Endpoint SQL Querying Tools

Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volumes for trend and pattern identification. The platform includes capabilities for distributed database management and cluster monitoring through SQL system tables. It supports data retrieval via standardized query languages and web-based application programming interfaces.

This project is an automated security testing suite designed to detect and exploit database vulnerabilities. It functions as a command-line utility that streamlines the identification, verification, and exploitation of web application flaws by automating the injection of malicious payloads into input parameters. The tool provides a comprehensive framework for database enumeration, allowing users to extract schema information, user data, and system configurations from identified injection points. What distinguishes this tool is its sophisticated engine for dynamic payload adaptation and heuristic fingerprinting, which adjusts injection techniques in real-time based on server responses. It supports advanced post-exploitation capabilities, including remote command execution on the underlying host operating system and file system access through database-level vulnerabilities. To navigate restricted environments, the software incorporates out-of-band data exfiltration channels and a middleware pipeline for applying user-defined transformations to bypass security filters and web application firewalls. The suite covers a broad range of operational requirements, including stateful session management, anti-CSRF token handling, and extensive request customization. It supports various target specification methods, such as proxy log analysis and remote API management, while offering granular control over scan performance and detection thresholds. The software is distributed as a command-line application, with configuration management supported through external file loading and command-line arguments.

q is a command-line utility for the processing, filtering, and aggregation of tabular text and database files using standard SQL syntax. It functions as a query engine that treats CSV and TSV files, as well as standard input, as relational database tables. The tool distinguishes itself by providing a persistent cache layer that stores processed tabular data in a binary format to accelerate repeated queries on large datasets. It also maps individual filenames or stream identifiers to relational table names, enabling SQL joins across disparate text files. The project covers a broad range of data analysis capabilities, including automated schema detection for column types, tabular output formatting, and the ability to export processed in-memory datasets into physical SQLite database files. It integrates directly into Unix pipelines by accepting tabular data via standard input.

Druid is a database connection management and monitoring framework designed to maintain persistent, high-performance links between applications and relational databases. It functions as a resource manager that automates the lifecycle of connection pools, reducing the overhead associated with repeatedly opening and closing network connections. The project distinguishes itself through an integrated query analysis engine that decomposes database statements into structured components. This capability enables real-time security auditing, syntax validation, and metadata extraction, allowing for the enforcement of security policies and performance monitoring directly within the database communication flow. Furthermore, it provides a pluggable dialect abstraction layer that translates operations to ensure compatibility across various database management systems. Beyond its core pooling and analysis functions, the project includes diagnostic tools for tracking connection health and performance metrics. It supports configuration-driven setup, allowing for the external definition of driver settings, pool parameters, and validation rules to maintain stability under varying traffic loads.

Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools. The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orchestrates these interactions by mapping questions to the underlying semantic model, ensuring that AI-generated insights remain accurate and context-aware. Furthermore, Cube is designed for multi-tenant environments, offering robust infrastructure isolation, row-level security, and dynamic context injection to ensure that data access is strictly governed and personalized for every user or tenant. Beyond its core modeling and AI features, the platform includes a comprehensive suite of tools for performance optimization, including automated pre-aggregation caching and asynchronous query queuing. It supports a wide range of data sources and deployment models, from self-hosted containers to managed cloud environments. The system also provides extensive programmatic control over report management, dashboard publishing, and user identity synchronization, making it suitable for embedding interactive analytics directly into custom software applications.

The framework is a comprehensive penetration testing platform designed for the development, testing, and execution of security exploits. It serves as a research toolkit and automated assessment environment, enabling security professionals to identify and validate vulnerabilities within networked systems and infrastructure through repeatable, standardized procedures. The platform distinguishes itself through a modular architecture that supports reflective payload injection, allowing for the execution of code directly in memory without writing to disk. It utilizes an asynchronous event loop to manage high-performance, concurrent network connections and features a transport-agnostic communication layer that abstracts protocols to maintain persistent command and control. Users can extend the core functionality through a plugin system and define complex exploit logic using a domain-specific language. The framework provides robust capabilities for remote payload management, including the configuration of network settings like sleep intervals and timeout thresholds. It maintains state persistence across long-running sessions by storing discovered host information and vulnerability data in a relational database. The software is designed for cross-platform deployment, with installation support available for Linux, macOS, and Windows environments.

