Migrate from Tableau to Apache Superset

Migrate from Tableau to Apache Superset: what the move involves

A renewal quote, a licensing change or a data residency requirement is usually what pushes a team off Tableau Server or Tableau Cloud, and the move is a rebuild of content on a SQL-first platform, not a file conversion. Workbooks do not translate into Superset dashboards; each one has to be re-expressed as datasets, SQL-defined metrics and charts. The effort depends on three things you can count before you start: the number of workbooks and dashboards in use, how many calculated fields and table calculations each one relies on, and how many data sources sit behind them. Start by pulling an inventory of those three numbers from your Tableau site rather than estimating from memory.

In most organizations the work falls to internal BI engineers who know the Tableau estate, supported by a DBA who owns the warehouse connections and a security engineer who owns authentication. The DBA decides which schemas Superset may read and under which service account. The security engineer maps Tableau's Creator, Explorer and Viewer roles onto Superset roles, and wires in LDAP or OAuth group mapping through AUTH_ROLES_MAPPING so access does not depend on local accounts. Confirm early which identity provider you will use, because that choice shapes the role model more than any chart does. DeployOpen can plan this inventory, implement the Superset deployment and its identity integration, and run the content rebuild under a written, fixed scope. Ask any implementation partner for their inventory method and the assumptions behind their effort estimate, and treat a quote that does not list the workbook and data source counts as incomplete.

Plan the cutover as a period in which both tools run against the same warehouse, with the Tableau site still live for users who have not moved. Use the Tableau REST API method for downloading workbooks, which returns .twb or .twbx files, to keep an exportable copy of every workbook while your licenses are active. Each dashboard should be signed off by its business owner against the Tableau original before it is retired. Before any upgrade of Superset itself, back up the metadata database and test in staging, following the upgrade guidance and checking UPDATING.md for breaking changes. Confirm with your own team how long parallel running must last, since that depends on your renewal date and the number of owners who need to sign off.

Which Tableau capabilities map to Superset and which need rework

This table maps each main Tableau capability to its closest Apache Superset equivalent and marks it as clean, needs rework or gap. Read the status column first, then check the notes on calculated fields and roles, which carry most of the migration effort. Superset has no visual calculated-field authoring equivalent to Tableau Desktop, so those calculations must be rewritten in SQL.

Tableau capability	Superset equivalent	Status	What to confirm or plan
Workbook export (.twb and .twbx)	No direct import; dashboards are rebuilt as Superset charts and dashboards	Needs rework	Export with the REST API Download Workbook method first, then use the XML to document sheets, filters and sources as a rebuild inventory.
Data sources and connections	Database connections and datasets	Clean	Each live connection maps to a Superset database connection if a Python DB-API driver and SQLAlchemy dialect exist for the source.
Extracts (.hyper data)	Physical tables or materialized views in the warehouse	Needs rework	Superset queries live sources, so extracted data must be loaded into a database it can reach before cutover.
Calculated fields	SQL expressions in virtual datasets or dataset metrics	Gap	Superset has no visual calculated-field authoring equivalent to Tableau Desktop, so each calculation must be written and tested in SQL.
Dashboards and sheets	Dashboards composed of charts built on datasets	Needs rework	Chart types and layout differ, so plan a visual check against Tableau output for each dashboard.
Filters and parameters	Native filters and dashboard filters	Needs rework	Confirm that each parameter type you rely on has a native filter equivalent before you retire the workbook.
Creator, Explorer and Viewer roles	Superset roles and permissions, with groups mapped through AUTH_ROLES_MAPPING	Needs rework	Map each Tableau role to a Superset role on paper first, since Superset roles do not mirror Tableau license types.
Scheduled subscriptions	Alerts and reports	Needs rework	Alerts and reports need the caching layer and Celery worker components, so confirm the infrastructure before promising scheduled delivery.
Embedded views	Embedded SDK with guest tokens	Needs rework	Embedding requires host application, identity and tenancy design; confirm row-level security needs with your application team.

What data and content can be exported from Tableau

Tableau content leaves the platform in layers: workbook definitions, embedded or linked data, and the identity records that control who sees what. Each layer needs its own export path and its own check before cutover. The cards below cover what the REST API returns, what to verify in each file, and what must be rebuilt rather than copied.

