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Making vanilla web-fontend (project with lit, web component) secured (discussions with chatGpt)

Query: I wn complete seperate .css/.scss and .html template file for my webcomponent.js like in angular. But with complete security. In lit. Use any js lib like domPurify, etc. But I wan complete safety. How? ChatGPT said: Thought for 16s Totally doable. Here’s a secure Lit setup with fully separate .html + .scss files , sanitized with DOMPurify , and rendered into the Shadow DOM without sacrificing performance. Folder layout src / lib/ safe- html .js components/ app-shell .html app-shell .scss app-shell .js 1) Install deps npm i dompurify npm i -D sass Vite will compile .scss . We’ll import the compiled CSS as text and attach it via adoptedStyleSheets (fast + memory-friendly). 2) app-shell.html (pure template file) Put your Carbon UI Shell markup here (no scripts). Example stub: < cds-header aria-label = "My App" > < cds-header-name prefix = "IBM" >[Platform] </ cds-header-name > < cds-header-glo...
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Some Database reporting tools (chatGpt answers)

 

1. Metabase

  • Description: An open-source business intelligence tool that allows you to create dashboards and visual reports from PostgreSQL data.
  • Features:
    • Easy to set up and connect to PostgreSQL.
    • Create graphical reports and dashboards with minimal technical knowledge.
  • Use Case: Great for real-time visual dashboards.

2. Redash

  • Description: Another open-source tool that integrates with PostgreSQL and provides dashboard creation and SQL-based query-building tools.
  • Features:
    • Easy query interface.
    • Visualization widgets (charts, graphs, etc.).
  • Use Case: For more flexible and extensive visualization and reporting from your database.

3. Grafana

  • Description: Best known for monitoring, but also provides PostgreSQL data source integration for creating complex graphical reports and dashboards.
  • Features:
    • Real-time reporting and dashboard creation.
    • Supports SQL queries from PostgreSQL.
  • Use Case: Advanced dashboards with various visualizations for real-time data tracking.
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You said:

compare metabase, grafina, superset
ChatGPT said:
ChatGPT

Here's a comparison of Metabase, Grafana, and Apache Superset based on their features, strengths, and use cases:

1. Metabase

  • Type: Business intelligence (BI) tool.
  • Target Users: Non-technical users, analysts, and small teams.
  • Key Features:
    • Easy-to-use, drag-and-drop interface for creating dashboards and visualizations without writing SQL.
    • Supports SQL queries for advanced users.
    • Simple charts, dashboards, and question-building for various data types.
    • Great for quick exploratory analysis.
    • Ease of Use: Very beginner-friendly, ideal for non-developers or teams that need easy setup and usage.
  • Real-Time Data: Offers live or near-real-time dashboards if your database supports it.
  • Customization: Limited customization in visualizations compared to Grafana or Superset.
  • Integration: Connects to multiple data sources like PostgreSQL, MySQL, MongoDB, and Google Analytics.
  • Limitations:
    • Limited advanced visualizations and customization.
    • More focused on exploratory data analysis than in-depth reporting or monitoring.
  • Best For:
    • Small to mid-sized teams that want to explore data without complex setup.
    • Non-technical users who want fast insights with minimal technical overhead.

2. Grafana

  • Type: Monitoring and analytics platform (originally for time-series data).
  • Target Users: DevOps, engineers, and technical teams that need live data and real-time monitoring.
  • Key Features:
    • Highly customizable dashboards, especially for time-series data like stock prices, tick data, etc.
    • Excellent support for real-time/live data visualizations.
    • Integrates with a wide range of data sources, including PostgreSQL, Prometheus, InfluxDB, and Elasticsearch.
    • Visualizations: Rich and highly customizable charts, graphs, and alerts for monitoring purposes.
    • Ease of Use: Requires more technical setup but is highly flexible for customization.
  • Real-Time Data: One of the best tools for live, real-time data monitoring. Ideal for financial data analysis, including stock market data.
  • Customization: Very high. You can build custom dashboards, set up alerts, and configure each panel in detail.
  • Integration: Wide range of plugins for different data sources and visualization formats.
  • Limitations:
    • Requires more technical skills to configure and maintain.
    • Less intuitive for non-technical users compared to Metabase.
  • Best For:
    • Technical users or teams that need highly customized, real-time dashboards.
    • Use cases involving live financial market data, system monitoring, or DevOps.

