data.all

Free plan APISelf-hostedWeb

What data.all is

data.all is an open source platform for managing and discovering data assets in AWS accounts. Its central catalog brings datasets, tables, folders, and dashboards into one searchable inventory, with filters for asset type, tags, region, and glossary terms. Datasets can hold structured tables or unstructured folders and use AWS S3, KMS, IAM, and Glue resources. Teams can share datasets across environments by granting selected roles read access through Lake Formation, without moving the data. The catalog can also import tables from Redshift Serverless and provisioned clusters.

For analysis and machine learning, data.all provides Athena worksheets, Jupyter notebooks and SageMaker Studio access, plus QuickSight dashboards. Administrators can organize teams and users across environments. Authentication can use Amazon Cognito federated with a corporate identity provider or an external OIDC provider; the platform relies on identity-provider group information. Deployments can be internet facing or VPC facing, where the UI and APIs are reachable only inside the VPC. The documented AWS setup needs a tooling account and at least one deployment account in matching regions, with administrator credentials for both. A Docker Compose local setup is also documented, though AWS credentials are needed to create environments and datasets.

Who it suits

data.all suits teams that need to catalog, discover, analyze, and share data assets across AWS environments. It also fits data scientists who want access to SageMaker based notebooks and teams that need administrators to manage users and groups across environments.

What it does well

Catalogs datasets, tables, folders, and dashboards with search filters.
Shares datasets through Lake Formation without moving the data.
Imports tables from Redshift Serverless and provisioned clusters.
Includes Athena worksheets, SageMaker notebooks, and QuickSight dashboards.
Offers internet facing and VPC facing deployment architectures.
Open source and free.

What to know first

AWS deployment requires at least two AWS accounts in matching regions.
Setup requires administrator credentials for tooling and deployment accounts.
AWS credentials are needed locally to create environments and datasets.
File uploads are disabled in the documented example configuration.

Getting started

Visit the data.all website and review the deployment guide.
Choose an internet facing or VPC facing AWS architecture.
Prepare a tooling account and at least one deployment account in matching regions.
Use administrator credentials for both accounts during deployment.
For local use, follow the documented Docker Compose setup; use AWS credentials to create environments and datasets.

Verdict

Choose data.all if your team needs an open source AWS platform for finding, analyzing, and sharing data assets across environments. Its catalog, Lake Formation sharing, and analytics tools make it a fit for AWS centered data work. The main setup consideration is the required multi account AWS deployment and administrator access.

Questions

Is data.all free?

Yes. It is free and open source.

Which platforms does it support?

Its listed platforms are API, self-hosted, and web.

What does the AWS deployment require?

It requires a tooling account and at least one deployment account, with matching selected regions. Administrator credentials for both accounts are required during setup.

Can teams share data without moving it?

Yes. Dataset sharing across environments can grant selected team roles read access through Lake Formation without moving the data.

Which AWS services and tools does it integrate with?

Documented modules include Glue, Lake Formation, S3, Athena, SageMaker, QuickSight, and HealthOmics Ready2Run workflows. It can also import tables from Redshift Serverless and provisioned clusters.

Facts from data.all's own pages

Administration: Administrators can organize and manage teams and users across environments. · data-dot-all.github.io, 8 Oct 2026
Analytics and ML: The platform provides Athena worksheets, Jupyter notebooks through SageMaker, SageMaker Studio access, and QuickSight dashboards. · data-dot-all.github.io, 8 Oct 2026
Audit: For structured data, SQL queries from EMR, Redshift, Glue Jobs, and Athena are captured through Lake Formation. · data-dot-all.github.io, 8 Oct 2026
AWS integrations: The documented architecture uses AWS services including Cognito, CloudFront or an internal Application Load Balancer, API Gateway, Lambda, Aurora PostgreSQL, ECS Fargate, and OpenSearch. · data-dot-all.github.io, 8 Oct 2026
Catalog: Its centralized catalog inventories datasets, tables, folders, and dashboards, with search filters for asset type, tags, region, and glossary terms. · data-dot-all.github.io, 8 Oct 2026
Configuration: Modules and some features can be enabled or disabled through configuration, and the guide notes that file uploads are disabled in its example configuration. · data-dot-all.github.io, 8 Oct 2026
Data catalog: Its centralized catalog inventories datasets, tables, folders, and dashboards, with search filters for asset type, tags, region, and glossary terms. · data-dot-all.github.io, 8 Oct 2026
Datasets: Datasets can hold structured tables or unstructured folders and use AWS S3, KMS, IAM, and Glue resources. · data-dot-all.github.io, 8 Oct 2026
Data sharing: Dataset sharing across environments can grant selected team roles read access through Lake Formation without moving the data. · data-dot-all.github.io, 8 Oct 2026
Deployment: The AWS deployment guide requires a tooling account and at least one deployment account, and their selected regions must match. · data-dot-all.github.io, 8 Oct 2026
Deployment options: The deployment guide describes internet-facing and VPC-facing architectures; the VPC-facing option makes the UI and APIs reachable only within the VPC. · data-dot-all.github.io, 8 Oct 2026
Identity: Authentication can use Amazon Cognito federated with a corporate identity provider, or an external OIDC provider; data.all relies on identity-provider group information. · data-dot-all.github.io, 8 Oct 2026
Integrations: Documented modules include AWS Glue, Lake Formation, Amazon S3, Athena, SageMaker, QuickSight, and HealthOmics Ready2Run workflows. · data-dot-all.github.io, 8 Oct 2026
Local use: The project documents a Docker Compose local setup with frontend, database, GraphQL, CDK proxy, and OpenSearch containers; AWS credentials are needed to create environments and datasets. · data-dot-all.github.io, 8 Oct 2026
Network options: Deployments can be internet facing, with public APIs protected by AWS WAF, or VPC facing, with private sites and APIs accessible through VPC endpoints. · data-dot-all.github.io, 8 Oct 2026
Notebooks: The platform provides notebooks based on Amazon SageMaker Studio for data scientists to build machine learning models. · data-dot-all.github.io, 8 Oct 2026
Operational limit: The AWS deployment instructions require administrator credentials for the tooling and deployment accounts during setup. · data-dot-all.github.io, 8 Oct 2026
Purpose: data.all is an open source platform that can be deployed in AWS accounts to help users manage and discover data assets. · data-dot-all.github.io, 8 Oct 2026
Redshift integration: It supports importing tables from Redshift Serverless and provisioned clusters into the catalog; connections can use Secrets Manager secrets or Redshift users. · data-dot-all.github.io, 8 Oct 2026
Requirements: AWS deployment requires at least two AWS accounts and administrator credentials for the tooling and deployment accounts. · data-dot-all.github.io, 8 Oct 2026
Security: Dataset S3 buckets are encrypted with AWS managed KMS keys, and data access is logged through CloudTrail. · data-dot-all.github.io, 8 Oct 2026

Where data.all stands on Freedom251

Data Marketplace Software#5 of 27