BI, dashboards and data viz

This project is a collection of structured study notes and conceptual breakdowns designed for the AWS Certified Cloud Practitioner exam. It serves as a technical reference and study guide, organizing cloud service details and architectural principles to assist in certification preparation. The knowledge base is built using markdown files and includes curated cheat sheets and interactive mind-map visualizations. These tools map complex certification topics into visual hierarchies to enable drill-down study paths and rapid revision. The materials cover a wide range of cloud capabilities, including core infrastructure, security governance, and the shared responsibility model. It provides detailed references for compute, storage, networking, and database services, as well as guidance on cloud economics and cost management. The repository utilizes Git-based versioning to track updates to the study materials.

Metabase is a business intelligence platform designed to connect to various storage systems and relational databases for data exploration, visualization, and reporting. It provides a centralized environment where users can build queries through a graphical interface or raw code, transforming raw information into interactive dashboards and charts. The platform is built to support self-service analytics, allowing non-technical team members to extract insights without requiring deep knowledge of database syntax. The platform distinguishes itself through a metadata-driven modeling layer that abstracts complex database schemas into user-friendly business entities. It includes an automated workflow engine that enables users to trigger external processes and update records directly from the interface, bridging the gap between data analysis and operational action. For organizations requiring external distribution, the software provides an embedded analytics solution that allows secure integration of dashboards into third-party websites and applications, supported by sandboxing to isolate visual components. Beyond core visualization, the system incorporates artificial intelligence to assist with query generation and data summarization through natural language interactions. It maintains strict data governance through granular role-based access control, ensuring that permissions are managed consistently across all connected information assets. The platform handles the full lifecycle of data retrieval, including orchestration, caching, and translation of high-level inputs into database-specific syntax.

This platform is a modular, metadata-driven framework designed for building custom business applications and data management systems without traditional coding. It functions as a low-code environment where data models, user interfaces, and business logic are defined through visual configurations rather than hardcoded views. The architecture supports multi-tenant isolation, allowing multiple independent applications to run within a single shared memory space while maintaining strict logical separation of data and configurations. What distinguishes this system is its deep integration of artificial intelligence across the entire development and operational lifecycle. It features an AI-powered engine capable of generating complete data models, interfaces, and workflows from natural language prompts. Beyond initial construction, the platform embeds intelligent agents into business processes to handle tasks such as lead scoring, sentiment analysis, and automated decision-making. These agents can be assigned unique personas and operational boundaries, and they collaborate within a centralized orchestration layer to automate complex, cross-system business logic. The platform provides a comprehensive suite of enterprise-grade capabilities, including visual data modeling, role-based access control, and automated workflow orchestration. It supports extensive system extensibility through a plugin-based architecture, enabling the dynamic loading of custom database collections, API endpoints, and frontend components. Furthermore, it includes robust tools for enterprise data synchronization, system auditing, and multi-application management, ensuring that complex business requirements can be met within a unified, scalable environment.

OpenBB is a financial data platform and investment research terminal designed to aggregate, normalize, and distribute market data across analytical workflows. It functions as a comprehensive ecosystem that bridges disparate financial data providers with custom applications, spreadsheets, and internal modeling infrastructure. The platform distinguishes itself through a provider-based data abstraction layer that normalizes heterogeneous financial APIs into a consistent, schema-driven format. This architecture supports quantitative research automation and the construction of interactive, widget-based dashboards, allowing users to maintain control over data within secure, self-hosted, or private infrastructure environments. Beyond its core terminal interface, the project provides a modular, plugin-driven architecture for integrating proprietary data feeds and external services. These capabilities enable the embedding of live market and historical datasets directly into custom software products and business intelligence platforms, ensuring consistent data availability for cross-platform analysis.

qsv is a high-performance command line toolkit for querying, transforming, and analyzing comma-separated value files. It functions as a data wrangling interface and a tabular data profiler, featuring a query engine capable of executing SQL statements and joins directly on flat files without requiring a database. The project is distinguished by its ability to process massive datasets that exceed available system memory. This is achieved through disk-based external memory processing, including multithreaded merge sorting, on-disk hash tables for deduplication, and lightweight file indexing for rapid row slicing. The toolkit covers a broad range of data operations, including schema inference, statistical profiling, and format translation between CSV, JSON, and Excel. It also provides capabilities for data cleaning, pivoting, and pseudonymization, alongside extensibility through a domain-specific language and external scripting integration. Pre-compiled binaries are distributed via Debian packages to simplify installation.