Workbook definitions through the REST API

The Tableau REST API includes a Download Workbook method, available from API version 2.0 and later, that returns each workbook as a .twb (XML) or .twbx file. A .twb holds the layout, sheets, dashboards and calculated field formulas but no data, while a .twbx packages the workbook with its local files and any embedded extract. Script the download across every site and project, log each object ID, and keep the output in version control so the Superset rebuild has a fixed reference. Confirm that your service account can see every project, because content the account cannot access will be skipped without a clear error.

Embedded extracts and data residency

A .twbx can contain a Hyper extract, a local copy of the data that Tableau queried. Before export, inventory which workbooks use extracts and which use live connections, because extracts may hold personal or regulated data that now sits in a file on a laptop or in a share. Extracts do not translate into Superset datasets, so the source database must be queried directly. Decide whether each extract is archived under your retention policy, deleted, or rebuilt from the system of record, and record that decision with the data owner.

Data source connections to Superset

Each Tableau data source points to a database through a connector and credentials. In Superset, the equivalent is a database connection defined by a SQLAlchemy URI and a Python DB-API driver installed in the Superset image. Before cutover, confirm that a driver exists for every source, that it is pinned to a version your security team has reviewed, and that the database user has read-only rights. Test each connection from the Superset host, not from a laptop, because firewall and network paths differ. Then create one dataset per table or governed view that a dashboard uses.

Users, groups and permissions rebuilt from identity

Tableau user, group and project permission records should not be copied into Superset. Export them only as a reference list, then recreate accounts through the identity source, using LDAP or OAuth2 and mapping groups to Superset roles with AUTH_ROLES_MAPPING. Map Tableau site roles to Superset roles deliberately, and confirm that each dashboard's viewers still match the old permission set. Keep the mapping table as evidence for the access review that follows cutover.

Dashboard content and calculated fields

Exported workbooks show calculated fields as Tableau expressions, and many of them depend on Tableau-specific functions or level-of-detail logic. These must be rewritten as SQL in the dataset or as Superset metrics and calculated columns, then checked against the figures the Tableau dashboard produced for the same filters and dates. Record each rewritten field, its source workbook and the test value used, so reviewers can see that the logic was preserved rather than assumed. Items that cannot be expressed cleanly in SQL should be flagged for a decision before cutover.

Migration sequence from inventory to decommission

This sequence moves a Tableau estate to self-hosted Apache Superset in controlled stages, so each group of dashboards is exported, rebuilt, tested and approved before users depend on it. Every step ends with a named artifact that gates the next one.

