Looker vs Domo: A Feature Comparison Matrix
Looker and Domo are two of the most popular BI tools on the market. In this comparison, we break down their features side by side to help you decide which one should you use.
LAST UPDATED September 17, 2025
AUTHOR 
Holistics Team
Holistics Team Over the years, we've received over hundreds of RFPs (Requests for Proposal) from a wide range of prospects and customers, from small businesses to international Fortune 500 companies. This has given us valuable insight into how data teams evaluate BI tools, the key questions they ask, and the capabilities they prioritize.
We’ve distilled those insights into a BI comparison matrix and used it to compare today’s leading BI platforms side by side.
Because every team’s priorities look a little different, we’ve also shared a Google Sheet version you can copy, adapt, and use directly with any vendors you’re assessing.
Our approach:
- Facts are prioritized over opinions, no recommendations pushed
- Details are backed by official documentation
- High-level criteria are broken down into specific, measurable sub-points
- Findings are presented in in clear, comparable tables
- Linking to real-world discussions from actual users
We understand we might come across as biased, since we're also a vendor selling BI solution. Rather than claiming neutrality, we'll let the content below speak for itself.
Found an inaccuracy or want your tool added? Use this form.
Feature-by-Feature Comparison Table
Scroll to the right to see more
| Dimension |  Looker |  Domo |
|---|---|---|
| Demo Playground Availability and quality of demo playground for testing the tool before purchase. | ||
| | ❌ No free trial. Sales-led demo model for enterprise clients. source | |
| Pricing Structure Pricing model and cost structure of the BI tool. | ||
| | Platform & User-Based Platform pricing for core instance plus user-based pricing for Developer, Standard, and Viewer roles. source | Consumption-Based Credit System Pay for what you use with credit system and base user fee starting at $750/year per user. source |
| | $35,000-150,000/year Base cost starts at $35,000-60,000/year. Average mid-sized company cost is $150,000/year. source | $50,000-200,000/year Small businesses $30,000/year, enterprise-level organizations can exceed $100,000 annually. source |
| Visualizations Chart and visualization capabilities of the tool. | ||
| |  Drag-and-drop canvas with diverse visualization options including tables, charts, and maps. source | 150+ Native Chart Types Over 150 native chart types including pie, line, bar charts, maps, scatter plots, and Gantt charts. source |
| |  Custom plug-ins for visualizations through Looker Marketplace with community and partner options. source | Extensive Customization Customize visuals and dashboards with no-code design approach for personalized layouts and themes. source |
| | Embedded Interface Branding Customize Looker interface to match branding for external analytics and custom applications. source | Personalized Branding Extensive custom styling and branding with user-friendly no-code design interface. source |
| | Narrative Dashboarding Craft compelling data stories with automated narratives and insightful text summaries. source | AI-Enhanced Data Stories Conversational AI (AI Chat) for natural language questions and automated alerts for key data changes. source |
| Ease of Use & Self-Service How user-friendly and self-service oriented the tool is for non-technical users. | ||
| | Drill-to-Row-Level Detail Expand filters and drill down to row-level detail for comprehensive data comprehension. source | Interactive Data Exploration Interactive dashboards with filters and customizable views for intuitive data analysis. source |
| | Marketplace Content Discovery Discover pre-built content, blocks, and custom plug-ins through Looker Marketplace. source | AI-Driven Data Exploration AI Chat for natural language questions and instant actionable insights through conversational AI. source |
| | Table Calculations & LookML Standard calculations via Table Calculations. Complex analyses require LookML modeling. source | ❌ No explicit built-in calculation features mentioned in documentation. source |
| | Drag-and-Drop Canvas Intuitive drag-and-drop canvas for creating visually appealing dashboards. source | ❌ No explicit report building features mentioned in documentation. source |
| | Gemini AI Assistant AI assistant for visualization creation, formula building, data modeling, and report generation. source | ❌ No explicit AI-assisted analytics features mentioned in documentation. source |
| Data Delivery How data and reports are delivered to end users. | ||
| | Data-Driven Alerts Create subscriptions and data-driven alerts based on insights for individual users and teams. source | Automated Alerts Automated alerts for key data changes to keep users updated on important information. source |
| | Content Sharing Guide Comprehensive documentation and guidance for effective content sharing within the platform. source | ❌ No explicit sharing or distribution features mentioned in documentation. source |
| Embedded Analytics Capabilities for embedding analytics into other applications. | ||