D3 is a modular library providing low-level primitives for creating data-driven visualizations. It functions as a flexible framework that allows for direct control over visual presentation by mapping abstract data dimensions to graphical properties, such as position, color, and size, without imposing predefined chart abstractions. The library distinguishes itself by offering specialized tools for complex data representation, including algorithmic layouts for hierarchical structures and geographic projection utilities for mapping spherical coordinates. It also includes a comprehensive suite for managing user interactions, enabling the creation of interactive selection areas that respond to mouse and touch input. Beyond visualization, the project provides a collection of utilities for document manipulation and data processing. These tools allow developers to query elements, apply data-driven transformations, and perform operations such as ordering, grouping, and summarizing datasets to prepare them for rendering in vector or bitmap contexts.

react-chartjs-2 is a data visualization library that provides a set of React components acting as a wrapper for Chart.js. It allows for the rendering of interactive charts and graphs within a React application using a declarative approach. The library manages HTML5 canvas charting components by tying the lifecycle of Chart.js instances to the mounting and unmounting of the React component tree. It translates component props into the configuration objects required by the underlying engine to map datasets to visual elements. The project covers a range of frontend integration capabilities, including the development of data visualization dashboards, interactive reporting, and web application analytics.

Chart.js is a declarative data visualization framework that renders interactive, responsive charts directly onto an HTML5 canvas element. It functions as a configuration-driven engine, transforming structured datasets into complex graphical representations by merging user-defined settings with global defaults. The library utilizes a high-performance rendering pipeline that executes drawing commands during each animation frame to maintain smooth visual feedback. The project distinguishes itself through a modular, extensible architecture that allows developers to register custom scales, controllers, and plugins to modify the internal lifecycle of a chart. This design enables the creation of specialized visual behaviors and the integration of diverse data formats within a single view. To ensure responsiveness and efficiency, the engine includes built-in decimation algorithms that filter large datasets, preventing performance degradation when rendering high volumes of information. Beyond its core rendering capabilities, the library provides a comprehensive suite of tools for managing axes, scales, and multi-series data comparisons. Developers can precisely control the appearance of grid lines, tick labels, and stacking behaviors to ensure data remains readable across various screen sizes. The system also supports advanced interaction handling, allowing for the identification of specific data points under the cursor to provide immediate feedback to the end user.

Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The web UI renders multi-gigabyte timelines in the browser without a backend server, using virtual scrolling and WebGL. Perfetto also provides an instrumentation SDK for emitting custom trace events from C++, Rust, or Java code, and includes a dedicated memory profiler that captures heap snapshots, allocation call stacks, and object graphs for leak detection. Beyond core tracing, the toolset offers command-line automation for batch trace collection and non-interactive analysis, plus import and export for formats such as Chrome JSON, Firefox profiles, Android Systrace, pprof, and Fuchsia traces. The trace processor supports a PerfettoSQL dialect with module imports, macro definitions, and span-join operators for complex correlation queries. The UI includes timeline navigation with flow events, track pinning and filtering, flamegraph visualization, and a command palette for keyboard-driven workflows. The viewer can also be embedded in external web pages via iframes and programmatic cross-window messaging. Documentation and prebuilt binaries are available for Android, Linux, macOS, and Windows, with SDK bindings for Python and Rust.

Grafana is an observability data platform designed to aggregate metrics, logs, and traces from diverse sources into a unified environment. It functions as a centralized interface for visualizing complex telemetry data, transforming raw streams into interactive dashboards that support real-time system health tracking and performance monitoring. The platform distinguishes itself through a plugin-based modular architecture that integrates disparate databases, cloud services, and monitoring tools via a standardized data abstraction layer. This framework allows for the dynamic loading of external components to support varied data sources and visualization types without requiring modifications to the core codebase. Additionally, the system incorporates a rule-based alerting engine that evaluates incoming data streams against defined thresholds to trigger automated notifications for incident response. Beyond its core visualization and alerting capabilities, the platform provides tools for infrastructure performance monitoring and operational data analysis. It utilizes a declarative, component-driven interface to manage dashboard states and a compiled backend to process high-throughput queries and API requests. The system maintains configuration persistence and state consistency across distributed instances through a centralized metadata storage layer.