Inventory workbooks, data sources and users
Start by listing every workbook, published data source, extract, schedule, subscription and project on the Tableau site, and record who owns and who views each item. Pull the content list and permission data through the Tableau REST API and cross-check it against the admin console, since the API lists what exists but not always how it is used. Assign each item a business owner and a usage flag, such as active, seasonal or retired. The output is a content register that the pilot, dataset library and cutover plans all draw from, and it is done when every item has an owner and a disposition (migrate, rebuild, retire or keep on Tableau).
Export workbooks and document their sources
Download the in-scope workbooks with the Download Workbook method, which returns .twb (XML) or .twbx packages, and store them in version control with the content register. Record for each workbook the connection type, the underlying database tables, custom SQL, and the calculated fields and parameters it depends on. Extracts in .hyper form need a separate decision because they are not the same as live database connections. Confirm with the Tableau admin which content your permissions allow you to export, since download rights depend on the role. The output is an archive of workbook files plus a source map that shows which database objects each dashboard reads.
Build a tested dataset library in SQL
Recreate each underlying source as a Superset dataset that points to a governed database view or table, rather than copying Tableau's logic into each dashboard. Rewrite calculated fields as SQL expressions in views or as Superset metrics and calculated columns, and move shared logic into one place so several dashboards read the same definition. Check each rebuilt measure against a figure that Tableau already shows, using the same filters and date ranges, and record any difference and its cause. The output is a dataset library with a reconciliation log, and the step is complete when each dataset's key figures match the Tableau source within an agreed tolerance that the data owner has approved.
Run a pilot on a few high-value dashboards
Choose a small set of dashboards that matter to a real team, ideally one with a clear owner, moderate complexity and regular use, and rebuild them in a staging Superset instance. Ask the pilot users to test filters, drill paths, export formats and scheduled reports against their current Tableau views. Track the defects and the features that Superset does not match directly, such as Tableau's visual authoring patterns, and decide how each gap is handled. The output is a signed-off pilot with a defect list closed or accepted by the business owner, plus a documented rollback plan that names how users return to the Tableau content if the pilot fails.
Map roles and access to Superset
Translate the Tableau roles on the content register into Superset roles, separating who builds datasets and charts from who only views dashboards. Tableau's Creator, Explorer and Viewer roles are not identical to Superset's permission model, so map by what each user actually does rather than by the license name alone. For directory-backed logins, configure LDAP or OAuth group mapping through AUTH_ROLES_MAPPING in the Superset configuration so access follows the identity provider, and test that a user in each group sees only the expected dashboards and datasets. Also confirm that the SECRET_KEY and metadata database are provisioned before any shared access is granted. The output is a role matrix and a tested group mapping, reviewed by the security owner.
Run both tools in parallel
Publish the approved dashboards in production Superset while the Tableau originals remain live, and ask each team to use both for a defined period. Compare usage, refresh results and scheduled report output during that window, and log any mismatch in data, permissions or performance. Keep Tableau as the system of record until the business owners confirm that the Superset versions are accurate for their decisions. The output is a parallel-run log with sign-off from each owning team, and the step is complete when no open discrepancy affects a reported figure.
Cut over and retire Tableau content
Announce the cutover date to users, switch links, embedded views and scheduled distribution lists to Superset, and make the Tableau versions read-only so that no one publishes new changes to them. Keep the final workbook archive and the reconciliation records under retention rules agreed with the compliance owner. Confirm that every item in the content register has a recorded disposition, and that no active subscription or extract refresh still points to a retired source. The output is a cutover record and a read-only Tableau archive, and the step is complete when support requests for the migrated dashboards have settled to the normal level for the team.
Decommission Tableau and confirm the exit
Before the Tableau subscription ends, confirm that the archive is complete, the export files open and the retention copies are stored in the agreed location. Ask the Tableau administrator to remove the site content, users and data source credentials, and check that the license terms for any retained data are understood. Check the subscription end date against the archive plan, since term licenses stop working when the subscription lapses. The output is a decommission checklist signed by the data owner and the security owner, plus a short post-migration review that records what to change in the next Superset upgrade cycle, following the upgrade steps in the Superset documentation.

How the Superset platform is built and where its data lives

Apache Superset's architecture documentation describes four components: the Flask and React application that serves the UI and API, a metadata database, an optional caching layer, and optional Celery worker and beat processes. The application and the metadata database are the minimum. The metadata database holds Superset's own state, including dashboards, charts, datasets, users, roles and permissions. It does not hold your warehouse data, because Superset queries your source databases live through SQLAlchemy dialects. Before you size anything, map each Tableau workbook and extract to the source system it reads from, since that source stays where it is and Superset connects to it directly.

Alerts and reports, async queries and dashboard thumbnails need the caching layer and the worker components, according to the same documentation, and most installations use Redis and Celery for them. This matters for a Tableau migration because teams often expect scheduled subscriptions to behave like Tableau's scheduled refreshes. If your estate depends on scheduled emails, slow dashboards run asynchronously or thumbnail previews, plan for Redis, Celery workers and a beat process from the start, and confirm the exact configuration with the current Superset documentation for your target version.

Production installs need PostgreSQL or MySQL as the metadata database. The architecture documentation states that the SQLite default used by Quickstart and PyPI installs, and the PostgreSQL container in the Docker Compose setup, are not recommended for production. The configuration documentation lists supported versions as PostgreSQL 10 to 17 and MySQL 5.7 and 8.x, so confirm your chosen version against that list. Back up this database before every upgrade, because Superset's upgrade process runs database migrations and it is the single store of the dashboard definitions you are rebuilding.

What usually goes wrong in a Tableau to Superset move

Most problems in a Tableau to Superset move come from assumptions carried over from one tool to the other: that calculations, extracts, roles and secrets work the same way. The cards below cover the failure points we see most often, each paired with a check to run before cutover.

Calculated fields rely on Tableau logic

Tableau calculated fields, table calculations and LOD expressions often evaluate at the viz level, after the data is pulled. Superset datasets are SQL-first, so each calculation has to be rewritten as SQL in the dataset or as a virtual dataset. Inventory every calculated field and table calculation from the exported .twb files, mark those that depend on sheet ordering or partitioning, and test each rewritten metric against the Tableau value on the same filter set before any user sees it.

