Spark Notebook alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view Spark Notebook alternatives based on common mentions on social networks and blogs.
10.0 10.0 Spark Notebook VS MLLibApache Spark - A unified analytics engine for large-scale data processing
9.9 0.0 Spark Notebook VS PredictionIOPredictionIO, a machine learning server for developers and ML engineers.
9.8 9.2 L2 Spark Notebook VS ZeppelinWeb-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
9.6 10.0 Spark Notebook VS BigDLBuilding Large-Scale AI Applications for Distributed Big Data
7.7 6.7 Spark Notebook VS SquantsThe Scala API for Quantities, Units of Measure and Dimensional Analysis
7.5 0.0 Spark Notebook VS FACTORIEFACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
6.1 0.0 Spark Notebook VS ND4SND4S: N-Dimensional Arrays for Scala. Scientific Computing a la Numpy. Based on ND4J.
4.9 0.0 Spark Notebook VS Compute.scalaScientific computing with N-dimensional arrays
4.5 7.1 Spark Notebook VS OpenMOLEWorkflow engine for exploration of simulation models using high throughput computing
C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.
2.8 3.8 Spark Notebook VS MGOPurely functional genetic algorithms for multi-objective optimisation
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Spark Notebook or a related project?
The Spark Notebook allows performing reproducible analysis with Scala, Apache Spark and the Big Data ecosystem.
Apache Spark is available out of the box, and is simply accessed by the variable
Multiple Spark Context Support
One of the top most useful feature brought by the Spark Notebook is its separation of the running notebooks. Each started notebook will spawn a new JVM with its own SparkSession instance. This allows a maximal flexibility for:
- dependencies without clashes
- access different clusters
- tune differently each notebook
- external scheduling (on the roadmap)
We achieve maximum flexibility with the availability of multiple
sparkContexts by enabling [metadata driven](./docs/metadata.md) configuration.
The Spark Notebook supports exclusively the Scala programming language, the Unpredicted Lingua Franca for Data Science and extensibly exploits the JVM ecosystem of libraries to drive an smooth evolution of data-driven software from exploration to production.
The Spark Notebook is available for *NIX and Windows systems in easy to use ZIP/TAR, Docker and DEB packages.
All components in the Spark Notebook are dynamic and reactive.
The Spark Notebook comes with dynamic charts and most (if not all) components can be listened for and can react to events. This is very helpful in many cases, for example:
- data entering the system live at runtime
- visually plots of events
- multiple interconnected visual components Dynamic and reactive components mean that you don't have write the html, js, server code just for basic use cases.
Go to [Quick Start](./docs/quick_start.md) for our 5-minutes guide to get up and running with the Spark Notebook.
C'mon on to Gitter to discuss things, to get some help, or to start contributing!
- [Explore the Spark Notebook](./docs/exploring_notebook.md)
- [HTML Widgets](./docs/widgets_html.md)
- [Visualization Widgets](./docs/widgets_viz.md)
- [Notebook Browser](./docs/notebook_browser.md)
- [Notebook Configuration and Metadata](./docs/metadata.md)
- [Using Cluster Configurations](./docs/using_cluster_tab.md)
- [Versioned notebook storage with Git(hub)](./modules/git-notebook-provider/README.md)
- [Running on Clusters and Clouds](./docs/clusters_clouds.md)
- Advanced Topics
- [Using Releases](./docs/using_releases.md)
- [Building from Sources](./docs/build_from_source.md)
- [Creating Specific Distributions](./docs/build_specific_distros.md)
- [Creating your own custom visualizations](./docs/custom_charts.md)
- [User Authentication](./docs/authentication.md)
Basic, Form & Kerberosauth, and many more via
pac4j(OAuth, OpendID, ...)
- Passing the logged in user to Secure Hadoop+YARN clusters via the [
- Advanced: How to Develop/improve
- [Overview of Project structure](./docs/code_structure.md)
Skymind - Deeplearning4j
Spark Notebook gives us a clean, useful way to mix code and prose when we demo and explain our tech to customers. The Spark ecosystem needed this.
It allows our analysts and developers (15+ users) to run ad-hoc queries, to perform complex data analysis and data visualisations, prototype machine learning pipelines. In addition, we use it to power our BI dashboards.
|Kensu||website||Lifting Data Science to the Enterprise level|
|Agile Lab||website||The only Italian Spark Certified systems integrator|
|CloudPhysics||website||Data-Driven Inisghts for Smarter IT|
|Aliyun||product||Spark runtime environment on ECS and management tool of Spark Cluster running on Aliyun ECS|
|EMBL European Bioinformatics Institute||website||EMBL-EBI provides freely available data from life science experiments, performs basic research in computational biology and offers an extensive user training programme, supporting researchers in academia and industry.|
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