Stats

Basic Jupyter Scala repo stats
3
1,365
8.6
13 days ago

almond-sh/almond is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.

Jupyter Scala Alternatives

Similar projects and alternatives to Jupyter Scala

  • GitHub repo IJava

    A Jupyter kernel for executing Java code.

  • GitHub repo Metals

    Scala language server with rich IDE features 🚀

  • GitHub repo soci

    Official repository of the SOCI - The C++ Database Access Library

  • GitHub repo sparkmagic

    Jupyter magics and kernels for working with remote Spark clusters

  • GitHub repo xeus-sql

    xeus-sql is a Jupyter kernel for general SQL implementations.

  • GitHub repo allthekernels

    A multiplexer kernel for Jupyter

  • GitHub repo xeus-tidb

    A Jupyter kernel for TiDB

NOTE: The number of mentions on this list indicates mentions on common posts. Hence, a higher number means a better Jupyter Scala alternative or higher similarity.

Posts

Posts where Jupyter Scala has been mentioned. We have used some of these posts to build our list of alternatives and similar projects - the last one was on 2021-03-10.
  • Is there any editor or IDE that supports Ammonite with inline dependencies?
    reddit.com/r/scala | 2021-03-10
    I use Almond in JupyterLab, which has pretty solid code completion. In IntelliJ, you can create a scratch sc file and run lines of it in the Scala REPL. That's really convenient for code completion and I normally will use that when I'm testing something from a specific project.
  • Recommended option for "Java with different syntax"?
    reddit.com/r/java | 2021-03-03
    The UI part. There's only the scala REPL. I think the closest is a scala kernel for Jupyter notebooks, check this out: https://almond.sh/
  • An SQL Solution for Jupyter
    news.ycombinator.com | 2021-02-09
    We have used https://almond.sh/ to create a Spark SQL interpreter using Jupyter Notebooks - plus a whole lot more which you can see here: https://arc.tripl.ai/tutorial

    After seeing many companies writing ETL using code we decided it was too hard to manage at scale so provided this abstraction layer - which is heavily centered around expressing business logic in SQL - to standardise development (JupyterLab) and allow rapid deployments.