What are the main features of apache/zeppelin?

The main features of apache/zeppelin are: Notebook Code Execution, Notebook-Based Data Collaboration, Apache Spark Analytics, Pluggable Storage Backends, JDBC Abstraction Layers, Notebook JDBC Querying, Interpreter API Integrations, Interpreter Session Management.

Zeppelin | Awesome Repos

Apache Zeppelin is a web-based notebook platform for interactive data analytics that supports executing code in over 20 languages within a single notebook. It provides a plugin-based interpreter architecture that allows the notebook to be extended with new languages and data sources, and includes a JDBC connector abstraction for connecting to any JDBC-compliant database. The platform also features session-isolated interpreter contexts, enabling separate interpreter instances per notebook or user with support for dependency injection and user impersonation.

The platform distinguishes itself through its integration with Apache Spark clusters for distributed data processing, supporting YARN, Mesos, and standalone modes. It offers a Helium application framework for extending the notebook UI with custom visualizations and applications, and provides Shiro-based authentication and authorization for role-based access control and per-notebook permission management. Notebooks can be saved to external storage backends such as Git, S3, Azure, or MongoDB through a pluggable storage interface, and paragraph outputs can be embedded on external websites for sharing and reporting.

The platform supports collaborative data analytics through web-based notebooks with live results and embedded outputs, and includes dynamic input form creation for parameterizing queries and code execution. It renders output in multiple formats including text, HTML, tables, network graphs, and Angular widgets using simple display directives.

Features

Notebook Code Execution - Executes code in over 20 languages within a web-based notebook, enabling interactive data exploration and analysis.
Notebook-Based Data Collaboration - Shares and collaborates on data analysis through web-based notebooks with live results and embedded outputs.
Apache Spark Analytics - A notebook environment that integrates with Spark clusters for distributed data processing and interactive analytics.
Pluggable Storage Backends - Saves notebooks to Git, S3, Azure, GCS, or MongoDB through a pluggable storage backend interface.
JDBC Abstraction Layers - Connects to any JDBC-compliant database by abstracting SQL execution behind a common driver interface.
Notebook JDBC Querying - Connects to any JDBC-compliant database to run SQL queries directly from a notebook.
Interpreter API Integrations - Adds support for new languages or data sources by writing a plugin that follows the interpreter API.
Interpreter Session Management - Manages separate interpreter instances per notebook or user, supporting dependency injection and user impersonation.
Plugin-Based Interpreter Frameworks - An interpreter framework that supports over 20 languages and data sources through a plugin-based architecture.
Interpreter-Based Plugin Systems - Extends the notebook with new languages and data sources by loading plugins that implement a standard interpreter API at runtime.
Multi-Format Notebook Outputs - Renders output as text, HTML, tables, network graphs, or Angular widgets using simple display directives.
Notebook Output Renderers - Renders notebook outputs as HTML, tables, network graphs, and Angular widgets using simple display directives.
Spark Cluster Connectivity - Runs distributed data processing on Spark clusters with support for YARN, Mesos, and standalone modes.
Shared Notebooks - Shares paragraph outputs on external websites by embedding them outside the notebook interface.
Custom Data Visualizations - Builds custom visualizations and display widgets to render data as tables, graphs, or interactive elements.
Notebook Storage Backends - Saves notebooks to cloud or version-controlled storage backends such as Git, S3, Azure, or MongoDB.
External Storage Integrations - Saves notebooks to Git, S3, Azure, or MongoDB for versioning, backup, and team collaboration.
Shiro-Based Access Controls - Restricts notebook access using Apache Shiro for role-based authentication and per-notebook permission control.
Per-Notebook Permission Controls - Controls read and write permissions per notebook to enforce data governance.
Notebook UI Extensions - Extends the notebook UI with custom visualizations, spells, and applications through a plugin-based extension system.
Dynamic Input Forms - Creates interactive form controls within a notebook paragraph to parameterize queries and code execution.
Science and Data Analysis - Web-based notebook for interactive data analytics.
Interactive Notebooks - Supports interactive analytics with multi-language notebook support.

polynote/polynote

4,595عرض على GitHub

Polynote is a polyglot notebook environment and interactive document system designed for executing code in multiple languages within a single document. It functions as a cross-language data analysis tool and a JVM language IDE, allowing users to combine executable code, rich text, and data visualizations to prototype and document technical workflows. The system is distinguished by its ability to share data and variables between different language runtimes, such as Python and the JVM. It uses cross-language object conversion and data wrapping to pass objects between runtimes, enabling multi-la

nteract/papermill

6,451عرض على GitHub

Papermill is a Jupyter notebook execution engine and parameterization framework designed to run notebooks programmatically. It allows users to inject custom input values into notebooks to execute the same logic across different datasets, transforming interactive notebooks into reproducible data science pipelines. The project functions as a language-agnostic notebook runner and orchestrator, supporting kernels for Python, R, Julia, and Scala. It is distinguished by its cloud-integrated runner capabilities, featuring built-in handlers to read and write notebooks directly to storage providers su

spark-notebook/spark-notebook

3,144عرض على GitHub

This project is an interactive, web-based notebook environment designed for distributed data science and large-scale computing. It serves as a development tool for executing code and performing data analysis specifically within the Apache Spark framework, providing a browser-based interface that combines code execution with reactive data visualization. The platform distinguishes itself through its deep integration with distributed infrastructure, allowing users to manage cluster resources, configure runtime dependencies, and isolate execution processes for individual notebooks. It supports co

Kotlin Jupyter is an interactive computing environment that enables the execution of Kotlin code within Jupyter notebooks. It functions as a kernel for the Java Virtual Machine, providing a platform for data analysis, rapid prototyping, and scientific computing research. The system manages the evaluation of code snippets by compiling them dynamically at runtime, allowing for real-time interaction and variable inspection. The project distinguishes itself through a sophisticated code transformation pipeline that intercepts and modifies user input to support custom syntax and automated logic. It

apachezeppelin

Zeppelin

Features

بدائل مفتوحة المصدر لـ Zeppelin

polynote/polynote

nteract/papermill

spark-notebook/spark-notebook

Frequently asked questions

سجل النجوم

Frequently asked questions

بدائل مفتوحة المصدر لـ Zeppelin

polynote/polynote

nteract/papermill

spark-notebook/spark-notebook

Kotlin/kotlin-jupyter