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in-process analytical database engine

Ranking updated Jun 30, 2026

For a python library for analytical database queries, the strongest matches are microsoft/powerbi-desktop-samples (This repository contains sample files for Power BI Desktop), duckdb/duckdb (This is the DuckDB engine itself, not a tutorial) and catchorg/catch2 (Catch2 is a C++ testing framework and has nothing). satwikkansal/wtfpython and dotnet/benchmarkdotnet round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.

We curate open-source GitHub repositories matching “duckdb python tutorial”. Results are ranked by relevance to your query — pick filters below to narrow, or refine with AI.

in-process analytical database engine

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  • microsoft/powerbi-desktop-samplesmicrosoft avatar

    microsoft/powerbi-desktop-samples

    1,929View on GitHub↗

    This repository contains sample files for Power BI Desktop, not a tutorial or example for using DuckDB from Python; it does not cover the DuckDB Python API, Jupyter, or any of the required features.

    Data Samples
    View on GitHub↗1,929
  • duckdb/duckdbduckdb avatar

    duckdb/duckdb

    38,805View on GitHub↗

    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 adapti

    This is the DuckDB engine itself, not a tutorial or example repository—while it is the subject of the tutorials you are looking for, it does not provide the hands-on Python-focused learning material or guide that your search requires.

    C++Analytical DatabasesColumnar EnginesEmbedded Databases
    View on GitHub↗38,805
  • catchorg/catch2catchorg avatar

    catchorg/Catch2

    20,198View on GitHub↗

    Catch2 is a comprehensive framework for C++ software validation, providing an environment for unit testing, integration verification, and performance analysis. It enables developers to define and execute automated test suites and micro-benchmarks directly within their applications. The framework is distinguished by its header-only distribution, which allows for integration into existing build systems without requiring complex external dependencies. It utilizes a hierarchical section-based execution model that supports behavior-driven testing, allowing for shared setup and teardown logic acros

    Catch2 is a C++ testing framework and has nothing to do with DuckDB, Python, or tutorial repositories, so it is completely unrelated to the visitor's intent.

    C++Performance Benchmarking
    View on GitHub↗20,198
  • satwikkansal/wtfpythonsatwikkansal avatar

    satwikkansal/wtfpython

    36,990View on GitHub↗

    wtfpython is a behavioral reference and catalog of language edge cases for the Python programming language. It serves as a guide to common development mistakes and ambiguous code structures that lead to unexpected results. The project identifies counter-intuitive code patterns and unexpected behaviors to help developers avoid pitfalls and logical errors. It utilizes a collection of curated examples to document language quirks and specific formatting conflicts, such as indentation errors. The reference includes verification of how specific code snippets behave across different versions of the

    This repository is a collection of Python language quirks and edge cases, not a tutorial or example repository for using DuckDB from Python — it does not cover DuckDB, Jupyter notebooks, SQL queries, or data frame integration.

    PythonUse Case Examples
    View on GitHub↗36,990
  • dotnet/benchmarkdotnetdotnet avatar

    dotnet/BenchmarkDotNet

    11,428View on GitHub↗

    BenchmarkDotNet is a library and tool suite for measuring the execution time and memory allocation of .NET code. It utilizes statistical sampling and warm-up iterations to determine the stability and precise execution speed of specific methods. The project provides a JIT disassembly viewer to inspect processor disassembly and analyze how the compiler executes code paths. It includes a memory allocation profiler that tracks managed and native memory traffic to identify efficiency bottlenecks. Additionally, a runtime performance comparator allows the same benchmarks to be executed across differ

    This repository is a .NET benchmarking tool (BenchmarkDotNet) with no connection to DuckDB, Python, Jupyter notebooks, or SQL query examples — it does not teach DuckDB from Python.

    C#Performance Benchmarking
    View on GitHub↗11,428
  • turboway/bigdata_analyseTurboWay avatar

    TurboWay/bigdata_analyse

    5,238View on GitHub↗

    This project is a collection of big data frameworks and pipelines, including an Apache Hive analysis framework, a behavioral data analytics platform, a predictive analytics engine, and real-time data pipelines. It provides the infrastructure for building Extract, Transform, Load (ETL) workflows to process large datasets for distributed storage and SQL-based analysis. The system supports diverse analytical implementations, such as a predictive engine using linear regression for value forecasting and a real-time architecture that moves data through message brokers for immediate reporting. It in

    This repository is a collection of big data analysis frameworks and pipelines built around Apache Hive, with no mention or use of DuckDB, so it does not serve as a tutorial or example for learning DuckDB from Python.

