12 docs tagged with "storage"

Dagster helps you use Azure Storage Accounts as part of your data pipeline. Azure Data Lake Storage Gen 2 (ADLS2) is our primary focus but we also provide utilities for Azure Blob Storage.

This integration allows you to connect to AWS Athena, a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Using this integration, you can issue queries to Athena, fetch results, and handle query execution states within your Dagster pipelines.

Using this integration, you can connect to an AWS Redshift cluster and issue queries against it directly from your Dagster assets. This allows you to seamlessly integrate Redshift into your data pipelines, leveraging the power of Redshift's data warehousing capabilities within your Dagster workflows.

The AWS S3 integration allows data engineers to easily read, and write objects to the durable AWS S3 storage -- enabling engineers to a resilient storage layer when constructing their pipelines.

The dagster-chroma library allows you to easily interact with Chroma's vector database capabilities to build AI-powered data pipelines in Dagster. You can perform vector similarity searches, manage schemas, and handle data operations directly from your Dagster assets.

Delta Lake is a great storage format for Dagster workflows. With this integration, you can use the Delta Lake I/O Manager to read and write your Dagster assets.

This library provides an integration with the DuckDB database, and allows for an out-of-the-box I/O Manager so that you can make DuckDB your storage of choice.

The Google Cloud Platform BigQuery integration allows data engineers to easily query and store data in the BigQuery data warehouse through the use of the BigQueryResource.

This integration allows you to interact with Google Cloud Storage (GCS) using Dagster. It provides resources, I/O Managers, and utilities to manage and store data in GCS, making it easier to integrate GCS into your data pipelines.

By integrating with lakeFS, a big data scale version control system, you can leverage the versioning capabilities of lakeFS to track changes to your data. This integration allows you to have a complete lineage of your data, from the initial raw data to the transformed and processed data, making it easier to understand and reproduce data transformations.

This library provides an integration with the Snowflake data warehouse. Connect to Snowflake as a resource, then use the integration-provided functions to construct an op to establish connections and execute Snowflake queries. Read and write natively to Snowflake from Dagster assets.

The dagster-weaviate library allows you to easily interact with Weaviate's vector database capabilities to build AI-powered data pipelines in Dagster. You can perform vector similarity searches, manage schemas, and handle data operations directly from your Dagster assets.