New: Airbyte Agents. Context-aware AI, built on your data.
Tutorials
Step-by-step guides for shipping agents and automations on live context with Airbyte.
Showing 1 - 30 of 113 results in Tutorials
Connect the Claude Agent SDK to Airbyte Agents and build a bug triage agent that reads new GitHub issues, cross-references Linear for duplicates, groups them by severity, and posts a summary to Slack.
Reduce token use and speed up LangChain agents with Airbyte’s context layer, unified data access, and agent-ready connectors.
See how Airbyte Agents turns Stripe dispute handling from a custom build into a Claude skill that pulls the right context and helps teams respond faster.
Learn how to use PyAirbyte to load data from Facebook marketing, store the data in Milvus (Zilliz) vector store and perform a short RAG demo (using OpenAI/LangChain).
Learn how to load data from Github into Weaviate using PyAirbyte, then to use source-github and its stream 'issues'.
Learn how to build a simple RAG (Retrieval-Augmented Generation) pipeline with Milvus Lite and PyAirbyte, for a fully local development in Python.
Learn how to use PyAirbyte to extract cryptocurrency data from CoinAPI.io, and load it to Snowflake, followed by a series of transformations and analyses to derive meaningful insights from this data.
Learn how to build robust ETL pipelines using Python, Docker, and Airbyte. A guide for data engineers covering setup, implementation, & best practices.
Learn how to load data from GitHub airbyte-source into Snowflake using PyAirbyte, and afterwards convert the stream data into vector.
Learn to build efficient data pipelines using Airbyte, dbt, and DuckDB. A comprehensive guide for data engineers with practical implementation steps.
Learn how to build a robust Large Language Model application using ChromaDB for vector storage and Airbyte for data integration, simplifying your AI development workflow.
Discover how to build efficient knowledge management systems using PyAirbyte and vector databases for streamlined data access.
Discover financial market monitoring using Airbyte and Polygon.io integration. Streamline data for actionable insights
Learn how to build a GitHub documentation chatbot with PyAirbyte and PG Vector for seamless data retrieval and enhanced user experience.
Streamline healthcare data integration with Airbyte's AI Assistant and FHIR API connector. Simplify workflows and improve insights.
Build a social media sentiment analyzer using Airbyte and Twitter API. Simplify data integration and analyze trends effectively.
Build a quick full-stack AI application which arranges your Asana tasks for you in order of priority using MIlvus, Airbyte Cloud, and Next.js.
Learn how to install and set up Qdrant, a powerful vector database for AI applications. This beginner's guide walks you through basic operations to manage and query embeddings.
Learn how to use PyAirbyte to extract product-related data from Shopify, followed by a series of transformations and analyses to derive meaningful insights from this data.
Lean how to use data stored in Airbyte's Snowflake Cortex destination to perform RAG by building a Product Assistant—an AI chatbot capable of answering product-related questions using data from multiple Airbyte-related sources.
Learn how to easily set up a data stack using Shopify, Airbyte, dbt, BigQuery, and Dagster. Pull Shopify data, put it into BigQuery, and play around with it using dbt and Dagster.
Build a full data stack that creates a table snapshot from a database and stores it in an Amazon S3 bucket as a JSONL file using Airbyte and then loads the snapshot file to a preferred data warehouse.
Build an "ELT simplified Stack" repository to pull Github data, put it into BigQuery, and play around with it using dbt and Prefect.
Learn how to build an end-to-end RAG pipeline, extracting data from S3 using Airbyte Cloud to load it on Vectara and set up a RAG there.
Learn how to build a full data stack using Airbyte Cloud, Terraform, and dbt to move data from Notion -> BigQuery -> Pinecone for interacting with fetched data through an LLM and form a full fledged RAG.
Learn how to use the PyAirbyte library to read records from Github, converts those records to documents, which can then be passed to LangChain for RAG.
Learn how to build an end-to-end RAG pipeline, extracting data from Salesforce using Airbyte Cloud to load it on Weaviate and set up a RAG there.
Learn how to build an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from Google Drive using Airbyte Cloud to load it on Snowflake Cortex.
Learn how to build an end-to-end RAG pipeline, extracting data from Microsoft Sharepoint using Airbyte Cloud, loading it on Milvus (Zilliz), and then using LangChain to perform RAG on the stored data.
Learn how to use PyAirbyte to extract cryptocurrency data from CoinAPI.io, followed by a series of transformations and analyses to derive meaningful insights from this data.