Extracts hold data the source does not

A Tableau extract (.hyper) can contain data that was never loaded into the source database, such as flat file uploads, blended sources or pre-aggregated results. Superset only queries live databases through SQLAlchemy dialects, so that data disappears at cutover. Before migrating, list every workbook that uses an extract and trace each extract back to its origin. Load any orphaned data into a governed database first, or the dashboards will show blank or stale figures.

Creator, Explorer and Viewer roles do not map cleanly

Tableau splits authoring rights across Creator, Explorer and Viewer role-based licenses, and only Creator includes Tableau Desktop. Superset uses its own roles and permissions, configured through Flask AppBuilder, and it can map LDAP or OAuth groups to roles with AUTH_ROLES_MAPPING. Write a role matrix first that lists who can edit datasets, build charts and only view dashboards today, then confirm each target role grants no more access than the user had in Tableau.

SECRET_KEY planned too late

Superset will not start without a user-specified SECRET_KEY, a requirement introduced in version 2.1.0. Teams that deploy from a sample configuration often discover this during the first staging run, or they generate a key in one environment and lose it when the container is rebuilt, which breaks stored credentials. Generate the key from your secrets manager, store it per environment, and document how PREVIOUS_SECRET_KEY will be used if you rotate it later.

Metadata database not backed up before upgrades

Superset stores dashboards, datasets, charts and permissions in its metadata database, which should be PostgreSQL or MySQL in production. The upgrade guidance calls for backing up that database, running superset db upgrade and superset init, and checking UPDATING.md for breaking changes. Take and test a restore of the metadata backup before every upgrade, and rehearse the upgrade in staging, because a failed migration without a tested backup can cost the whole dashboard catalog.

Users expect Tableau-style visual authoring

Tableau Creators drag fields onto shelves and build views interactively, while Superset charts are configured from datasets that are defined in SQL and governed by the data team. Analysts who expect the same freedom may report the migration as a regression even when the numbers match. Run a training session with real Tableau authors on their top three dashboards, and decide in advance which authors will own dataset creation and which will only build charts on approved datasets.

How the move differs by estate size and starting point

A small estate is mostly a set of published dashboards that a large audience opens, filters and exports, with few people building new content. The migration is usually dominated by rebuilding a modest number of views and by confirming who needs what access, not by engineering. Because Viewer-level users consume content rather than author it, the Superset side can start with a few curated dashboards owned by a small group of builders, which keeps the initial scope narrow.

Fewer workbooks to rebuild: Count published views and mark which ones are still used, since unused content often never needs a Superset equivalent.
Access is the main design task: Map Viewer users to Superset roles and decide whether dashboards are shared by link, by group or through the embedded SDK.
Export is quick: Bulk download through the REST API covers a short list, so the export step rarely drives the timeline.
Dashboards become charts on datasets: Each view is recreated as charts on a dataset, and filters are rebuilt as dashboard native filters.
Few people own the result: Name at least two Superset builders so knowledge does not sit with one person.

Before starting, ask the Tableau administrator for a usage report of views over the last several months so the migration list reflects real use.

What drives the cost and duration of the migration

This table lists the factors that most often move the effort and cost of moving a Tableau estate to self-hosted Apache Superset, why each one matters, and how to keep it manageable. Read it as a checklist to scope with your implementation partner before asking for a quote, since no fixed figures are given here.

