Superset Minimum Requirements
Meeting the superset minimum requirements takes a Linux server, Python, and a metadata database. You also need a message queue and a cache to run background queries. Teams often set up these parts fast to get charts running right away. When teams rush this setup, they often use one shared database login for all users. A broad database role lets any chart builder read sensitive tables or run slow queries. Core components for superset minimum requirements The analytics app runs as a web server
Data Residency Requirements: the complete guide
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Meeting the superset minimum requirements takes a Linux server, Python, and a metadata database. You also need a message queue and a cache to run background queries. Teams often set up these parts fast to get charts running right away. When teams rush this setup, they often use one shared database login for all users. A broad database role lets any chart builder read sensitive tables or run slow queries.
Core components for superset minimum requirements
The analytics app runs as a web server that sends queries to your data sources. It does not store your business data inside its own local files. Instead, it needs a separate relational database to save its own app state. This metadata store holds dashboard layouts, chart settings, user accounts, and saved queries. While the app starts with a simple local file database, production teams need PostgreSQL or MySQL.
CPU and memory needs depend on how many active users view dashboards at once. The web app itself uses very little compute because the data warehouse does the heavy work. Still, you need extra memory if you turn on background workers for long queries. Background workers use a message broker and a cache layer to hold results. This setup stops web requests from timing out when a warehouse query takes several minutes.
You also need to install specific database drivers for every warehouse you plan to query. Each data source requires its own Python library so the web server can talk to it. Finally, the host machine needs network routes to reach those target databases on their ports.
How database roles and permissions work in practice
Once the server runs, an admin adds a data source using a database credential. In most setups, the analytics tool uses one service account for each connected warehouse. Every dashboard viewer and SQL editor user sends queries through that single shared database account.
The app includes its own role system to control what users see inside the web interface. Admins can group users into roles that limit which dashboards or datasets they can open. You can also set up row-level filters inside the app for specific dashboard views. Still, if you enable the built-in SQL editor, users can write custom queries directly. Those custom queries run with the full power of the underlying service account.
Data Residency Requirements: architecture patterns and best practices
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What native setup solves and where gaps remain
Sizing your server and setting app roles gives your team a working analytics platform. The metadata store keeps dashboards safe, and worker nodes keep the web app fast. Built-in app roles help organize which teams can view specific charts and saved datasets.
Still, the native setup leaves a major gap at the database query layer. The target database only sees the shared service account, not the individual human user. Audit logs in your warehouse cannot show which person ran a specific query. Also, app-level rules do not inspect raw SQL statements in flight before they run. A user with SQL editor access can still query raw emails, phone numbers, or card numbers. Native settings decide who logs into the app, not what individual queries return in flight.
Check database and API queries on the wire
Identity tools, roles, and network rules decide who opens a session. Setup alone cannot check what each query does or mask sensitive values in a response once a session starts. When workflows touch Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, or internal HTTP APIs, hoop.dev runs beside those services as an open-source proxy, a sidecar for your Postgres or API. It inspects requests coming in and results going back on the wire.
Your login details and client tools stay as they are while hoop.dev guardrails refuse bad deletes or drops before they run. On responses, hoop.dev data masking rewrites matched sensitive values in memory. Furthermore, hoop.dev holds SOC 2 Type II, is MIT licensed, and writes every query and decision to an audit trail that generates evidence for HIPAA, GDPR, and PCI DSS. See the source on GitHub.
Frequently asked questions
Can I use the default local database for a team deployment?
No, the default file database is only meant for local testing by one person. Production teams must use a dedicated database like PostgreSQL or MySQL for metadata storage.
Do more dashboards require a larger analytics server?
Your connected data warehouse handles the heavy query processing, not the analytics web server. You only need more server memory and CPU when many users load charts at once.
How can we stop SQL editor users from seeing sensitive fields?
Database roles alone struggle here because the app shares one login across many users. Placing a query control layer in front of the database rewrites matched fields like card numbers in results.