    PythonData Analysis WorkflowsETL Workflows
    View on GitHub↗5,238
  • connorferster/handcalcsconnorferster avatar

    connorferster/handcalcs

    5,805View on GitHub↗

    handcalcs is a mathematical documentation generator and Python LaTeX calculation renderer. It serves as an automated calculation sheet tool that converts Python code and numeric calculations into formatted LaTeX mathematical documentation, functioning as both a symbolic math formatter and a Jupyter notebook math extension. The project transforms Python variable names into Greek symbols, subscripts, and standard mathematical notation. It converts code into formatted mathematical expressions that display the original formula, the numeric substitution, and the final result, allowing for the crea

    handcalcs is a mathematical documentation tool for rendering Python calculations as LaTeX, with no relation to DuckDB, databases, SQL queries, or data frame integration—it cannot serve as a DuckDB Python tutorial.

    CSSJupyter Notebook Integrations
    View on GitHub↗5,805
  • reactotron/reactotronreactotron avatar

    reactotron/reactotron

    15,564View on GitHub↗

    Reactotron is a desktop application and collection of developer tools designed for monitoring runtime errors, analyzing network requests, and inspecting state within JavaScript and React environments. It serves as a debugger for both React JS and React Native projects, providing a visual interface to monitor internal application state and performance. The tool provides specialized inspectors for React Native mobile applications and React web applications. It allows for the real-time tracking of state changes and the dispatching of actions to a state manager to test different application scena

    Reactotron is a debugging tool for React and React Native applications, not a tutorial or example repository for using DuckDB with Python, so it is completely unrelated to your search.

    TypeScriptPerformance Benchmarking
    View on GitHub↗15,564
  • nuclio/nuclionuclio avatar

    nuclio/nuclio

    5,730View on GitHub↗

    Nuclio is a high-performance serverless framework designed for Kubernetes that automatically executes user functions when events arrive from HTTP endpoints, message queues, or streaming data platforms. It processes hundreds of thousands of events per second per function instance through efficient parallel workers, and can allocate functions to run on either CPU or GPU hardware to match workload requirements for data processing or machine learning tasks. The platform scales function instances down to zero when idle and wakes them on demand based on incoming event load, while providing an event

    Nuclio is a Kubernetes-native serverless framework for event-driven functions, with no connection to DuckDB or Python-based database tutorials, so it does not fit the search for a DuckDB Python example repository.

    GoJupyter Notebook Integrations
    View on GitHub↗5,730
  • sharkdp/hyperfinesharkdp avatar

    sharkdp/hyperfine

    28,316View on GitHub↗

    Hyperfine is a command-line benchmarking tool used to measure the execution time of shell commands through multiple runs and statistical analysis. It functions as a comparative benchmarking utility and a shell performance analyzer, allowing for the evaluation of multiple commands against a reference baseline to determine relative speed. The tool distinguishes itself by isolating actual command performance through shell overhead correction and the ability to bypass the shell entirely using system calls. It supports parameterized execution, enabling benchmarks to run across a range of varying i

    Hyperfine is a command-line benchmarking tool for measuring shell command execution time, with no connection to DuckDB, Python, or data analysis tutorials — completely off-topic for your search.

    RustPerformance Benchmarking
    View on GitHub↗28,316
  • show-me-the-code/pythonShow-Me-the-Code avatar

    Show-Me-the-Code/python

    4,226View on GitHub↗

    This project is a curated library of Python code examples, educational resources, and programming tutorials. It functions as an educational repository designed to teach Python language fundamentals through practical implementation tasks, real-world exercises, and functional code snippets. The collection covers a diverse range of implementation examples, including the development of interactive websites and message boards using web frameworks. It also features scripts for audio speech processing, automated media processing for images, and the extraction of data from web content. Additional ca

    This repository is a general collection of Python code examples across many domains, with no mention of DuckDB, SQL queries, Jupyter, or Pandas integration, so it does not match the specific DuckDB tutorial focus of this search.

    HTMLPractical Application Exercises
    View on GitHub↗4,226
  • facebook/follyfacebook avatar

    facebook/folly

    30,412View on GitHub↗

    Folly is a collection of high-performance C++ components designed as an extension to the C++ Standard Library for large-scale production environments. It provides specialized toolkits for memory management, concurrency, asynchronous workflows, and low-latency input and output operations. The project distinguishes itself through the provision of lock-free containers and bounded queues to minimize contention in multi-threaded applications, alongside a framework for managing deferred computations using futures and promises. It further offers specialized memory arenas and optimized implementation

    This is a C++ components library by Facebook with no connection to DuckDB, Python, or tutorials; it does not teach DuckDB usage from Python in any way.