pgcli is an interactive command-line interface and database management tool for PostgreSQL. It functions as an interactive SQL shell and query editor that allows users to inspect schemas, manage connections, and run queries against PostgreSQL data sources. The tool is distinguished by its real-time, schema-aware autocompletion for keywords, tables, and columns, as well as dynamic SQL syntax highlighting. It provides safety mechanisms through transaction-aware guardrails that warn against or block destructive statements when no active transaction is detected. Broad capabilities include secure database connectivity via URIs, DSNs, and SSH tunneling, along with a customizable terminal interface supporting Vi keybindings and external editor integration. The system also handles data visualization through result-set paging and multiple formatting modes, while offering utilities for SQL data export and periodic query execution.

Goose is an extensible agentic AI platform designed for autonomous task orchestration and developer-centric assistance. It provides a workflow engine that manages complex, multi-step objectives by delegating tasks to specialized subagents, all while maintaining stateful session continuity. The system is built to integrate directly into terminal and coding environments, allowing for automated file manipulation and context-aware interaction. The platform distinguishes itself through a secure, sandboxed runtime environment that enforces granular permission controls and policy-driven guardrails. By utilizing a standardized protocol-based architecture, it allows users to connect external tools, services, and third-party models as modular extensions. This framework supports the creation of reproducible automation recipes, which can be configured, shared, and executed to standardize recurring workflows across different projects. Beyond its core orchestration capabilities, the system includes comprehensive developer tooling for session management, interaction logging, and terminal-based interfaces. It supports advanced automation tasks, including browser-based testing and external service integration, through a flexible extension lifecycle that allows for dynamic toolset adjustments during active sessions.

This project is an educational framework designed to teach the fundamentals of building core distributed systems and web services from scratch in Go. It provides a collection of modular implementations that demonstrate how to construct essential infrastructure components, including web servers, remote procedure call systems, distributed caches, and database abstraction layers. The framework distinguishes itself by focusing on the internal mechanics of these systems rather than providing a high-level abstraction for production use. It covers the implementation of complex architectural patterns such as consistent hashing for data distribution, least-recently-used cache eviction, and reflection-based service registration. By building these components manually, the project illustrates how to handle network connectivity, protocol negotiation, and service discovery in a distributed environment. Beyond core networking and storage, the repository includes implementations for machine learning primitives, such as neural network architectures and training loops, as well as tools for database interaction and object-relational mapping. It also incorporates various utility layers for logging, performance benchmarking, and concurrency management to provide a comprehensive view of system-level programming. The repository is structured as a series of guided modules, allowing developers to explore the implementation details of each system component through hands-on construction and testing.

This framework provides a development toolkit for building autonomous agents that utilize language models to solve complex, non-deterministic tasks. Its core design centers on a code-executing architecture where agents generate and run Python code snippets to perform logic, data manipulation, and tool interactions. By moving beyond structured data formats, the system enables agents to manage program flow and object state through iterative reasoning cycles. The project distinguishes itself through its focus on code-based agent implementation and secure execution environments. Developers can choose between code-generating agents for complex logic or structured tool-calling agents for reliable, schema-validated interactions. To ensure safety when running model-generated scripts, the framework supports isolated runtime environments, including containers and remote virtual machines, which prevent unauthorized system access while maintaining state across task cycles. The platform offers a comprehensive suite of capabilities for managing agentic workflows, including multi-agent orchestration, stateful memory management, and interactive planning. It provides a unified interface for integrating diverse language model providers and simplifies tool creation by automatically converting Python functions into executable tools via metadata and type hints. Users can monitor the decision-making process through an interactive interface that visualizes reasoning steps and supports manual intervention during task execution.

Redash is a self-hosted analytics platform and SQL data visualization tool. It provides a web-based SQL query editor for writing, executing, and scheduling database queries, and functions as a business intelligence dashboard for monitoring metrics via visual widgets. The platform distinguishes itself through its data source connectors, which integrate with various SQL, NoSQL, and API-based stores to retrieve information for analysis. It enables self-service analytics by allowing users to run queries with dynamic parameters and supports shared data reporting via public links or embedded dashboards. The system covers a broad range of capabilities, including a data visualization engine for creating charts and maps, automated data alerting for monitoring query thresholds, and role-based access control for managing user permissions. It also includes utilities for database schema browsing and exporting query results. Administration is supported through a command-line interface for system tasks and database schema initialization.