| | API-Driven Embedded Powerful embedded capabilities with robust API coverage for extensive data experiences. source | Embedded Analytics Embed analytics into any application, portal, or website to extend data reach and deliver insights. source |
| | Embedded Interface Branding Customize Looker interface to match branding for external analytics and custom applications. source | Custom Branding White-labeling and custom theming for embedded content to reflect brand's look and feel. source |
| | ❌ No embedded report builder for end users in embedded context. source | Self-Serve Analytics Simple drag-and-drop tools for teams to create visualizations within embedded content. source |
| Reliability & Performance System reliability, performance optimization, and monitoring capabilities. | ||
| | In-Database Architecture In-database architecture optimizing performance by directly querying cloud databases in real time. source | ❌ No specific information on query optimization, caching, pushdown, or pre-aggregation mentioned. source |
| | System Activity Explores System Activity Explores provide insights into user interactions, content engagement, and query performance. source | Automated Alerts Automated alerts for key data changes, but no explicit freshness indicators or error alerts mentioned. source |
| Semantic Modeling Data modeling and semantic layer capabilities. | ||
| |  Universal semantic modeling layer as single source of truth with LookML modeling language. source | ❌ No explicit semantic layer or consistent metrics enforcement mechanisms mentioned in documentation. source |
| |  Git-based version control for data models with proprietary dashboard versioning capabilities. source | ❌ No information about Git version control for managing semantic models or BI artifacts mentioned. source |
| | ❌ No explicit automated metadata syncing from dbt or other warehouses into semantic layer. source | ❌ No explicit automated metadata synchronization from dbt or data warehouses mentioned. source |
| | LookML Analytics-as-Code LookML modeling language enables code-based definition of dimensions, measures, and business logic. source | ❌ No information about defining dashboards or models in YAML/DSL formats or CI/CD workflows mentioned. source |
| Security and Governance Security features and governance capabilities. | ||
| | Unified User Management Unified user management with SSO via Google Cloud IAM and role-based access control. source | Enterprise-Level Security Enterprise-level security, compliance, and governance with SSO and encryption capabilities. source |
| | System Activity Explores System Activity Explores serve as audit logs for monitoring platform usage and system efficiency. source | ❌ No explicit audit compliance features mentioned in documentation. source |
| | ❌ No explicit data masking or encryption features mentioned in documentation. source | ❌ No explicit data masking or encryption features mentioned in documentation. source |
| | System Activity Explores System Activity Explores provide insights into user interactions, content engagement, and query performance. source | ❌ No explicit monitoring or logging capabilities mentioned in documentation. source |
Looker Pros and Cons
Looker Pros
- Powerful semantic modeling with LookML: Looker’s core strength is LookML, a code-based modeling layer that defines metrics, joins, and permissions centrally. This makes metrics reusable across dashboards and ensures “one version of the truth.” Teams can version-control LookML in GitHub, which appeals to analytics engineers used to software development workflows.
- Scalability and governance: Because the semantic layer enforces consistent definitions, Looker scales well in organizations with high data literacy. Dimensions and measures are defined once, then reused across hundreds of dashboards without duplication.
- Good developer experience: For technical BI teams, Looker’s integration with Git, support for persistent derived tables, and Liquid templating language enable sophisticated modeling. It’s closer to writing software than dragging charts, which some teams prefer.
- Data democratization: With a well-built model, business users can run ad hoc queries in the Explore interface without writing SQL. This reduces the bottleneck on BI teams for basic questions.
Looker Cons
Here are some of the limitations of Looker:
- Steep learning curve for LookML: LookML is powerful but hard to learn. Many teams underestimate the time needed to train analysts, and bugs in LookML can cascade into every dashboard.
- High cost: Looker pricing typically starts around $40k/year for a base package, with additional licenses costing $300–$1,500 per user depending on role. Smaller companies often find this prohibitive.
- Weak visualization layer: Out-of-the-box charts are basic compared to Tableau or even Power BI. More advanced visuals require JavaScript extensions, which adds complexity and maintenance overhead
- Poor fit for ad hoc or exploratory analysis: Looker excels when metrics are modeled ahead of time, but it’s cumbersome for quick one-off analysis. Teams often fall back on SQL or supplement with tools like Hex.
- Uncertain Google roadmap: Since Google acquired Looker, development has shifted toward positioning it as a semantic layer for Looker Studio. Many in the community feel core BI innovation has slowed, raising questions about long-term direction
When should a data team use Looker?
