Software engineer at https://ironarray.io/
#blosc2 #caterva2 #cat2cloud
🚀 C-Blosc2 2.19.0 is out!
We’ve added b2nd_expand_dims(), making it easy to add new dimensions to your b2nd arrays—perfect for evolving your data structures on the fly.
Big thanks to @lshaw8317 for the contribution! 🙏
Check out the release notes: https://github.com/Blosc/c-blosc2/blob/main/RELEASE_NOTES.md
#C-Blosc2 #DataCompression #OpenSource
Struggling with slow I/O in hashtag#HDF5? Try hashtag#Blosc2 as a filter or I/O data handler — faster data, less pain!
For those who could not attend, the recording of our webinar, "A Gentle Introduction to Cat2Cloud," is now available!
https://ironarray.io/cat2cloud#webinars-learn-more-about-cat2cloud
If interested, remember that the subscription to our beta program is currently open!
Compress Better, Compute Bigger, Share Faster!
#Blosc2 now runs directly in your browser! Leveraging the power of #WASM, #Pyodide, and #JupyterLite, you can harness efficient, adaptable compression through the web's universal interface. Experience the future of large-scale data processing without leaving your browser window.
Compress Better, Compute Bigger, Share Faster
Thanks to the advanced double partitioning techniques in #Blosc2, our #Caterva2 package can serve small slices of big datasets (3.8 GB) through internet in less than the blink of an eye.
See how you can do that with the help of #JupyterLite in #Cat2Cloud using two different techniques:
1) Plain Python-Blosc2 library for quick and dirty access
2) Caterva2 Python client for a more heavy-duty and flexible operation
Try it out! 👉 https://cat2.cloud/demo/roots/@public/examples/large-dataset-indexing.ipynb?roots=%40public
Caterva2 also supports filtering 🗃️data with fields, via the web client 🌐 or programmatically from Python code 🐍 . In the last few months we've extended this functionality even more, so that even the most advanced ❓❓❓queries are possible!
This makes it ideal for dealing with the structured datasets typically used in Machine Learning 🤖 and Data Science 🧪. Have a look 👀 to see what Caterva can do for you -> https://ironarray.io/caterva2.
#DataScience #Python #Data #Analytics
Caterva2 also supports filtering 🗃️data with fields, via the web client 🌐 or programmatically from Python code 🐍 . In the last few months we've extended this functionality even more, so that even the most advanced ❓❓❓queries are possible!
This makes it ideal for dealing with the structured datasets typically used in Machine Learning 🤖 and Data Science 🧪. Have a look 👀 to see what Caterva can do for you -> https://ironarray.io/caterva2.
#DataScience #Python #Data #Analytics
Did you know Caterva2 has native support for HDF5 files 📂 ? Here at ironArray we are constantly working 🛠️ on the software to offer an improved product to our users - checkout how to use the unfold 📖 command to expose your .h5 files to Caterva2's powerful analysis toolkit 🧰 - > https://ironarray.io/caterva2
You'll also find a comprehensive video which walks you through all the bells 🔔 and whistles we've packed into Caterva2!
☁️ Cat2Cloud Video 5! ☁️
🎉 Exciting news! With Cat2Cloud we're introducing a new way to work with large datasets in the cloud ☁️, inspired by spreadsheets 📊. Imagine an expression that automatically updates its value whenever the underlying data changes – that's the power we've brought to cloud-based data analysis.
In our new video, we connect the ✅ Single Source of Truth (SSoT) concept to lazy expressions and the powerful append 📝 functionality.
Full video at: https://ironarray.io/cat2cloud
Enjoy!
Our **4th video** on **Cat2Cloud** is now live! 🎉
**Optimize your data analysis pipelines** using **lazy expressions** in Jupyter Notebooks. 📊✨
Seamlessly combine **local 🖥️ and remote 🌐 data processing** 💾⚡
👉 Full video https://ironarray.io/cat2cloud
🚀 Compute Bigger with Cat2Cloud Compression! 🗜️
In this third video, we explore lazy expressions 😴—a compression-native tool to optimize operations ➕✖️➗ on your data using the Cat2Cloud web client. 🌐
💡 With lazy expressions, you can decompress and compute data only when needed, saving time and resources! ⏱️
👉 Try it out here: https://cat2.cloud/demo
🔗 Learn more at https://ironarray.io/cat2cloud 👀
#compression #datascience #ironArray #data
Excited to announce the release of the second cat2cloud introductory video! 🥳
Our software optimises file transfer ⚡ and access for server-hosted data 📅 . cat2cloud's compression-first framework enables users to minimise transfer times and storage requirements 🗜️.
