Native Python access to IPFS in Jupyter Notebooks

  In a previous post, we discussed using the new InterPlanetary File System (IPFS) protocol as a way to load data into Jupyter Notebooks. After experimenting with our previous example code for using IPFS, we decided that using IPFS would be more organic in a Notebook if you could use it from Python as a native library. The jStart team […]

Read more

Jupyter DeclarativeWidgets make data exploration easy!

Over the past year, we have built many demonstrations using declarativewidgets to create dashboards and good-enough applications in the Jupyter Notebook. We’ve discovered several patterns that are in great need of simplification. One of these patterns is data exploration. By data exploration, I mean the ability for the user to query and visualize a data set. In declarativewidgets, we make it simple to bind visual elements to […]

Read more

Load Your Data Into a Jupyter Notebook

You’ve heard all the flashy statistics about big data, like how every day more than 2.5 quintillion bytes of data is created and that more data has been created in the last two years alone than every previous year combined (IBM). Here’s another one to add to the list: 99.5% of newly created data is never analyzed (MIT). Only half a […]

Read more

Using R and Declarative Widgets in Jupyter Notebooks

In previous blogs posts such as: Declarative Widget System for Jupyter Notebooks and Adding Declarative Widgets to the Jupyter Notebook we introduced what declarative widgets are, technologies involved, and walked through a use case driven by a survey on user experience conducted by the Jupyter community. The end result of our work is that practitioners in the R community are no longer limited to a static […]

Read more

InterPlanetary File System (IPFS) on Jupyter

The fundamental concept of IPFS is that instead of looking for locations, as with HTTP, you look for the content of a file.  (erisindustries) When you say Blockchain, Git, BitTorrent, I hear: Directed acyclic graphs (like git) with hashed hierarchical checkpoints (like a blockchain or merkle trees) distributed peer to peer (like bit torrent). This is literally what IPFS is, and […]

Read more

Using Remote Kernels with Jupyter Notebook Server

Jupyter Notebook uses kernels to execute code interactively. The Jupyter Notebook server runs kernels as separate processes on the same host by default. However, there are scenarios where it would be necessary or beneficial to have the Notebook server use kernels that run remotely. A good example is when you want to use notebooks to explore and analyze large data […]

Read more

Adding Declarative Widgets to the Jupyter Notebook

Web-based notebooks have proven to be an extremely popular medium for mining data and sharing valuable insights.  They combine a traditional development console with the robustness of the world-wide-web, but often fail to leverage the full web browser capabilities to provide a rich, interactive user experience. We will look at a case where the Jupyter Declarative Widget Extension was used […]

Read more

Deploying Dynamic Dashboards

You’ve spent some time cleaning and manipulating data in Jupyter Notebook. You even added some interactive widgets to show off your results. Now you need to make this available to others. You could use nbconvert, but then you lose widget interactivity. And distributing the raw notebook file is far too technical for some users. How can we distribute our report […]

Read more

E pluribus unum – OpenStack Swift Manifest Objects

By default, the content of an OpenStack Swift object cannot be greater than 5 GB. However, you can use a number of smaller objects to construct a large object via the concept of segmentation. From OpenStack Large Object Support, “Segments of the larger object are uploaded and a special manifest file is created that, when downloaded, sends all the segments concatenated as […]

Read more

Spark on z/OS and Jupyter: fast, flexible analysis of mainframe data

Many enterprises are faced with the need to expand data processing access to users without impacting mission-critical transactional application environments. The trending approach to this problem is to move the data from these systems of record to a data warehouse. Moving data-at-rest to a mirrored data repository for analytics can yield costly side-effects such as expensive migration workloads, data concurrency and […]

Read more
1 2 3