Driver	Why it changes cost or effort	How to keep it down
Workbook count and dashboard complexity	Each workbook must be inventoried, mapped to a Superset dashboard and checked visually, so effort grows with the number of views and sheets per dashboard.	Retire unused workbooks first, using view usage data, and consolidate near-duplicate dashboards before export.
Number of data sources and extracts	Each live connection or Hyper extract needs a matching Superset database connection and dataset, plus a decision on whether data is queried live or refreshed on a schedule.	Group sources by warehouse, reuse one connection per database, and confirm early which extracts can be replaced by direct queries.
Calculated field and LOD complexity	Tableau calculations, table calculations and level-of-detail expressions must be rewritten as SQL in datasets or metrics, and each rewrite needs validation against Tableau output.	Move shared logic into governed SQL views in the warehouse, and prioritize the calculations that appear in many workbooks.
Role model and permission mapping	Creator, Explorer, Viewer and Display roles map to Superset roles and permissions that must be designed and tested, and gaps may need custom role work.	Document current license assignments and user groups, then agree a small set of target Superset roles before building anything.
SSO and LDAP integration	Connecting Superset to the identity provider through OAuth2 or LDAP, and mapping groups to roles with AUTH_ROLES_MAPPING, requires coordination with identity teams and testing per provider.	Reuse the existing identity provider and group structure, and test one login and role mapping path end to end before rollout.
Kubernetes or virtual machine hosting choice	A Kubernetes deployment brings cluster, ingress and operator work, while a virtual machine deployment shifts effort to patching, backups and service management. Helm chart status should be checked for new installs.	Choose the hosting model that matches existing platform skills, and confirm the current recommended install method in the Superset documentation.
Metadata database, cache and worker components	Production use needs PostgreSQL or MySQL for metadata, plus a caching layer and Celery workers for alerts, reports, async queries and thumbnails. Each component adds build, monitoring and backup work.	Confirm which features are in scope, since alerts and reports drive the extra components, and size them from actual usage.
Security review and compliance evidence	Access control, secrets handling, network placement and audit logging need review and documented evidence, and findings can add rework before go-live.	Agree the control set and evidence list at scope stage, and run the review on staging rather than at cutover.
Parallel run length	Running Tableau and Superset side by side means paying for both during the overlap and reconciling results, so a longer overlap raises ongoing license and staff time.	Set exit criteria per dashboard, and align the overlap window with the Tableau renewal date so licenses are not extended unnecessarily.

Keeping Superset supported after cutover

Once users work in Superset, the platform needs a repeatable upgrade routine, and the first step is always the metadata database. Back up the metadata database, then run superset db upgrade and superset init against a copy of production in staging before touching production. Superset's upgrade documentation calls for this sequence, and the release notes in UPDATING.md list breaking changes between versions. Read every entry between your current version and the target before scheduling a window, paying particular attention to changes to database drivers, authentication settings and the SECRET_KEY handling, because those break dashboards and logins in ways that a successful startup will not reveal. Version 6.1.0 is the release referenced in the sources for this page, and the GitHub releases page already lists a 7.0.0 pre-release, so confirm which version you are targeting and whether it is the current stable line when you plan.

Your deployment method also shapes the upgrade work. The Superset 6.1.0 documentation describes Kubernetes installs with the official Helm chart, but the current development documentation marks that chart as deprecated and points new deployments to the Apache Superset Kubernetes Operator. If you cut over to Superset on Kubernetes in the coming months, choose the operator for a new deployment and confirm the migration path from the chart with your platform team before go-live, rather than building your upgrade runbook around a chart that the project is moving away from. Record the chosen method, the image tag and the database version in your change log so every later upgrade starts from a known state.

Superset is licensed under the Apache License 2.0 and has no paid edition, so there is no license renewal that can stop the platform from running. Your recurring costs are the infrastructure, the database and cache services, the people who run upgrades and the support you choose to buy. When you request a quote from an implementation partner, ask for the upgrade routine, staging environment, backup and restore testing and the hours for each upgrade cycle as separate line items, and ask who owns the SECRET_KEY and the OAuth or LDAP configuration after handover. Confirm the license terms against the LICENSE.txt in the repository with your legal team, since that file is the authoritative text.

Questions buyers ask before moving from Tableau to Superset

How long does a move from Tableau to Superset take?

No supplied source gives a duration for this move, so the honest answer depends on your estate. The main drivers are the number of workbooks and data sources in use, how many calculated fields rely on Tableau-specific logic, how many dashboards have active users, and whether row-level security and SSO must match the current setup. Count your workbooks through the REST API export, group them by data source and by owner, and measure how many of them are actually used. Ask any partner for a timeline built from that inventory, in writing, before work starts.

Can our team migrate Tableau to Superset without outside help?

Yes, a capable internal team can do this, provided it has people who can deploy and operate Superset and who can write SQL against your warehouse. The Superset upgrade documentation expects a metadata database backup, database upgrade and initialization steps, staging tests and a check of breaking changes before each version change, so someone must own those routines after go-live. Teams without that experience often bring in outside help for the platform build and the dashboard rebuild, then keep the day-to-day running in-house. DeployOpen offers fixed-scope implementation and retainers for ongoing support, with scope agreed in writing first.