In this video, you can see how easy and quick it is to manage file storage using the ubiquitous jupyter notebook format 🐍 !
Find out more (and see the first video) at https://ironarray.io/cat2cloud!
#API #datascience #compression #jupyter
We are pleased to announce the release of Python-Blosc2 3.3.0, a nice update for:
🔄 New blosc2.transpose() function for natively transposing 2D arrays
⚡️ New fast path for NDArray.slice() when slices align with underlying chunks - up to 40x speedup
🔧 NDArray.slice() now preserves original compression parameters
📝 Improved documentation with several English edits throughout
Happy to see the first contributions of Luke Shaw! 😀
https://github.com/Blosc/python-blosc2/releases
Compress Better, Compute Bigger!
🚀 **Exciting News!** After 15 years of developing #Blosc/#Blosc2, we're thrilled to announce the beta program for Cat2Cloud! 🎉
- 🔄 Share complex data securely and effortlessly
- 🗜️ Access to the best compression algorithms available
- ⚡ Perform advanced computations directly in the cloud
...and more!
https://ironarray.io/cat2cloud
Join our beta program today and be among the first to experience the power of Cat2Cloud!
#DataScience #Compression #SaaS #CloudComputing #BetaProgram
Share Data Faster!⚡
Excited to share that Python-Blosc2 3.2.0 is out! 🎉
I'm proud to have contributed the new blosc2.matmul() function, which enables efficient matrix multiplication on NDArray instances. I’ve written a blog post about optimizing chunks with @Blosc2 check it out at:
Did you know that you can use the @blosc2.jit decorator to accelerate your computations with NumPy? See how you can compute expressions between 10x and 100x by just adding a single line to your function.
a, b and c are NumPy arrays, but the @blosc2.jit decorator will convert them into proxies for NumPy that can leverage the new Blosc2 compute engine. No need to add loops manually, just add @blosc2.jit and it will do its magic!
Compress Better, Compute Bigger!
📢 We are happy to announce the release of Python-Blosc2 3.2.0!
This release introduces the new blosc2.matmul() function for computing matrix multiplication on NDArray instances. Special thanks to Ricardo Sales Piquer, our new intern, for his contribution to this feature. He has blogged about this: https://www.blosc.org/posts/optimizing-chunks-blosc2/
Lastly, we are excited to introduce WASM32 wheels for the first time. You can download them from the assets section on the release page: https://github.com/Blosc/python-blosc2/releases/tag/v3.2.0
Enjoy!
Celebrating 100 stars for Python-Blosc2 in GitHub! 🎉 🎉
https://github.com/Blosc/python-blosc2
Compress Better, Compute Faster
We've poured our hearts into creating comprehensive documentation for Python-Blosc2, aiming to make your experience smoother and more productive! Explore the latest resources, including guides and tutorials, at:
- https://www.blosc.org/python-blosc2/
- https://www.blosc.org/python-blosc2/getting_started/overview.html
- https://www.blosc.org/python-blosc2/getting_started/tutorials.html
Your feedback is invaluable in shaping these materials to better serve the community. Let us know what you think!
Compress Better, Compute Bigger!
📢 C-Blosc2 2.17.1 is out! We have fixed several things:
* Fix uninitialized memory access in newly added unshuffle12_sse2 and unshuffle12_avx2 functions
* Fix unaligned access in _sw32 and sw32_
* Fix DWORD being printed as %s in sprintf call
* Fix warning on unused variable (since this variable was only being used in the linux branch)
* `splitmode` variable was uninitialized if goto was triggered
See: https://github.com/Blosc/c-blosc2/releases
Compress Better, Compute Bigger!
