Using Programming Frameworks with CrateDB¶
Application programming frameworks integrating with CrateDB.
Many of them are built on top of the Python programming language, making it easy to use the Python libraries that you know and love.
A few quick examples about how to use relevant frameworks together with CrateDB.
CrateDB’s SQLAlchemy dialect implementation provides fundamental database adapter infrastructure to framework integrations.
Dash¶
Dash is a low-code framework for rapidly building data apps in Python, based on Plotly. Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs, directly to your analytical Python code.
Dash is a trusted Python framework for building ML & data science web apps. Many specialized open-source Dash libraries exist that are tailored for building domain-specific Dash components and applications.
Dash Enterprise
Dash Enterprise is Plotly’s paid product for building, testing, deploying, managing, and scaling Dash applications organization-wide, advertised as the Premier Data App Platform for Python.
When building Dash apps in a business setting, Dash Enterprise supports you to deploy and scale them, plus integrate them with IT infrastructure such as authentication and VPC services, in order to deliver faster and more impactful business outcomes on AI and data science initiatives.
Dash Enterprise enables the rapid development of production-grade data apps within your business. Python has taken over the world, and traditional BI dashboards no longer cut it in today’s AI and ML driven world. Production-grade, low-code Python data apps are needed to visualize the sophisticated data analytics and data pipelines that run modern businesses.
Plotly Dash Course - Session 1.
Gradio¶
Gradio is an open source programming framework for quickly creating and sharing machine learning model demo applications, written in Python.
Creating a user interface only requires adding a couple lines of code to your project.
It does not require any experience with HTML/JS/CSS, or web hosting.
Gradio can be embedded in Python notebooks, or presented as a web application.
Once you’ve created an interface, you can permanently host it on Hugging Face.
How to Build Machine Learning APIs Using Gradio.
hvPlot and Datashader¶
hvPlot is a familiar and high-level API for data exploration and visualization. Datashader is a graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly.
It is used on behalf of the hvPlot package, which is based on HoloViews, from the family of HoloViz packages of the PyViz ecosystem.
With Datashader, you can “just plot” large datasets and explore them instantly, with no parameter tweaking, magic numbers, subsampling, or approximation, up to the resolution of the display.
hvPlot sources its power in the HoloViz ecosystem. With HoloViews, you get the ability to easily layout and overlay plots, with Panel, you can get more interactive control of your plots with widgets, with DataShader, you can visualize and interactively explore very large data, and with [GeoViews], you can create geographic plots.
hvPlot and Panel: Visualize all your data easily, from notebooks to dashboards | SciPy 2023.
Plotly¶
Plotly Open Source Graphing Libraries make interactive, publication-quality graphs. Line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, bubble charts, and maps.
The supported programming languages / libraries / frameworks are Python, R, Julia, JavaScript, ggplot2, F#, MATLAB®, and Dash.
Based on Plotly, Dash is a low-code framework for rapidly building data apps in Python.
Streamlit¶
Streamlit is an open source application programming framework for quickly sketching out Python data applications. It provides fast, interactive prototyping, and live editing.
Build dashboards, generate reports, or create chat apps using beautiful, easy-to-read code.
No in-depth knowledge of HTML/JS/CSS needed, the framework offers elegant default styling, which can be adjusted when applicable.
Transform Python scripts into interactive web apps in minutes, instead of weeks.
Build upon a range of Streamlit components.
Optionally use their Community Cloud platform to deploy, manage, and share your application.
Streamlit 101 - A faster way to build and share data applications.