Bauplan vs DataLad

Bauplan is a data lakehouse for running Python and SQL pipelines directly on Apache Iceberg tables in a user's object storage, without managing servers or clusters. It uses serverless execution for Python pipelines and supports Python, SQL, and mixed workloads on the same tables. Data branches, isolated runs, atomic publishes, and rollback are intended to safeguard changes to production data. Bauplan also describes workflows where AI agents build pipelines, explore data, and manage a lakehouse through its MCP Server and Skills. Integrations listed include Airflow, Prefect, Dagster, Temporal, DBOS, Jupyter, Marimo, Streamlit, Snowflake, BigQuery, Metabase, Superset, Fivetran, and Airbyte. Bauplan says customer data stays in the customer's S3 bucket and offers PrivateLink or bring-your-own-cloud options. Its free Sandbox uses a shared environment where all data is public, which the company recommends for toy projects and demos rather than company data. Dedicated costs $700 a month after the first 14 days free and includes a dedicated private instance, data in the customer's S3, 10GB memory, and unlimited queries, users, and agents. Enterprise pricing is custom. Bauplan states that it is SOC 2 Type II compliant.

DataLad is a free, open-source system for managing file-based datasets on Linux, macOS, and Windows. It uses Git and git-annex to version files of any size without custom data structures, central infrastructure, or third-party services. Provenance records support reproducible workflows, and linked datasets can form deeply nested hierarchies managed with recursive commands. Users can work through a graphical interface or command line, with operations to create and clone datasets, retrieve content when needed, save changes, and drop or push content. Built-in export commands support services such as GitHub and Figshare; compatibility also includes Dropbox and Amazon S3. Installation documentation covers Python, datalad-installer, Git, and git-annex. The handbook describes using git-annex with GnuPG to encrypt data during storage and transport, and credentials can be held in the operating system’s encrypted keyring through Python keyring. A privacy caveat is that sensitive information saved in Git remains visible in revision history even after later removal. Help is available through Matrix chat, weekly office hours, and GitHub issues. DataLad and its associated documentation use the MIT license.