3. Apache Superset

  • Type: Data exploration and visualization platform.
  • Target Users: Data scientists, engineers, and analysts who want advanced data exploration capabilities.
  • Key Features:
    • Supports advanced SQL-based analysis.
    • Offers a variety of chart types, including time-series, candlestick charts (for stock analysis), and heatmaps.
    • More complex than Metabase but allows deeper customization for technical users.
    • Visualizations: Advanced visualizations for complex reports. Custom dashboards with deep analytics capabilities.
    • Ease of Use: Steeper learning curve than Metabase but offers more customization and control.
  • Real-Time Data: Supports real-time data visualization but requires more setup compared to Grafana.
  • Customization: High. Users can write custom SQL queries, apply filters, and build complex dashboards.
  • Integration: Works with databases like PostgreSQL, MySQL, SQLite, and also cloud services like Amazon Redshift.
  • Limitations:
    • Requires more technical knowledge than Metabase.
    • Not as optimized for real-time monitoring as Grafana.
  • Best For:
    • Teams that need advanced analytics, complex dashboards, and SQL-based querying.
    • Technical users or data scientists who want to explore and visualize data in detail.

Summary:

  • Metabase: Easiest to use, ideal for small teams and non-technical users who want quick insights without writing SQL. It’s great for general data exploration but lacks deep customization.

  • Grafana: Best for real-time data monitoring and custom visualizations, especially for technical users. It's excellent for live financial data and monitoring but requires more technical setup.

  • Apache Superset: Provides advanced data exploration capabilities with deep customization. Suitable for data scientists and engineers who need detailed, SQL-based analytics but aren’t focused purely on real-time monitoring.

Recommendation:

  • If you prioritize real-time monitoring and live data (e.g., stock tickers), Grafana is likely the best fit.
  • For advanced analytics and complex reports based on historical and live data, Superset offers more flexibility.
  • If you want a user-friendly, exploratory tool for non-technical users, Metabase is a good starting point.
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You said:
grafana, superset good, but any such reporting tool from gnu?
ChatGPT said:
ChatGPT

There isn't a direct GNU reporting tool equivalent to Grafana or Superset, but there are a few open-source alternatives within the GNU ecosystem or with GPL-compatible licenses. Some of these might not have the same real-time monitoring capabilities, but they are highly customizable and can integrate with databases like PostgreSQL.

1. GNU Data Language (GDL):

  • Purpose: GDL is a scientific data visualization and analysis tool, primarily used for large datasets and complex visualizations.
  • Features: It has strong data analysis and visualization capabilities, and though it's not specifically built for real-time data or stock market reporting, it can be adapted for complex analysis.
  • Usage: Best for scientific datasets, but with effort, it can be adapted for financial data visualization.

2. GNU Octave:

  • Purpose: Octave is a numerical computation tool and can be used for data visualization through scripts.
  • Features: Octave has strong capabilities for plotting and data visualization, including integration with databases like PostgreSQL. It would require some scripting to set up real-time data processing.
  • Usage: Primarily used for data analysis, Octave can handle large datasets and has built-in graphing capabilities, but it's more manual compared to Grafana/Superset.

3. ReportLab (GPL compatible):

  • Purpose: Although not part of GNU, ReportLab is a powerful open-source Python library (under the BSD license) for generating dynamic PDFs.
  • Usage: It's mainly for creating static reports but can generate graphical reports and data visualizations from PostgreSQL. However, it won't provide real-time monitoring like Grafana.
  • Customization: You would need to script the reporting part, but it's a highly flexible option.

4. GNUPlot:

  • Purpose: GNUPlot is a graph plotting program that can visualize data from multiple sources, including databases like PostgreSQL.
  • Usage: Like Octave, it requires scripting and manual setup to integrate with your stock market data. It doesn't offer dashboards but can plot real-time data if configured properly.
  • Features: Simple yet powerful plotting tools with extensive customization options.

5. LibreOffice Base (part of the LibreOffice suite):

  • Purpose: LibreOffice Base is a database management tool similar to Microsoft Access.
  • Features: It can generate forms, reports, and charts from PostgreSQL databases.
  • Usage: While it doesn't handle real-time data well, it's useful for generating static reports and basic visualizations from your stock market data.

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