DuckDB is an in-process analytical database engine designed to run directly within an application process. As a zero-dependency, embedded system, it provides enterprise-grade SQL data processing capabilities without the overhead of managing a dedicated database server. It is built to handle complex analytical and aggregation tasks by storing and retrieving information in columns, allowing for high-performance relational data manipulation. The engine distinguishes itself through a columnar vectorized execution model that maximizes CPU cache efficiency during query operations. It employs adaptive query optimization to dynamically select execution plans at runtime and utilizes zero-copy ingestion to map external data formats directly into memory. To facilitate integration with analytical programming environments, the system supports high-performance data exchange through standardized memory formats and provides specialized connectors for Python, R, and Java. The project covers a broad capability surface, including advanced relational join operations, incremental result streaming for large datasets, and flexible data ingestion from various file formats. It supports complex data types and provides a comprehensive command-line interface for interactive session management and batch processing. The codebase is designed for portability, offering single-file amalgamation to simplify integration into external projects and build systems.

SpacetimeDB is a stateful, real-time database platform that executes application logic directly within the database engine. By unifying data storage and business logic, it allows developers to build applications where state transitions are processed through atomic, server-side functions. The platform maintains persistent connections to stream incremental updates to clients, ensuring that local caches remain synchronized with the server state at all times. The platform distinguishes itself by generating type-safe client interfaces directly from server-side schema definitions, ensuring consistent data structures across the entire application stack. It utilizes persistent event sourcing to record every state change to disk, which facilitates full data recovery and historical auditing. Furthermore, the system supports language-agnostic module deployment, allowing custom application logic to run within the database environment regardless of the source programming language. Beyond its core execution model, the platform provides a comprehensive suite of tools for managing the full application lifecycle. This includes centralized identity and access management, task scheduling for recurring operations, and granular team-based project administration. Developers can interact with the system through a command-line interface that supports local development, testing, and the generation of type-safe bindings. The platform offers flexible deployment options, supporting both managed cloud services and self-hosted infrastructure. It includes built-in monitoring and observability features, such as real-time log streaming and performance metrics, to assist in tracking data changes and function execution.

GPT Researcher is an autonomous agent framework designed to automate the process of gathering, synthesizing, and documenting information from diverse web and local sources. It functions as a research-oriented execution environment that orchestrates specialized agents to perform complex, multi-branch research tasks, transforming raw data into structured, factual, and cited reports. The project distinguishes itself through a graph-based orchestration layer that manages state transitions and information flow between specialized agents. It employs recursive tree-search execution to explore complex topics by branching into sub-queries, while a modular tool-calling interface allows for the integration of external search engines, databases, and specialized data retrieval servers. This architecture enables the system to perform deep, concurrent research while maintaining real-time progress tracking through non-blocking callback mechanisms. Beyond its core research capabilities, the framework supports hybrid knowledge synthesis by normalizing web-scraped content and local file formats into a unified context. It provides extensive tooling for report customization, including prompt-driven synthesis and the automatic generation of inline visual illustrations. The system is designed for integration into broader software ecosystems, offering asynchronous endpoints and containerized deployment options to facilitate its use within custom web applications or messaging platforms.

AlaSQL is a JavaScript SQL database engine that allows for the filtering, grouping, and joining of in-memory object arrays and JSON data. It functions as an in-memory SQL database and client-side data processor, enabling the execution of SQL statements against JavaScript arrays and external data sources in both browser and server environments. The project serves as a universal data query tool capable of performing relational joins across diverse sources, such as merging Google Spreadsheets, SQLite files, and remote APIs into a single result set. It also acts as an IndexedDB SQL wrapper, allowing complex queries and joins to be executed over browser-based storage. Its capabilities cover cross-format data integration, including the import and export of CSV, JSON, and multiple Excel workbook formats. The engine supports graph data analysis for identifying entity relationships and provides extensibility through custom SQL functions, plugin integration, and multi-stage aggregators. The system includes a command line interface for executing SQL statements and supports offloading database operations to web workers to prevent blocking the user interface.