Looker is ideal for organizations that want strong governance and are comfortable with a code-first approach to BI. Its LookML modeling layer enforces consistent metrics across dashboards, which works well in data-mature companies with multiple teams querying the same datasets. It’s especially valuable in enterprises where version control, permissions, and scalability matter more than flashy visuals.
Looker is a great option when you need centralized definitions and auditability, but it demands technical investment: analysts must learn LookML, and setup time is longer compared to self-service tools. Smaller companies often find the price and technical barrier too steep.
Related Reading: The Best Looker Alternatives
Domo Pros and Cons
Domo Pros
- All-in-one cloud platform: Domo bundles ETL, dashboards, and even app-building tools in one SaaS package. For teams without a modern data stack, this “batteries included” approach can be appealing.
- Strong data connectors: Out of the box, Domo supports hundreds of connectors to SaaS apps like Salesforce, Google Analytics, and Shopify. For marketing and operations teams, this makes integration less of an IT-heavy project.
- Ease of use for business users: The UI is approachable, and business teams can create cards (Domo’s version of visualizations) without much training. Non-technical stakeholders often like how quickly they can get something on a dashboard.
Domo Cons
- High and opaque pricing: Domo is infamous for pricing that starts high and escalates quickly. Contracts often run into six figures annually, and the company is reluctant to share transparent pricing upfront.
- Limited flexibility in visuals: While good enough for simple dashboards, Domo’s “cards” are nowhere near as customizable as Tableau or even Power BI. For design-heavy use cases, teams often feel constrained.
- No semantic modeling layer: Metabase doesn’t provide a reusable semantic layer like Holistis’ semantic layer or Looker’s LookML. Each dashboard often defines metrics independently, which can lead to inconsistencies and duplicated logic across reports.
- Company direction concerns: Some users worry Domo tries to do too much, data apps, ETL, dashboards, alerts, without excelling at any one piece. That breadth can feel like bloat, especially for teams that already have modern data infrastructure in place.
When should a data team use Domo?
Domo is a fit when business teams want an all-in-one cloud BI platform with minimal setup. It bundles connectors, ETL, dashboards, and even lightweight app-building into a single SaaS package. For companies without a modern data stack—or executives who value mobile dashboards—Domo’s convenience can be appealing. It’s especially popular in marketing and operations teams that need quick integrations with SaaS apps like Salesforce or Shopify. That said, Domo is expensive, its visuals are basic compared to Tableau, and vendor lock-in is a real concern. Teams with established data infrastructure often find it redundant or overpriced.
The Best Alternative to Looker and Domo
Looker and Domo take very different approaches to BI. Looker is strong on semantic modeling and governance, but it’s expensive, code-heavy, and slow to roll out. Domo focuses on ease of use and mobile dashboards, but it’s costly, less flexible, and creates vendor lock-in.
If you’re looking for a third option, something that combines the best of Looker and Domo, while avoiding their trade-offs, Holistics Data is worth a close look. Holistics is a programmable BI platform designed to give data teams governance and scalability, without sacrificing self-service and ease of use.
- Code-based Semantic Modeling Layer: Define analytics logic in a shared, accessible semantic layer. Metrics can then be extended and reused across the organization to ensure consistency and accuracy everywhere.
- Analytics as Code: Analysts can treat analytics like software: Define dashboard, metrics, and BI components as code, govern with Git version control, perform branching, CI/CD, and deploy through dev-to-prod pipelines.
- Governed self-service: Analysts curate datasets, and business users explore them without writing SQL. Automated drills, simple 1-click calculations and a drag-n-drop interface make ad hoc analysis intuitive.
- Flexible customization: Build polished, on-brand dashboards by arranging visuals, filters, and narratives freely on a canvas.
- Reliable AI assistant: Unlike most AI copilots, Holistics AI is built on its semantic layer and analytics-as-code foundation. That makes it context-aware, governed, and far more reliable for day-to-day use.
- Embedded Self-service: With Holistics Embed Portal, developers can embed a mini-BI experience directly into their applications. Customers can explore data, customize reports, and self-serve insights within the app itself.
Community Discussions
Discover what other practitioners are discussing about this topic.
r/businessintelligence
Posted on June 2024 View source
Which BI tools impressed you the most (excluding usual suspects Tableau, Power BI, etc)?
Every BI tool have their pros and cons.
Wondering if anyone came across a BI tool that impressed them a lot in terms of features, ease of use, scalability, etc.
