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Load Hevo Data Data In Python Using Dlthub

Leo Migdal
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load hevo data data in python using dlthub

Build a Hevo Data-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support. In this guide, we'll set up a complete Hevo Data data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below: We’ll show you how to generate a readable and easily maintainable Python script that fetches data from hevo_data’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load: You will then debug the Hevo Data pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Before getting started, let's make sure Cursor is set up correctly: We focus on the needs & constraints of Python-first data platform teams: how to write any data source, achieve data democracy, modernise legacy systems and reduce cloud costs. dlt (data load tool) is the most popular production-ready open source Python library for moving data. It loads data from various and often messy data sources into well-structured, live datasets. Unlike other non-Python solutions, with dlt library, there's no need to use any backends or containers. We do not replace your data platform, deployments, or security models.

Simply import it into your favorite AI code editor, or add it to your Jupyter Notebook. You can load data from any source that produces Python data structures, including APIs, files, databases, and more. In July we released the initial dltHub workflow that lets developers build dlt pipelines and reports with LLMs. Now developers are creating 10,000s of dlt sources each month with an AI code editor of their choice such as Cursor, Claude, Codex or Continue. dlt is an open-source Python library that loads data from various, often messy data sources into well-structured datasets. It provides lightweight Python interfaces to extract, load, inspect and transform the data.

dlt and the dlt docs are built ground up to be used with LLMs: LLM-native workflow will take you pipeline code to data in a notebook for over 5,000 sources. dlt is designed to be easy to use, flexible, and scalable: To get started with dlt, install the library using pip (use clean virtual environment for your experiments!): If you'd like to try out dlt without installing it on your machine, check out the Google Colab demo or use our simple marimo / wasm based playground on this docs page. Use dlt's REST API source to extract data from any REST API. Define the API endpoints you'd like to fetch data from, the pagination method, and authentication, and dlt will handle the rest:

Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,or a GPT-4 assisted development playground—dlt can be dropped in anywhere. 🚀 Join our thriving community of likeminded developers and build the future together! dlt supports Python 3.9 through Python 3.14. Note that some optional extras are not yet available for Python 3.14, so support for this version is considered experimental. Load chess game data from chess.com API and save it in DuckDB: Try it out in our Colab Demo or directly on our wasm-based playground in our docs.

Welcome to my public learning journey with dlt — an open-source Python library that makes it easy to build robust, modern ELT (Extract-Load-Transform) pipelines with minimal code. This repository is a hands-on, beginner-to-advanced guide to learning dlt. It includes weekly examples, use cases, and walkthroughs — from ingesting data from APIs and databases to integrating with Airbyte, dbt, and cloud warehouses. Whether you're new to data engineering or looking to streamline your pipeline development, this repo will guide you through: Each week, I explore a new feature or use case, share my learnings, and post a tutorial or project here. You can also follow along on LinkedIn for bite-sized updates and context.

I'm Mahadi Nagassou, a data scientist passionate about simplifying AI and data engineering for African developers and organizations. Through this project, I hope to make learning dlt easy and accessible for anyone curious about building clean, scalable data pipelines with Python. Join our Slack community or book a call with our support engineer Violetta. This documentation provides a guide on using the dlt library to load data from GitHub repositories into The Local Filesystem. The GitHub verified source allows you to extract data on issues, pull requests, and events using the GitHub API. The data can then be stored in a local folder, creating a data lake with formats such as JSONL, Parquet, or CSV.

By leveraging the open-source dlt library, you can efficiently manage and analyze your GitHub data locally. For more detailed information on the GitHub API, visit GitHub Documentation. dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference. First you need to install the dlt library with the correct extras for The Local Filesystem:

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from GitHub to The Local Filesystem. You can run the following commands to create a starting point for loading data from GitHub to The Local Filesystem: dlt (data load tool) is an open source Python library that loads data from often messy data sources into well-structured, live datasets. It automates all your tedious data engineering tasks, with features like schema inference, data normalization and incremental loading. Run it where Python runs - on Airflow, serverless functions, notebooks.

No external APIs, backends, or containers, scales on micro and large infra alike. With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. User-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Customize our verified data sources, or any part of the code to suit your needs. Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,or a GPT-4 assisted development playground—dlt can be dropped in anywhere. 🚀 Join our thriving community of likeminded developers and build the future together!

dlt supports Python 3.9+. Python 3.13 is supported but considered experimental at this time as not all of dlts extras have python 3.13. support. We additionally maintain a forked version of pendulum for 3.13 until there is a release for 3.13. Load chess game data from chess.com API and save it in DuckDB: Explore ready to use sources (e.g.

Google Sheets) in the Verified Sources docs and supported destinations (e.g. DuckDB) in the Destinations docs. This repository contains Jupyter notebooks and more extensive projects that illustrate various methods for loading data into different destinations (e.g. Weaviate database) using the dlt library. To run the notebooks, you will need credentials for the tools being used. They are added to the .dlt folder.

For instance, if you're working on a Weaviate notebook, you will have to add Weaviate credentials. Refer to the notebooks to find out which credentials are needed. Project demos are more extensive compared to the notebook ones and have their own README files. Refer to each project for more details. This repository is licensed under the Apache License 2.0. Please refer to the LICENSE.txt file for more details.

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