#java #apache_doris #business_intelligence #data_analysis #data_visualization #echarts #kettle #superset #tableau

DataEase is a free, open-source tool that helps you analyze data and see business trends easily. It connects to many types of data sources like databases and files, and you can create charts quickly by dragging and dropping. It's easy to use, even if you're not tech-savvy, and it has an AI feature that answers your data questions in natural language. You can also share your data safely with others. This tool is beneficial because it's simple, powerful, and free, making it accessible to everyone who needs to analyze data.

https://github.com/dataease/dataease

#csharp #asp_net_core #blazor #blazor_application #blazor_components #blazor_ui #blazor_ui_components #blazor_webassembly #bootstrap #bootstrap5 #charts #component_library #components #csharp #data_grid #data_visualization #datagrid #fluent #material #netcore #wasm

Radzen Blazor Components offer over 90 free and open-source UI controls for Blazor applications. These components are native to Blazor, meaning they are written in C# and don't rely on JavaScript frameworks. This makes them efficient and easy to use. You can install them from NuGet or build your own copy from source. The components are constantly updated with new features, and you have access to community support and optional dedicated support with a Radzen Professional subscription. This helps you develop Blazor applications quickly and effectively, with tools like a WYSIWYG design canvas and scaffolding for CRUD applications, making your development process smoother and more productive.

https://github.com/radzenhq/radzen-blazor

#typescript #apache #canvas #charting_library #charts #data_visualization #data_viz #echarts #svg #visualization

Apache ECharts is a free and powerful tool for creating interactive and customizable charts. It is written in JavaScript and easy to use, making it great for adding charts to your products. You can download it from the official website, use npm, or a CDN. It has lots of documentation and examples to help you get started. Using ECharts benefits you by allowing you to create professional-looking charts quickly and easily, which can enhance your data visualization and make your products more engaging.

https://github.com/apache/echarts

#clojure #analytics #bi #business_intelligence #businessintelligence #clojure #dashboard #data #data_analysis #data_visualization #database #metabase #mysql #postgres #postgresql #reporting #slack #sql_editor #visualization

Metabase is a simple, open-source tool that helps everyone in your company ask questions and learn from data without needing to know SQL. You can set it up in just five minutes and create interactive dashboards, define key metrics, and send data to Slack or email. It also allows you to set alerts for data changes and embed charts into your app. Metabase supports various databases and can be run almost anywhere. Using Metabase, you can make better decisions with your data easily and quickly, making it a valuable tool for your team.

https://github.com/metabase/metabase

#c_lang #alerting #cncf #data_visualization #database #devops #docker #grafana #influxdb #kubernetes #linux #machine_learning #mongodb #monitoring #mysql #netdata #observability #postgresql #prometheus #raspberry_pi #statsd

Netdata is a powerful monitoring tool that helps you keep an eye on your servers, containers, and applications in real-time. Here’s what you need to know Netdata collects data every second, giving you immediate insights into your system's behavior.
- **Zero-Configuration** Netdata uses ML to detect anomalies and patterns in your metrics, helping you identify issues before they become critical.
- **Scalability** Netdata monitors everything from system resources to application logs, providing a complete view of your infrastructure.
- **Energy Efficiency**: Studies have shown that Netdata is the most energy-efficient monitoring tool, consuming fewer resources than other solutions.

Using Netdata benefits you by providing real-time, high-resolution monitoring, automated anomaly detection, and advanced visualization tools, all while being highly scalable and energy-efficient. This makes it easier to manage and troubleshoot your systems effectively.

https://github.com/netdata/netdata

#python #ai #csv #data #data_analysis #data_science #data_visualization #database #datalake #gpt_4 #llm #pandas #sql #text_to_sql

PandaAI is a tool that lets you ask questions about your data using natural language. It's helpful for both non-technical and technical users. Non-technical users can interact with data more easily, while technical users can save time and effort. You can load your data, save it as a dataframe, and then ask questions like "Which are the top 5 countries by sales?" or "What is the total sales for the top 3 countries?" PandaAI also allows you to visualize charts and work with multiple datasets. It's easy to install using pip or poetry and can be used in Jupyter notebooks, Streamlit apps, or even a secure Docker sandbox. This makes it simpler and more efficient to analyze your data.

https://github.com/sinaptik-ai/pandas-ai

#shell #chart #charts #d3 #data_visualization #svg #visualization

D3.js is a free JavaScript library that helps create interactive and custom data visualizations. It offers great flexibility, allowing developers to make dynamic graphics using web standards. This means you can easily customize how your data looks and behaves on the web. The benefit of using D3.js is that it allows users to explore complex data in an engaging way, making it easier to understand and analyze information. Whether you're working with simple charts or complex networks, D3.js provides tools to bring your data to life interactively.

https://github.com/d3/d3

#typescript #data_visualization #database #database_schema #documentation #documentation_tool #entity_relationship_diagram #er_diagram #erd #nodejs #orm #postgresql #prisma #react_flow #reactjs #ruby_on_rails #sql #tbls #typescript #visualization #webassembly

Liam ERD is a tool that helps you create easy-to-understand diagrams of your database. It makes interactive diagrams that you can zoom in and out of, filter, and explore easily. This tool is useful for both small and large projects, handling over 100 tables with ease. It's simple to set up and is open-source, meaning you can contribute to it. Using Liam ERD helps you visualize complex database structures quickly, making it easier to understand and work with your data. This saves time and reduces errors compared to drawing diagrams manually.

https://github.com/liam-hq/liam

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

https://github.com/ml-tooling/best-of-ml-python

#typescript #data_visualization #geospatial_analysis #javascript #maps #python #visualization #webgl

deck.gl is a powerful tool that helps you create fast, interactive, and visually impressive maps and data visualizations using WebGL technology. It lets you turn large sets of data into layers like icons, polygons, and text, which you can view in different ways such as maps or 3D scenes. It works well with popular map providers like Google Maps and Mapbox, and supports easy interaction like clicking and filtering. You can use it simply by adding a script or installing it via npm or Python. This makes it easier for you to build custom, high-performance visualizations quickly and with less coding effort.

https://github.com/visgl/deck.gl