OpenHands is an autonomous agent framework designed for software engineering workflows. It provides a modular platform for orchestrating AI agents that reason, plan, and execute tasks within isolated, containerized development environments. By integrating with standard version control and development tools, the system enables agents to autonomously navigate codebases, implement features, and resolve issues through iterative reasoning and tool execution. The platform distinguishes itself through a model-agnostic orchestrator that connects diverse language models to a unified tool registry. It supports complex, multi-agent collaboration via hierarchical task delegation, allowing parent agents to spawn and manage independent sub-agents for parallelized workflows. Security is managed through configurable action approval policies and real-time risk evaluation, ensuring that autonomous operations remain within defined safety boundaries. The system covers a broad capability surface including persistent conversation state management, automated code review, and web research automation. It features an event-driven architecture that serializes interactions into immutable logs, facilitating observability and time-travel debugging. Developers can extend agent functionality through custom skill definitions, plugin packages, and integration with external services via standardized protocols. The project provides a command-line interface for managing agent sessions, remote server deployments, and containerized workspace lifecycles. It is designed for extensibility, allowing users to configure agent behavior through structured objects, markdown-based definitions, and environment-specific settings.

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query execution, graph data manipulation, and continuous data flow processing. It includes capabilities for distributed job execution, interactive query shells, and the integration of user-defined functions. The project includes distributed cluster security with network traffic encryption and supports metadata management via Hive metastore integration.

This project serves as a centralized directory and interoperability hub for the Model Context Protocol, providing a curated collection of standardized service connectors that bridge artificial intelligence models with external software, databases, and APIs. It facilitates the integration of AI agents with diverse ecosystems by offering a registry of machine-readable interface definitions that enable dynamic tool discovery and structured context injection. The directory distinguishes itself by focusing on the protocol-based interoperability required for autonomous AI agents to interact with heterogeneous remote services. It emphasizes a decoupled request-response pattern and a bidirectional capability handshake, ensuring that AI hosts and servers can negotiate operational constraints and supported features before any tool invocation occurs. This architecture supports stateless service implementations, allowing for independent scaling and deployment of tools across various environments. The collection covers a broad functional range, including integrations for business productivity, data science, infrastructure management, and developer utilities. These connectors enable AI agents to perform tasks such as secure database querying, code execution, desktop automation, and persistent memory management. The repository acts as a community-driven resource for developers seeking to extend the operational range of their AI agents through modular, plug-and-play service integrations.

This project is a business intelligence suite and SQL data visualization platform used for data analysis, reporting, and monitoring. It provides a web application for exploring datasets and building interactive dashboards, complemented by a web-based SQL query editor for analyzing raw data from connected stores. The platform features a semantic data layer to define standardized metrics and dimensions, ensuring consistent data interpretation across reports. It includes a security framework with role-based access control to manage user permissions and authentication across shared dashboards. The system covers a range of capabilities including no-code data visualization for creating charts and geospatial maps, interactive dataset analysis, and SQL database integration. It also supports programmatic platform management and query automation through a REST API.

ClickHouse is a high-performance, columnar analytical database designed for real-time query execution and large-scale data aggregation. It functions as a distributed data warehouse capable of processing petabytes of information, while also providing an embedded engine that integrates directly into applications for native query capabilities without external dependencies. The system is built to handle high-throughput ingestion and complex analytical workloads, delivering millisecond-level latency for interactive dashboards and operational monitoring. The platform distinguishes itself through advanced storage and execution techniques, including vectorized query processing and a merge tree storage engine that maintains performance during massive insertions. It features adaptive subcolumn mapping for semi-structured data and supports native vector search for machine learning and generative AI applications. To facilitate efficient data movement, the engine utilizes zero-copy shared memory buffers, minimizing overhead when interacting with external analytical tools or processing diverse file formats like Parquet, JSON, and Arrow. Beyond its core storage and processing capabilities, the project provides a comprehensive suite of tools for observability, security, and data integration. It includes built-in support for natural language querying, automated workflow orchestration for AI agents, and extensive diagnostic features for query plan inspection. The platform also offers robust cloud infrastructure management, including support for private networking, compliant deployment strategies, and integrated billing consolidation.