Been looking at tools such as evidence.dev, Rill, streamlit, some others and wondering others perspective on it, is it any good? Types of vizualisations available, end-to-end BI process improved or added headache?
Wondering if anyone came across a BI tool that impressed them a lot in terms of features, ease of use, scalability, etc.
Been looking at tools such as evidence.dev, Rill, streamlit, some others and wondering others perspective on it, is it any good? Types of vizualisations available, end-to-end BI process improved or added headache?
2top comments
Posted on April 2023 View source
Between the three, which BI tool do you prefer and why?
1top comment
dongdesk April 2023 Power BI. If you know what your doing with that tool and Azure you shouldn't need a dozen other tools. It is dominating the quadrants. It is cheap. Paginated is now included in Pro.
Dwh, measures in dax, blob storage compatibility, great visualization, can migrate cubes to it, etc.
Tableau is not getting investment, others are either immature, dying, recreating the wheel....
r/PowerBI
Posted on September 2023 View source
Why is PBI better than Tableau?
My organization is looking at Tableau and I am admittedly a bit biased against it. PBI has been introduced but most folks are using excel and its hobbled by the lack of data flows being enabled.
To me then reasons why PBI rocks are: DAX Third party tools (dax studio, tabular editor) Complex data modeling Deneb and other custom visuals Integration with the Microsoft stack / power platform/ excel The Italians/ Patrick
I have heard that tableau offers: Easier or quicker reads of data over power bi (especially over a million records) More natural integration with AWS and Sagemaker Easier to make visuals
Am I missing anything?
To me then reasons why PBI rocks are: DAX Third party tools (dax studio, tabular editor) Complex data modeling Deneb and other custom visuals Integration with the Microsoft stack / power platform/ excel The Italians/ Patrick
I have heard that tableau offers: Easier or quicker reads of data over power bi (especially over a million records) More natural integration with AWS and Sagemaker Easier to make visuals
Am I missing anything?
2top comments
Elevator_Parking September 2023 My Org. Is currently transitioning from Tableau to PBI. Having used Tableau for the past 3 years I will say it’s more snappy with throwing measures on a chart to do quick analysis. Also building dashboards appear to look nicer than PBI. But having to create a new sheet for every visual can become a pain with data heavy dashboards.
As I am learning PBI, I feel getting data to join is easier in PBI. Not having to do power query in excel then load into tableau to build a data source. Where it all lives within PBI. Although DAX is intimidating, I am starting to understand the logic behind it.
Mdayofearth September 2023 The biggest weakness in Tableau right now, as I see it, is that it is owned by Salesforce. I have no confidence that they can modernize Tableau.
The biggest weakness in PBI it's its visuals. The GUI is very clunky.
r/Looker
Posted on July 2023 View source
How does Looker compare to power BI and tableau?
For those with experience in the other two tools what would you say are the pros/cons of Looker?
2top comments
OvremployedSnowflake July 2023 Looker is a BI Platform whereas Tableau (Ive not used PBI) is a Visualization tool.
They serve different purposes imho. Tableau does not allow much flexibility when it comes to ad-hoc reporting. A Tableau best practice is to not build dashboards on datasets with more than a handful (20 or so?) attributes, whereas Looker has the concept of 'Explores' that can contain hundreds of attributes for the end user.
A company that only uses a visualization tool like Tableau really limits who has access to pull reports. People at my company complain so much about Looker and they want Tableau but what they don't realize is that if we had Tableau, they likely would not have a developer license and they would have to give their reporting/dashboarding requirements to a BI Developer to create their reports. For small companies that can really create a bottle neck and the average business end user likely doesn't know enough SQL to explore the data.
burningburnerbern July 2023 Semantic layer of using LookML rocks. Visualization piece not so much. Compared to tableau is lacks a lot.
r/BusinessIntelligence
Posted on December 2024 View source
Which BI Tool?
Hi, Our current team has a huge footprint (200+ dashboards)with QlikView. Also, the team has traditionally used it as a ETL tool as well. Now we have a mandate to decommission QlikView and start exploring other tools. We do not have an option to move to QlikSense. Internally the firm is pushing towards Power BI. Tableau is already in use, but it is not as robust as QlIkView (personal opinion).
Can someone suggest some other alternatives that can help us.
Can someone suggest some other alternatives that can help us.
2top comments
rotr0102 December 2024 Sounds a lot like us. Legacy environment:
- multiple BI tools / multiple data warehouses
- QlikView: 340 dashboards, 2k+ daily tasks, ETL in Qlik script
- Moving to: 5Tran, Snowflake, dbt, PowerBI
Observations:
- Qlik script skillset is highly transferable to snowflake SQL.
