Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Editor's Note: The following is an article written for and published in DZone's 2021 Low-Code Development Trend Report.
If you ride on a sailboat in a steady breeze, it glides through the water effortlessly. Few things can compare to crossing a bay without the noise and commotion of a thundering internal combustion engine.
Notebooks are becoming the standard for prototyping and analysis for data scientists. Many cloud providers offer machine learning and deep learning services in the form of Jupyter notebooks. Other players have now begun to offer cloud-hosted Jupyter environments, with similar storage, compute and pricing structures. One of the main differences can be multi-language support and version control options that allow data scientists to share their work in one place.
Jupyter notebook environments are now becoming the first destination in the journey to productizing your data science project. The notebook environment allows us to keep track of errors and maintain clean code. One of the best features although simple is that the notebook would stop compiling your code if it spots an error. Regular IDE’s do not stop compilation even if an error is detected and, depending on the amount of code, it can be a waste of time to go back and manually detect where the error is located.
The new Db2 Data Management Console is a free browser-based user interface included with Db2 for Linux, UNIX, and Windows. It's more than a graphical user interface to monitor, manage, run, and optimize Db2. It is a set of open RESTful APIs and microservices for Db2.
Anything you can do in the user interface is also available through REST. You can also embed parts of the user interface into your own webpages, or Jupyter notebooks.
Continuing from the previous post, Two Years in the Life of AI, ML, DL, and Java, where I expressed my motivation, I mentioned our discussions, one of the discussions was that you can write in languages like Python, R, and Julia in JuPyteR notebooks. Most were not aware that you can also write Java and Scala in addition to Python, SQL, etc. with the help of Apache Zeppelin notebooks. And so, I wanted to share something to broaden everyone’s awareness of Apache Zeppelin and its features. The project itself is written in Java and is an open architecture, which means that Zeppelin can support anything as long as an interpreter for that thing has been provided.
In case I have lost some of you, here’s what I meant by JuPyteR notebooks and writing notebooks in different languages. Also, have a look at the list of kernels supported by JuPyteR notebook. In this post, however, we are covering Apache Zeppelin, how to get it to work, and how to use a couple of notes in the Zeppelin environment.