Best BI & Reporting Tools for Amazon Redshift
What is Amazon Redshift?
When it comes to cloud warehouse solutions, Amazon Redshift often tops our minds.
Founded in 2012, Amazon Redshift is an analytical MPP database that companies use to centralize their data. If you wonder about how amazing this product is, think of the fact that it has been leveraged by some of the most popular brands in the world, like Lyft, Duolingo, or Yelp.
Although its market share has been threatened by Snowflake in recent years, Redshift has a far greater advantage by being a part of the infamous AWS ecosystem.
Therefore, Redshift has attracted a fair amount of BI partners. In this blog post, I will introduce you to the best BI tools that can leverage Redshift's power. Please note that the tools listed below are in no particular order.
AWS Quicksight: The Default Choice for Redshift
If you're already deep in the AWS ecosystem, Amazon QuickSight feels like the obvious first choice for Redshift reporting. It’s cloud-native, serverless, and offers tight integration with Redshift.
That’s why QuickSight often wins the first round in BI evaluations for AWS-native stacks. You get dashboards, scheduled reports, and ML-powered insights, all managed within the same infrastructure your data warehouse lives in.
But the tradeoffs start to show fast.
Where QuickSight falls short
- Limited modeling: There’s no real semantic layer or reusable metric definitions. Business logic lives in dashboards or SPICE datasets, making governance tricky at scale.
- Rigid UX: Visualizations are functional, but not flexible. Custom layouts and complex interactivity (e.g., dynamic filters, parameterized queries) require awkward workarounds.
- Slow iteration: Collaboration is clunky, and versioning/report development lacks Git or code-first workflows. You’re stuck in the UI.
- SPICE dependency: To get decent performance, you often need to ingest your Redshift data into SPICE (QuickSight’s in-memory engine). But SPICE adds latency, refresh issues, and unexpected cost jumps as datasets grow.
Reddit threads on r/aws and r/dataengineering echo this: QuickSight is solid for basic dashboards, but anything more complex — like custom metrics, SQL chaining, or deeper modeling — hits a wall. One user put it bluntly: “QuickSight is great until your users start asking real questions.”
As your data team grows, so do your needs.
That’s when teams start to look elsewhere. Holistics, Omni, Looker, and even open-source tools like Superset offer more semantic control, better developer workflows, and more flexible sharing options.
QuickSight remains a cost-effective starting point, but it rarely scales as the long-term BI backbone of a modern data org.
Holistics
Holistics is a governed self-service BI tool that lets data analysts model and transform data in Redshift easily. At the same time, non-technical users can explore the data and find insights on their own using a drag-and-drop interface.
In Holistics, metrics and dimensions are defined in code (via AQL), version-controlled in Git, and exposed through a drag-and-drop interface that business users actually use. For teams already using Redshift and dbt, Holistics sits nicely on top of your models, allowing custom visualizations, alerting, and dashboard-as-code workflows.
Pros
- Allow you to query Presto using customizable SQL queries and get fast results with its cache layer
- Materialized views of query results are stored back to your own SQL database, for immediate access and fast visualizations and reports.
- Robust code-based semantic modeling layers to help data teams manage data logic centrally and define reusable metrics.
- Automated scheduling of reports and dashboard with the latest data in Presto, sent directly to your email inbox.
- Fast, flexible embedded analytics.
- Drag-and-drop interface for business users to explore data and generate reports to answer ad-hoc questions.
- Canvas-based Dashboards that allow data teams to have full customization over dashboard content, styling, and layout.
Cons
- There are a few minor UI difficulties, which can be off-putting for new users.
- Holistics' AQL (analytics query language) is powerful but might come with high learning curve.
PowerBI
Power BI continues to dominate enterprise deployments, especially those already using Microsoft 365, Azure, or Dynamics. For Redshift users, native connectivity exists, but it’s not as seamless as with Microsoft-native sources.
to: it’s cheap (Pro at $10/user/month), familiar for Excel users, and backed by a deep catalog of tutorials, community content, and third-party add-ons.
What breaks it: DAX is notoriously painful to learn. The Windows-only desktop modeling requirement is a blocker for modern, Git-integrated workflows. And collaboration is gated behind publishing permissions and licensing headaches.
Pricing
Power BI pricing is also attractive for small-scale companies with small data teams. If you're an individual and only need Power BI on your local computer to do analysis, then you can download the desktop version for free. However, if you want to use more Power BI services and publish your reports on the cloud, you can take the Power BI Cloud service solution for $9.99 per user per month.
Please note that if your company is concerned about security and on-premise deployment, the price goes up considerably toPower BI $4,995 per month, with an annual subscription.
