Apache Superset vs Redash: Open-Source SQL Dashboards Compared (2026)

I ran Redash for two years at a startup where the entire analytics team was five engineers who thought in SQL. It was perfect -- write a query, pick a chart type, pin it to a dashboard, move on. Then Databricks acquired Redash, the hosted version disappeared, and the open-source releases stopped coming. When we needed to support a growing team with proper access control and more chart types, we moved to Superset. The migration took a full sprint and we never looked back.

That experience shaped a strong opinion: these tools serve the same audience (SQL-first teams) but sit at very different points on the complexity-vs-capability spectrum. And in 2026, the development trajectory gap between them makes the comparison as much about risk as about features.

What each tool actually is

Apache Superset started inside Airbnb's data team around 2015 and graduated to a top-level Apache project. It is a full-featured BI platform: SQL Lab (a browser-based SQL IDE), 40+ visualization types, a semantic layer with reusable metrics and calculated columns, role-based access control, and a plugin architecture for custom charts. It runs on Python/Flask with Celery for async queries, Redis for caching, and PostgreSQL or MySQL for metadata. The managed version is Preset, built by Superset's creator.

Redash launched in 2013 as a straightforward tool for one job: write SQL, visualize results, share dashboards. Also Python/Flask. Founded by Arik Fraimovich, acquired by Databricks in 2020. The hosted Redash.io service was shut down in 2023. The open-source repo still exists at getredash/redash, maintained by the community, but with no roadmap and no major releases since v10 (late 2021).

Both tools assume their users write SQL. Neither has a meaningful visual query builder. That is why this comparison exists -- they compete in the same niche, unlike Metabase which targets non-SQL users first.

The 2026 elephant: Redash's stalled development

I need to be direct about this because it overshadows everything else in this comparison.

Redash worked. Past tense is unfortunately appropriate when talking about its development velocity. The tool still runs -- plenty of teams use it daily in 2026 and it does not spontaneously break. But the open-source project has been on life support since the Databricks acquisition:

• No major release since v10 (late 2021)
• Community PRs accumulate without merging
• Driver updates lag behind database releases
• Docker images get published intermittently
• No public roadmap, no core team shipping features
• Security patches arrive, but slowly and without a predictable cadence

The community has kept it alive through forks and patches. Some organizations maintain internal builds with cherry-picked fixes. But "community-maintained project with no funding and no full-time contributors" is a fundamentally different proposition from "top-level Apache project with corporate backing and monthly releases."

Superset, by contrast, ships regularly. The contributor base is large. Preset (the managed service) funds core development. New chart types, performance improvements, and security fixes land on a predictable schedule. This matters -- not because Redash will stop working tomorrow, but because every month that passes widens the gap in features, security posture, and database driver support.

SQL editing: Redash's simplicity vs Superset's power

Both tools have SQL editors. The experience is meaningfully different.

Redash's query editor is clean and focused. You write SQL, you get results. Its strengths:

• Parameterized queries using {{parameter}} syntax -- renders as widgets (dropdowns, date pickers, multi-selects) on the dashboard. Elegant, fast, intuitive.
• Query snippets for reusable SQL fragments
• Query results as data sources -- write a query that reads from another query's output
• Fork-and-edit workflow for collaboration
• Schema browser with basic autocomplete

The editor does one thing well: it gets out of your way and lets you write SQL. For a team of engineers who already have strong opinions about how to write queries, this minimalism is a feature.

Superset's SQL Lab is closer to a full IDE:

• Better autocomplete that understands table relationships and column types
• Query history with search and re-run
• Multiple tabs with simultaneous queries
• Explore results directly -- click "Create Chart" from any result set
• Cost estimation for queries (on supported databases)
• Saved queries with tagging and organization
• Result set pagination and export

SQL Lab is a productivity tool in its own right. I have watched analysts who previously ran queries in DataGrip switch to SQL Lab entirely because the chart-creation workflow is seamless -- run the query, examine results, click a button, build a chart. No copy-paste, no switching tools.

The tradeoff: SQL Lab has more UI surface area to learn. Redash's editor is immediately obvious to anyone who has used a SQL client. SQL Lab takes 30 minutes of exploration before you know where everything lives.

Visualizations and dashboards

This is where the capability gap is widest.

Superset ships 40+ chart types: standard bar/line/scatter, but also deck.gl geospatial maps, heatmaps, chord diagrams, treemaps, sunbursts, sankey diagrams, box plots, bullet charts, and several histogram variants. The formatting controls are granular -- axis labels, color schemes, conditional formatting, annotations, time comparisons. You can build custom visualization plugins if the built-in ones don't cover your use case.

Redash ships roughly 15 chart types: line, bar, area, pie, scatter, cohort, pivot table, counter, map, word cloud, funnel, and a few others. The formatting options are minimal. Charts look functional -- they communicate data effectively -- but the customization ceiling is low. If you want to change a specific color, adjust axis tick formatting, or add a reference line, you will frequently hit walls.

On dashboards specifically: Superset supports dashboard-level filters that cascade across multiple charts, cross-filtering (click a bar to filter the whole dashboard), tab layouts within dashboards, and CSS overrides for styling. Redash dashboards are flat grids of widgets with basic global filters. Functional, but limited when you need to build anything sophisticated.

The semantic layer difference

Superset has one. Redash does not.

In Superset, you define "Datasets" -- registered tables or queries with metadata attached: metric definitions (SUM(revenue), COUNT(DISTINCT user_id)), calculated columns, column descriptions, and caching rules. Once defined, anyone building a chart from that dataset gets the pre-built metrics. You define "Monthly Revenue" once; every dashboard that uses it pulls the same calculation.