This project is a distributed, document-oriented database system designed to store information in flexible, hierarchical structures. It supports horizontal scaling through automated sharding and maintains high availability across global clusters using a multi-node replication protocol. By executing multi-document operations as atomic units, the system ensures data integrity and consistency across distributed environments. The platform distinguishes itself by integrating advanced vector-based indexing, which enables semantic similarity searches alongside traditional geospatial and lexical queries. It functions as an enterprise-grade data platform, incorporating granular access controls, encryption, and auditing mechanisms to meet the requirements of regulated production environments. These capabilities allow for the management of large-scale datasets while maintaining the flexibility of a schema-less storage model. The system provides a comprehensive suite of tools for database administration, including command-line utilities for infrastructure management, data migration, and performance monitoring. It supports integration with container orchestration platforms and offers standardized client libraries to facilitate connectivity across various programming languages and business intelligence tools.

This project is a serverless service that generates dynamic, themeable visual summaries of software development activity. It functions as an automated metadata visualizer, transforming raw platform logs and repository metrics into resolution-independent vector graphics that can be embedded directly into markdown environments. The service distinguishes itself by offering highly configurable, query-parameter-driven rendering that allows users to customize the visual presentation of their coding patterns, language proficiency, and repository details. It supports both real-time generation via serverless functions and the creation of static image files through automated workflows, providing flexibility in how data is fetched and displayed. The platform aggregates disparate data points from multiple sources to provide comprehensive insights into development habits and project metadata. Users can deploy private instances of the service to maintain full control over caching strategies, authentication tokens, and rate limit management.

DataEase is an open-source, self-hosted business intelligence platform designed for building interactive data visualizations and managing analytical reporting. It provides a centralized environment where users can construct dashboards through a drag-and-drop interface, connecting to diverse data sources including relational databases, data warehouses, and external APIs. The platform distinguishes itself through its focus on embedded analytics and enterprise-grade governance. It allows for the seamless integration of charts, dashboards, and management modules into third-party web applications using secure iframe containers and token-based authentication. To support complex organizational needs, it includes granular role-based access control, row-level data filtering, and hierarchical organization management, ensuring that data remains secure and isolated across different departments. Beyond core visualization, the system offers extensive automation and connectivity features. It supports automated report scheduling and distribution, cross-source data modeling, and a plugin-based architecture that allows for the addition of custom data sources and visualization types. The platform also includes robust monitoring tools, such as threshold-based alerting and execution logging, to maintain operational visibility over automated tasks. The system is built to be highly configurable, offering options for platform branding, global variable definitions, and comprehensive identity management through integrations with external authentication providers.

TrendRadar is a market intelligence tool designed to aggregate and analyze external information sources for monitoring shifts in consumer behavior and industry patterns. It functions as a visual data analytics dashboard, transforming raw market data into interactive charts and insights through a component-based interface. The platform utilizes a declarative state management system where application behavior is governed by a centralized configuration object. This architecture supports interactive dashboard development, allowing users to manipulate data sets and visualize emerging trends over time. Changes to the configuration state are handled through event-driven synchronization, ensuring that data representations remain consistent across the interface. The system incorporates a structured configuration management workflow, utilizing a schema-driven approach to validate user-defined settings and parameters. This environment includes a dedicated editor for adjusting the filters and metrics used to track information, supported by a build process that optimizes assets for browser delivery.

Baserow is a self-hosted, no-code relational database platform built on PostgreSQL. It provides a spreadsheet-like interface for structuring and managing data without writing code, while exposing all database resources via a REST API to support headless architectures. The platform distinguishes itself by integrating large language models and embedding servers to power AI assistants and automated data generation. It further extends its utility as a no-code application builder, allowing users to create custom internal portals, dashboards, and business tools using visual logic and managed data. The system covers a broad range of capabilities, including business process automation with visual triggers, collaborative workspace management, and flexible data visualization through kanban boards, calendars, and timelines. It also supports advanced extensibility via a plugin system for custom field types and view filters, and executes user-defined scripts within a secure webassembly sandbox. Deployment is supported across various environments using Docker Compose, Helm charts for Kubernetes, and cloud infrastructure templates.