Pros
- Great for dashboards tied to Microsoft data infrastructure
- Mature modeling and filtering logic via DAX
- Integrates with Excel, Teams, Power Automate
- Desktop app is fast (if you’re on Windows)
Cons
- Poor Git/version control support
- No Mac/Linux desktop support
- Difficult to embed or customize beyond Microsoft Cloud
- DAX's learning curve slows down teams without formal training
Looker
Another famous name in the BI industry, Looker is a powerful BI tool that provides an innovative approach for real-time data exploration and analytics.
Looker set the standard for modeling in BI with LookML, version control, and a SQL-first mindset. It still offers strong semantic governance, and integrates well with dbt and Redshift.
But in 2025, many teams find Looker too sluggish. Setup is slow. Permissions and dev workflows are rigid. And LookML has aged poorly compared to lightweight YAML/SQL hybrids.
Pricing
Looker does not publicly release its pricing information because it will be customized for each company. Looker pricing could range from $3000 - $10000 per month for 10 users with an annual subscription. As I mentioned above, Looker is designed for companies with mature and dedicated data teams that are willing to adopt a completely new modeling language and spend time setting up Looker to fit their whole data stack.
Pros
- Centralized semantic layer and metrics governance
- Git integration and version history
- Native Redshift support
- Powerful scheduled delivery + alerts
- Embedded analytics support
Cons
- High cost (licenses + setup)
- LookML is verbose and hard to onboard
- Requires dedicated team to operate
- Limited visual customization and charting
For more detailed BI tool list and analysis, check out:
Tableau
Throughout my career, whenever I'm talking to an analyst who's on the fence about which BI tool to choose, Tableau is always one of the two options.
This is simply because Tableau offers robust visual analytics and data drilling tools across all of its products. Its visualization capabilities are almost second to none in the market, and that's why it has attracted lots of companies from big to small.
Pricing
Tableau pricing is fairly complicated and is charged both based on your use-case and the number of team members. Moreover, Tableau also charges based on the roles of users, with a Creator costing $70/month, an Explorer $35, and a Viewer $12. Since this number is dependent on your business, you really should plan ahead and prepare for an upsurge of additional costs if you decide to purchase Tableau.
Pros
- Pixel-perfect dashboards, strong visual storytelling
- Custom interactivity and filtering
- Native Redshift support, plus live/extract switching
- Tableau Public ecosystem for portfolio/experiments
Cons
- No central modeling layer — logic gets duplicated
- Cost increases rapidly by seat and use case
- Server management is non-trivial (especially on-prem)
- Not designed for modern analytics engineering workflows
Sisense
If you knew about Periscope Data, you will know about Sisense. It's an attractive product with substantial data visualization capabilities and a friendly UI that bought Periscope back then at the end of 2019.
The portability, ability to build data cubes in the tool, and the low learning curve are Sisense's greatest advantages that make any other BI tool look behind their back.
Pricing
Sisense doesn’t publish public pricing, but estimates suggest:
- Cloud-hosted: Starts around $21,000/year for 5 users
- On-prem/self-hosted: Roughly $10,000/year base
- Full enterprise: Can exceed $60,000/year depending on seats, usage, and support tiers
Pros
- Powerful embedding capabilities with full frontend customization
- In-chip acceleration engine for fast queries across large datasets
- Custom scripting support in SQL, R, and Python
- Web-based development environment with developer-friendly APIs
- Suitable for both internal dashboards and white-labeled reporting
- Strong support for Redshift, Snowflake, BigQuery, and others
Cons
- Expensive at scale, with pricing that starts high and climbs quickly
- Steeper setup and modeling curve compared to modern lightweight tools
- UI and user experience can feel outdated compared to newer tools
- Not ideal for non-technical business users without training
Related reading: A Community Crowd-Sourced BI Tools Comparison Matrix (Worksheet Included)
Choosing BI Tools for Redshift: Key Considerations
Here’s what to consider when evaluating tools for Redshift:
- Do you need a semantic layer? If so, Looker, Holistics, and Omni lead. Tableau and Power BI trail here.
- What’s your team’s technical depth? Hex and Superset are power-user focused. Sigma and Tableau cater more to business users.
- How important is cost predictability? Tools like Holistics and Superset are more usage-based or free. Looker and Tableau pricing can balloon.
- Do you embed dashboards in your product? Superset, Holistics, and Omni offer flexibility here.
- Are you already on Microsoft/AWS/Google stack? If yes, Power BI, Quicksight, and Looker Studio may offer smoother integrations.