all the help!

Did you know that our Enterprise license comes with up to 100 hours of support (negotiable)? We want to *collaborate* with you to make your interaction with ironArray (both the library but *also* the company) as fluid and interactive as possible. Your success is our fulfilment!

Did you know that our array constructors work in parallel and can go more than 10x faster than NumPy? And that by leveraging compression you can host way more data using the same memory resources? More info 👉ironarray.io/docs/html/benc… Enjoy speed and compactness with ironArray!

Did you know that ironArray comes with AI-driven compression algorithms that lets it easily adapt to your preferences? SPEED: favor speed CRATIO: favor compression ratio BALANCE: balance among the two above No more time wasted deciding the codec to use! 👉blog.ironarray.io/surpassing-mem…

Persistent storage has been gaining performance by leaps and bounds since solid state disks introduction. Check how out-of-core computation can lead to huge savings in memory compared with other solutions, while also performing decently fast: blog.ironarray.io/out-of-core-co…

ironArray implements a matrix multiply that consumes (much) less time & memory than other parallel solutions. Even on Intel machines, ironArray can go at a speed that is just 2x slower than NumPy+MKL. Read more 👉 blog.ironarray.io/matrix-multipl… Save time & energy with ironArray ⚡️🌲

ironArray comes with a nice assortment of blazing fast random generators. Check our tutorial here: ironarray.io/docs/html/tuto… API: ironarray.io/docs/html/refe… But as there is more to life than speed, our tests also guarantee that distributions pass the Kolmogorov-Smirnov test 👇

🎉🎉New blog on estimating 𝜋 by throwing darts and using ironArray: blog.ironarray.io/estimating-by-… In this installment: high quality random number generators, User Defined Functions and reductions. See how ironArray can consume much less time and memory, compared with #NumPy/#Numba 👇

User Defined Funtions are a data mini-language (DSL), where code is intertwined with compressed data at a fundamental level to achieve best performance. See how to create libraries of UDFs for operating with your data at ⚡️ speed: 👇 blog.ironarray.io/estimating-by-… Do more, with less.

Reductions are the bread and butter of everyday data analysis. ironArray allows doing this for on-disk data faster and consuming much less memory (up to 20x) than e.g. Dask+Zarr. ironarray.io/docs/html/benc… Do more with less.

See why #ironArray can make your #data simulation code to fly 🪁🪁: ironarray.io/blog_materials…

ironArray joined Twitter 3 years ago already? Really? Proud to see how much progress we did during these intense years! 🎉🎉 🚀🚀

Honored to put our team and knowledge in this work!