    C++Performance Benchmarking
    View on GitHub↗30,412
  • jakevdp/pythondatasciencehandbookjakevdp avatar

    jakevdp/PythonDataScienceHandbook

    48,561View on GitHub↗

    This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

    This repository is a general Python data science handbook covering NumPy, pandas, and scikit-learn, but it does not mention DuckDB or its Python API, so it does not serve as a tutorial for using DuckDB from Python.

    Jupyter NotebookInteractive Data Science EnvironmentsInteractive NotebooksInteractive Shells
    View on GitHub↗48,561
  • creativetimofficial/material-kitcreativetimofficial avatar

    creativetimofficial/material-kit

    5,926View on GitHub↗

    Material Kit is an open-source UI component library that provides pre-styled Material Design elements for building responsive web interfaces with Bootstrap 5. It offers a collection of reusable components like buttons, inputs, navbars, cards, and modals that follow Google's Material Design guidelines, along with a 12-column flexbox grid system for fluid layouts that adapt to any screen size. The kit distinguishes itself by including pre-built page sections such as headers, feature blocks, pricing tables, and footers that can be combined into complete page layouts, reducing the time needed to

    This is a UI component library for Material Design web interfaces, completely unrelated to DuckDB or Python tutorials.

    HTMLUse Case Examples
    View on GitHub↗5,926
  • nautechsystems/nautilus_tradernautechsystems avatar

    nautechsystems/nautilus_trader

    20,056View on GitHub↗

    Nautilus Trader is a high-performance algorithmic trading framework built in Rust, designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive platform for managing multi-asset portfolios and interacting with diverse financial markets through a standardized connectivity suite. The system is engineered to handle high-frequency data processing and complex order execution while maintaining precise numerical accuracy across various asset classes. The framework distinguishes itself through an architecture centered on deterministic even

    Nautilus Trader is an algorithmic trading framework, not a DuckDB Python tutorial or example repository, so it does not match your search for hands-on DuckDB learning materials.

    RustPerformance Benchmarking
    View on GitHub↗20,056
  • infinitered/reactotroninfinitered avatar

    infinitered/reactotron

    15,513View on GitHub↗

    Reactotron is a desktop-based development environment designed for inspecting, monitoring, and manipulating mobile and web applications in real time. It functions as a centralized hub that connects to a running application via a persistent WebSocket connection, allowing developers to observe internal state, network traffic, and console output without manual instrumentation. The tool distinguishes itself through a modular plugin architecture that enables custom debugging commands and specialized extensions. It provides advanced diagnostic capabilities, including the ability to overlay design m

    Reactotron is a desktop debugging tool for React Native and React apps — it has no connection to DuckDB, Python, or data analysis, and therefore does not serve as a DuckDB Python tutorial or example repository.

    TypeScriptApplication Debugging ToolsReact Native DebuggersCross-Platform Development Tools
    View on GitHub↗15,513
  • asabeneh/30-days-of-pythonAsabeneh avatar

    Asabeneh/30-Days-Of-Python

    65,111View on GitHub↗

    This project is a structured educational curriculum designed to guide beginners through the fundamental concepts and syntax of the Python programming language. It functions as a self-paced technical training resource, providing a curated path for individuals to acquire core software development skills through a series of daily lessons and practical exercises. The guide distinguishes itself by combining theoretical explanations with hands-on coding tasks that cover the language's dynamic type system, interpreted execution model, and whitespace-based block scoping. It emphasizes the practical a

    This is a general-purpose Python learning curriculum that covers a wide range of topics including databases and data science, but it does not specifically teach DuckDB usage, its Python API, Jupyter integration, or provide DuckDB-examples, so it does not match the DuckDB-focused tutorial you are looking for.

    PythonBytecode InterpretersBuilt-in Data StructuresDynamic
    View on GitHub↗65,111
  • jackfrued/python-100-daysjackfrued avatar

    jackfrued/Python-100-Days

    183,425View on GitHub↗

    This project is a comprehensive, day-by-day curriculum designed to guide learners through the Python programming language and its professional applications. The content spans from fundamental syntax and object-oriented design to advanced topics including database management, web development, data analysis, and machine learning. The curriculum is structured into distinct modules that cover practical software engineering practices, such as version control, containerization, and system architecture. It also provides resources for technical interview preparation and an analysis of career paths wi

    This is a comprehensive Python curriculum covering many database topics, but it does not specifically focus on DuckDB or include dedicated DuckDB examples, so it is unlikely to provide the hands-on DuckDB tutorial you are seeking.