What skills and staff time does a Superset migration require?

Expect three groups of work: platform operations, data modelling and dashboard rebuilding. Platform operations covers the web application, the metadata database (PostgreSQL or MySQL), and, if you need alerts, reports, async queries or thumbnails, a caching layer and Celery workers. Data modelling means turning Tableau data sources into Superset datasets and rewriting calculated fields in SQL, which needs analysts who know both the business logic and the warehouse. Dashboard rebuilding needs authors who can recreate charts and filters. Confirm with your own team how many hours each group can release and who will own each part after cutover.

Do Tableau workbooks convert to Superset automatically?

No. The Tableau REST API can download each workbook as a .twb or .twbx file, which gives you the complete inventory, but it does not produce Superset dashboards. You will read the workbook XML to identify data sources, calculations, filters and layout, then recreate the data sources as Superset datasets and rewrite calculated fields as SQL in the warehouse or as virtual datasets. Tableau visual authoring has no one-to-one equivalent in Superset, so some charts will need a different chart type. Plan to rebuild rather than convert, and keep a mapping sheet that links each old workbook to its new dashboard.

Does Apache Superset have a paid edition?

No. Apache Superset is an Apache Software Foundation project released under the Apache License 2.0, and the project has no paid edition. Your software cost is therefore zero, and the recurring costs sit in infrastructure, the metadata database, caching and workers, staff time and any implementation or support contracts you choose. Check the license text in the project repository before you distribute modified builds, and confirm with your legal team how the license applies to your use, including any embedding plans.

What should we ask a consultant for in a migration quote?

Ask for a written scope that lists the workbooks and data sources in or out of scope, the target architecture (single host, Docker-based install or Kubernetes), the authentication design, the role mapping from Creator, Explorer and Viewer to Superset roles, the parallel run period and the cutover and rollback steps. Ask who maintains the Superset upgrade path after handover and what documentation you receive. Ask how the quote handles workbooks that turn out to be more complex than the inventory suggested. DeployOpen plans and delivers fixed-scope engagements, so the quote should follow from that written scope.

What should we confirm about Superset's production setup before we start?

Confirm four things before committing. First, the metadata database must be PostgreSQL or MySQL in a supported version: PostgreSQL 10 to 17 or MySQL 5.7 and 8.x, according to the configuration documentation. Second, Superset requires a user-specified SECRET_KEY to start, and you should plan how that key is stored and rotated. Third, Kubernetes deployments should follow the current guidance, since the Helm chart is marked deprecated for new deployments. Fourth, confirm the release line you will run, because the current releases and the pre-release notes change over time, so check the project's release page for the version your team will pin.

How DeployOpen scopes and runs a Tableau to Superset migration

DeployOpen is a Chainscore Labs company that plans, implements, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside the client's own infrastructure, including their cloud accounts, data centers or air-gapped networks. For a Tableau to Apache Superset move, that means the Superset deployment, its metadata database, the caching and worker components, authentication, the export and rebuild work, and the cutover runbook are all delivered into the client's environment rather than hosted by DeployOpen. The first scoping conversation should establish which Tableau edition is in use (Server or Cloud), how many workbooks, data sources and extracts exist, which identity provider must be integrated, and which databases Superset must reach.

Most engagements are fixed-scope, so scope, deliverables and timelines are agreed in writing before work starts. A useful written scope names the workbooks in or out of the migration, the data sources to recreate as Superset datasets, the calculated fields to rebuild in SQL, the role mapping from Creator, Explorer and Viewer, the parallel-running period, and the acceptance tests each dashboard must pass. DeployOpen does not publish a price list, so a quote follows from that written scope. Ask any provider for the items that drive effort: the count of workbooks that depend on extracts or custom SQL, the number of distinct data sources, and whether the Superset upgrade and backup routine must be handed over with the platform.

Ongoing support, upgrades and continuous testing are available as a retainer after cutover, which matters because Superset releases need staging tests and a check of UPDATING.md before each upgrade. Confirm with any provider whether retainer terms cover Superset version upgrades, backup verification and security patching, and who owns the evidence those controls produce. DeployOpen prepares systems, controls and documentation for compliance assessments, but it does not issue audit reports, attestations or certifications; your independent auditor or certification body makes that determination.

Migrate from Tableau to Apache Superset