This project is a client-side rendering engine that transforms declarative, text-based syntax into visual diagrams directly within the browser. By utilizing a domain-specific language, it allows users to define complex structures—such as software architectures, process flows, and system behaviors—without the need for manual layout configuration. The library functions as a browser-based runtime that parses these definitions into intermediate abstract syntax trees, which are then processed by specialized engines to generate high-fidelity, resolution-independent graphics. The system distinguishes itself through a modular architecture that decouples diagram types into independent plugins, allowing for a wide range of visualizations including sequence diagrams, entity relationship models, and project timelines. To ensure security when processing untrusted input, the library supports sandboxed rendering within isolated frames. It also features automatic rendering capabilities, which monitor the document object model to detect and visualize diagram definitions embedded within standard web content. Beyond its core rendering engine, the project supports a documentation-as-code workflow by integrating with various development environments, productivity platforms, and content frameworks. This enables the inclusion of version-controlled, dynamic visuals in technical guides and wikis. The library is designed for flexible deployment, offering support for content delivery network integration to facilitate implementation without requiring local build processes.

Highcharts is a JavaScript charting library and interactive data visualization framework used to render complex datasets as visual charts and maps. It functions as a customizable SVG charting engine that produces scalable vector graphics for data series. The library allows for the creation of interactive data visualizations with configurable colors, labels, and themes. It also provides a cross-platform visualization wrapper to embed these web-based charts into native iOS and Android mobile applications. The toolkit supports the development of custom dashboards and provides options to build custom library bundles, combining specific modules into single files to reduce payload size.

This project is an open-source, privacy-focused web analytics platform designed for high-throughput data ingestion and multi-tenant data management. It provides a cookie-less tracking engine that captures visitor interactions using ephemeral request metadata, ensuring comprehensive traffic visibility while maintaining strict privacy standards. The architecture utilizes an event-driven ingestion pipeline and aggregated metric storage to decouple data collection from processing, enabling efficient long-term retrieval and responsive dashboard performance. What distinguishes this platform is its emphasis on first-party data collection and proxy-based routing. By allowing tracking requests to be routed through a custom domain, the system effectively masks analytics traffic as internal requests, bypassing ad-blocking software and privacy filters that typically interfere with client-side scripts. This approach, combined with server-side event processing, ensures that site owners maintain accurate traffic data even when browser-based limitations are present. The platform offers a broad capability surface for managing complex organizational needs, including granular role-based access control, SAML-based single sign-on, and automated reporting workflows. Users can programmatically manage site configurations, integrate external data sources, and export raw event logs for deep analysis in third-party business intelligence tools. The system also supports advanced conversion funnel tracking, allowing teams to define and measure specific user journeys and revenue-generating actions across multiple websites from a centralized dashboard.

Weekly is an automated workflow orchestrator and content curation platform designed to manage the lifecycle of technical information. It functions as a static site generator and newsletter publication system, enabling users to aggregate, organize, and distribute curated resources through structured markdown files and automated deployment pipelines. The platform distinguishes itself through its modular pipeline processing, which connects discrete tasks such as web data extraction, generative AI workflows, and optical character recognition. By integrating browser-based scraping with database-backed content management, it allows for the automated collection and synchronization of information across multiple platforms and syndication feeds. Beyond its core curation capabilities, the system supports a broad range of utility functions including document processing, metadata analysis, and interactive data visualization. It provides tools for managing digital libraries, enforcing code standards, and orchestrating event-driven notifications via webhooks, ensuring that content remains searchable, current, and accessible.

Glance is a self-hosted web portal designed to aggregate real-time data, system monitoring, and content feeds into a single, customizable dashboard. It functions as a centralized interface for managing internal tools and network services, allowing users to secure their portal with password authentication and define the application's visual identity and layout through structured configuration files. The platform is built on a modular widget engine that treats individual dashboard components as isolated units. This architecture enables users to arrange widgets into custom pages and columns, with each component independently fetching and rendering data from external sources, service status checks, or system interfaces. The system supports a wide range of integrations, including containerized workload monitoring, server resource tracking, and various web-based content feeds. Beyond its core monitoring capabilities, the application includes productivity utilities such as task management and custom search interfaces. It provides flexibility for advanced users through custom API data visualization, which allows for the transformation of raw JSON responses into formatted widgets using custom templates. The system is designed to operate behind reverse proxies and can be configured to interact directly with host-level interfaces and container runtimes.