    Jupyter NotebookEducational ResourcesPython TutorialsMachine Learning Fundamentals
    View on GitHub↗183,425
  • ankidroid/anki-androidankidroid avatar

    ankidroid/Anki-Android

    10,755View on GitHub↗

    Anki-Android is an open-source education application designed for long-term memory retention through the use of spaced repetition. The platform enables users to create, manage, and study multimedia flashcards that support text, images, audio, and mathematical notation. It provides a structured environment for learning by scheduling review intervals based on an optimized algorithm that prioritizes content according to individual performance. The application distinguishes itself through its cross-platform synchronization capabilities, allowing users to maintain consistent study collections and

    This is a spaced-repetition flashcard app for Android, not a tutorial or example repository for using DuckDB from Python — it has no connection to DuckDB, Python notebooks, or SQL query examples.

    KotlinSpaced Repetition SystemsEducation ApplicationsFlashcards
    View on GitHub↗10,755
  • android/compose-samplesandroid avatar

    android/compose-samples

    22,893View on GitHub↗

    Compose Samples is a collection of reference implementations for the Jetpack Compose UI library, serving as a practical guide for building native Android user interfaces. It demonstrates the use of a declarative framework where Kotlin functions describe layout structures and data dependencies, enabling developers to construct modern, reactive interfaces. The repository highlights architectural patterns that prioritize maintainability and testability, such as layered organization and unidirectional data flow. It showcases how to implement adaptive layouts that automatically adjust to various s

    This repository contains Jetpack Compose samples for Android UI development, with no connection to DuckDB, Python, or data analysis workflows — it is entirely unrelated to the tutorial you are looking for.

    KotlinPerformance Benchmarking
    View on GitHub↗22,893
  • vitessio/vitessvitessio avatar

    vitessio/vitess

    20,788View on GitHub↗

    Vitess is a database clustering system for horizontal scaling of MySQL. It functions as a middleware layer that abstracts complex sharding and physical topology, allowing applications to interact with a distributed database environment through a unified interface. By intercepting and routing SQL queries across multiple shards, it enables large-scale data management while maintaining the appearance of a single database instance. The platform distinguishes itself through its ability to perform online schema migrations and distributed transaction coordination without requiring application downti

    Vitess is a MySQL clustering middleware, unrelated to DuckDB and Python; this repository contains no tutorial or example for DuckDB usage.

    GoPerformance Benchmarking
    View on GitHub↗20,788
  • vitest-dev/vitestvitest-dev avatar

    vitest-dev/vitest

    15,970View on GitHub↗

    Vitest is a high-performance testing framework designed for JavaScript and TypeScript applications. It provides an integrated environment that supports unit, integration, and browser-based testing, allowing developers to execute test suites natively without requiring separate build steps or complex configuration. The project distinguishes itself through a highly optimized execution model that leverages worker-thread isolation and on-demand module transformation to provide rapid feedback. It includes a comprehensive suite of mocking and spying utilities that allow for the interception of depen

    Vitest is a JavaScript testing framework, not a DuckDB Python tutorial or example repository — it has no connection to DuckDB, Python, Jupyter, or pandas, so it does not match your search intent.

    TypeScriptPerformance Benchmarking
    View on GitHub↗15,970
  • dlt-hub/dltdlt-hub avatar

    dlt-hub/dlt

    5,472View on GitHub↗

    dlt is a Python data ingestion tool and ETL pipeline framework designed to fetch data from diverse sources and persist it into structured destinations. It functions as a schema inference engine that automatically detects data types and flattens nested JSON structures into relational tables, moving data from sources to lakehouses, warehouses, or vector databases. The project distinguishes itself through AI-powered pipeline generation, using large language models to scaffold extraction code and connectors for REST APIs. It also supports multimodal vector storage and specialized population of ve

    dlt is a data ingestion and ETL framework, not a tutorial or example repository for learning DuckDB from Python — it does not provide step-by-step instruction or hands-on examples focused on DuckDB usage.