Redash has no equivalent concept. Every query defines its own logic. If three dashboards need "Monthly Revenue," three queries contain that calculation independently. For small teams this is fine -- five analysts can keep their queries consistent through convention. For larger teams, it becomes a source of discrepancy and confusion.

Deployment and operations

Both tools run in Docker. Neither is a single-container affair, but the complexity differs.

Redash deployment:

docker compose up -d

Containers: web server, worker(s), scheduler, PostgreSQL, Redis. Typically 4-5 containers. Straightforward. You can have a working Redash instance in 15 minutes from a cold start. The Docker Compose file is well-structured and the documentation (while aging) covers the basics.

Superset deployment:

docker compose -f docker-compose-non-dev.yml up -d

Containers: web server, Celery worker(s), Celery beat (scheduler), Redis, PostgreSQL, init container. Typically 5-7 containers. Configuration happens through superset_config.py -- a Python file with dozens of options for caching, security, feature flags, and database connections.

The practical difference: Redash's configuration surface is small. You set environment variables and it works. Superset's configuration surface is large -- Python file, feature flags, security settings, Celery configuration, caching layers. For a team with ops experience this is manageable. For a team that wants to docker compose up and forget about it, Superset requires more initial investment.

At scale, the story reverses. Superset's architecture -- with separate workers, configurable caching, and async query execution -- handles hundreds of concurrent users. Redash's simpler architecture works well for small teams but starts straining under heavy concurrent load.

Access control and security

Superset has mature RBAC: roles, permissions on datasets and dashboards, row-level security (restrict query results based on user attributes), and integration with OAuth, LDAP, and SAML. You can build complex permission models -- this team sees only their division's data, that role can view but not edit, this admin manages a specific set of dashboards.

Redash has basic groups and permissions: users belong to groups, groups have access to data sources. You can restrict who queries which databases. Dashboard-level permissions exist but are coarse. There is no row-level security, no fine-grained dataset permissions, and SSO integration depends on community-maintained patches.

For a team of 5-10 engineers who all have full database access anyway, Redash's permission model is sufficient. For an organization with 50+ users, multiple teams, and compliance requirements, Superset's access control is necessary.

Embedding

Both tools support embedding dashboards in external applications, but with different maturity levels.

Superset supports iframe embedding with authentication tokens, and the community has built embedded SDK solutions. The setup works but requires configuration and an understanding of the security model.

Redash supports embedding via public URLs and iframe embedding for specific visualizations. It is simpler to set up for basic cases but lacks the auth integration and flexibility that production embedded analytics typically requires.

Neither tool matches Metabase Pro's embedded analytics polish. If embedding is your primary use case, look at our Metabase vs Superset comparison -- Metabase wins on that axis.

The comparison table

When to pick Superset

• Your team has more than 10 people who will use dashboards
• You need proper access control with role-based permissions
• You want 40+ chart types and the ability to add custom ones
• Active development and a predictable release cadence matters
• You have ops capacity to manage the deployment
• You want the option of a managed service (Preset)
• You are building something that needs to scale over the next 2-3 years

When Redash still works

• Tiny team (under 10) of SQL-fluent engineers
• You already run Redash and it is not causing problems
• Your requirements are genuinely simple: query, chart, share
• You value minimalism and fast setup over feature richness
• You do not need fancy charts, a semantic layer, or fine-grained permissions

The migration reality

Many teams that adopted Redash in 2017-2019 are now migrating away. The most common destinations are Superset and Metabase. This migration is manual -- no automated tooling exists. You reconnect data sources, recreate queries (SQL mostly transfers directly, but parameterization syntax differs), rebuild dashboards from scratch, and re-establish permissions.

A team with 30 queries and 5 dashboards can migrate in a few days. A team with 200+ queries will spend a week or more. Every team I have seen make this move pruned 30-50% of their queries in the process -- most organizations accumulate abandoned queries that nobody runs.

This is a one-way door. Once you invest the time migrating to Superset, you gain features (semantic layer, better charts, RBAC) that you will immediately start using, and those do not port back.

Where both tools share limitations

Both Superset and Redash are single-database query tools. They connect to a database, you write SQL against it, you see results. If your data already lives in a warehouse, this model works perfectly.

But if you need to join data across multiple sources -- Stripe transactions with CRM contacts, or warehouse tables with a CSV your finance team emailed you -- neither tool can help without an ETL layer in between. For that kind of cross-source, ad-hoc analysis, tools like Fastero that combine database connections with file uploads and cross-source queries fill a different gap.

My actual recommendation

If you are choosing between these two tools for a new deployment in 2026: pick Superset. The additional setup complexity is a one-time cost. The ongoing benefits -- active development, better charts, proper access control, a semantic layer -- compound over time. And you are betting on a project with a clear future rather than one living on borrowed time.

If you already run Redash and it works: keep running it until something forces a change. That forcing function will eventually arrive -- a database driver that stops working, a security vulnerability without a patch, or a team that outgrows the access control model. When it does, Superset is the natural destination for SQL-first teams.

If you are also considering Metabase, read our Metabase vs Superset and Metabase vs Redash comparisons -- Metabase serves a different audience (non-SQL users) but overlaps on the dashboard-building use case. For a broader survey, see our best BI tools roundup, or our comparison of Grafana vs Superset if monitoring dashboards are part of your requirements.

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Last updated: July 2026. Superset ships frequently -- check the Apache Superset releases for the latest. Redash's community fork is at github.com/getredash/redash.