Easily set up a data stack using Airbyte, dbt, BigQuery, and Dagster to pull weather data from WeatherStack API, put it into BigQuery, and play around with it using dbt and Dagster.
Configure an error analysis stack utilizing Sentry, Airbyte, Snowflake, dbt, and Dagster.
Learn how to use data stored in Airbyte's Vectara destination to perform RAG.
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 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.
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 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.
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 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 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 add custom sources built from the Connector Builder to PyAirbyte, Airbyte's open-source Python library.
Learn how to use PyAirbyte to extract data from Github, followed by a series of transformations and analyses to derive meaningful insights from this data. In particular, we demonstrate PyAirbyte capabilities for extracting data incrementally.