    PythonETL Workflows
    View on GitHub↗5,472
  • valyala/fasthttpvalyala avatar

    valyala/fasthttp

    23,264View on GitHub↗

    Fasthttp is a high-performance networking framework for Go, designed to maximize throughput and minimize memory overhead in demanding web applications. It functions as a specialized HTTP server and client library that prioritizes efficient resource management, allowing developers to build scalable services capable of handling massive concurrent traffic with minimal garbage collection pressure. The library distinguishes itself through a focus on zero-allocation processing and low-level optimization. It achieves this by recycling temporary request and response objects through managed pools and

    Fasthttp is a high-performance Go HTTP framework, not a DuckDB Python tutorial or example repository — it does not relate to DuckDB, Python, Jupyter, or data analysis at all.

    GoHigh-Performance HTTP ServersHTTP Request HandlingAsynchronous Request Handlers
    View on GitHub↗23,264
  • quii/learn-go-with-testsquii avatar

    quii/learn-go-with-tests

    23,510View on GitHub↗

    This project is an educational platform and tutorial series designed to teach the Go programming language through the practice of test-driven development. It provides a structured path for developers to master language fundamentals, concurrency, and standard library usage by building functional applications in small, verifiable increments. The core methodology centers on the test-driven development cycle, where failing tests are written before implementation to define requirements and ensure code correctness. This approach is applied across a wide range of practical scenarios, including the c

    This repository is a tutorial for learning Go programming through test-driven development, with no connection to DuckDB or Python, which is the opposite domain of what you are looking for.

    GoPerformance Benchmarking
    View on GitHub↗23,510
  • yutiansut/quantaxisyutiansut avatar

    yutiansut/QUANTAXIS

    9,955View on GitHub↗

    Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

    Quantaxis is a quantitative trading framework, not a hands-on tutorial or example repository for learning DuckDB from Python — it does not provide DuckDB API examples, notebooks, or educational content related to DuckDB.

    PythonBacktesting SimulationsQuantitative Trading PlatformsAccount Management
    View on GitHub↗9,955
  • getmoto/motogetmoto avatar

    getmoto/moto

    8,550View on GitHub↗

    Moto is a cloud service mockery framework and API mock server that simulates AWS infrastructure locally. It allows developers to test cloud-dependent code and verify infrastructure-as-code templates without deploying real resources or incurring costs. The project functions as an SDK interceptor that can patch existing service clients to redirect requests to a local mock environment. It can also be run as a standalone HTTP server, enabling any programming language to interact with the simulated endpoints. The framework covers a vast array of simulated capabilities, including data storage, com

    Moto is an AWS service mocking framework, not a DuckDB Python tutorial or example repository — it contains no DuckDB content, SQL examples, or data frame integration, making it completely unrelated to your search.

    PythonAPI Interception ProxiesCloud Service MocksCloud Service Simulators
    View on GitHub↗8,550
  • codeigniter4/codeigniter4codeigniter4 avatar

    codeigniter4/CodeIgniter4

    5,924View on GitHub↗

    CodeIgniter is a PHP web framework built on the Model-View-Controller pattern, designed for building full-stack web applications. It provides a lightweight toolkit with minimal configuration, organizing application logic into controllers, models, and views for clean separation of concerns. The framework includes a fluent query builder for constructing SQL statements programmatically, PSR-4 autoloading with namespace mapping, and a service-based dependency injection container for managing shared class instances. The framework distinguishes itself through its comprehensive set of built-in tools

    CodeIgniter 4 is a PHP web framework for building full-stack web applications, which has absolutely no connection to DuckDB, Python, Jupyter notebooks, or any of the tutorial/example features you need.

    PHPPerformance Benchmarking
    View on GitHub↗5,924
Compare the top 10 at a glance
RepositoryStarsLanguageLicenseLast push
microsoft/powerbi-desktop-samples1.9K—mitJan 26, 2026
duckdb/duckdb38.8KC++MITJun 16, 2026
catchorg/catch220.2KC++bsl-1.0Feb 18, 2026
satwikkansal/wtfpython37KPythonWTFPLJan 13, 2026
dotnet/benchmarkdotnet11.4KC#MITJun 23, 2026
turboway/bigdata_analyse5.2KPythonMITApr 27, 2023
connorferster/handcalcs5.8KCSSApache-2.0Jan 30, 2026
reactotron/reactotron15.6KTypeScriptMITMay 28, 2026
nuclio/nuclio5.7KGoApache-2.0Jun 16, 2026
sharkdp/hyperfine28.3KRustApache-2.0Apr 30, 2026

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