- Snowflake is a columnar database, like Qlik, and like Qlik you do not have to worry about traditional database concepts like indexing, record locking, performance, etc. Your Qlik developers might find it difficult to find noticeable differences between Qlik script/qvd and Snowflake SQL/snowflake tables beside the obvious syntax differences.
- MSnowflake has all functionality of Qlik script (we did HEAVY ETL transformations in Qlik), and it adds capability like better data insert/merge, and recursion, etc. Some geospatial functionality that looks very similar to Qlik geo analytics which we also use heavily. Obviously, snowflake adds all the new generation of cloud data warehouse capabilities like ingesting different cloud data formats.
Tips:
- Ensure your leadership understands that cloud means renting their infrastructure. Cost is the biggest pain point of my approach-but it was the choice of our leadership team. On premise to cloud is a big transformation, make sure you know what that means.
- Separate the data warehouse and BI layers, and build good models. Note: Qlik is less picky than other tools about the model because of its associative data models. PowerBI technically works with any model, but you’ll see its best with a star schema. Also, this separation between data and presentation will allow flexibility in future tool changes.
hasbrain March 2025 Late to the party, and someone has already mentioned, but I’d like to vouch for Holistics, Sigma and Omni.
They are all decent BI tools, but if you’ve got heavy ETL needs like you did with QlikView, you’ll probably need to add a dedicated ETL tool into the mix. Something like Alteryx or Azure Data Factory could fill that gap.
r/BusinessIntelligence
Posted on September 2024 View source
Tableau vs Looker
The company (600 people) i work for has to streamline their BI tooling portfolio which means in a few months we need get rid of either Tableau or Looker. Currently most of the reporting is done via Tableau, but we have a few Looker users as well. To make things easier - irony - we also want to let users do their own thing (self-service BI).
I would prefer to keep both tools alive, Tableau for fancy executive type and complex dashboards, and use Looker for self-service. However leadership needs a bigger yacht so we have to cut costs, one has to go. Can’t do Power BI, we are a G-suite company.
What do you think about my view/assumptions? How would you decide which tool to pick if any?
I would prefer to keep both tools alive, Tableau for fancy executive type and complex dashboards, and use Looker for self-service. However leadership needs a bigger yacht so we have to cut costs, one has to go. Can’t do Power BI, we are a G-suite company.
What do you think about my view/assumptions? How would you decide which tool to pick if any?
4top comments
edimaudo September 2024 First of self serve BI is a myth. Second I assume your IT infrastructure can handle either option.
If you want great visualization, drill downs then Tableau is the way to go. If you are in the google ecosystem and also need some ML stuff then looker. Can also look at the volume licensing structure. I would suggest going with one rather than both. Maintenance costs would eat you alive.
aclaypool78 September 2024 Relatively new to Looker development (6 Mos and LookML certified), and nothing seems to work as well in Looker as it did in my old job in Tableau - except the crazy ass sql that can be written by clicking in a well curated explore. Looker Data Studio Pro could satisfy your Tableau hungry people (not as good as Tableau, but it's got more viz capabilities than Looker). It seems those awesome days of analysts building amazing tools, but also holding the keys to the information are numbered if not already gone. Embrace the future as disappointing as it is.
aaahhhhhhfine September 2024 Looker's big advantage, as it's always been, is its semantic layer. It's easy there to map out hugely advanced and complex datasets in ways that then make visualization easier. It's really built as a nice semantic layer that happens to do visualization.
Tableau is kind of the opposite. Tableau sucks at actual data stuff... Managing the data, joining it, building reusable measures, etc... tableau sucks for that stuff. But Tableau makes pretty pictures.
So you might want to think about what matters to you and your users over time.
aaahhhhhhfine September 2024 Seems pretty straight forward - get rid of Looker and make the few users who are using it change to Tableau.
You may have a lower subscription cost for looker, but you need to hold that recurring savings up against the cost of change for your tableau setup: Report migration, change management / re-training of existing user-base, dual licensing during migration period, setup of integrations, etc.
How far out is your breakeven point then on a pure cost / savings basis?
After that you have to consider that in all likelihood, your report migration will not just be a migration but a report change, in cases where Looker can't produce the same visuals etc. as Looker. That's going to add an extra element of stuffing looker down people's throats to get a worse looking product (report) than they already had.
I hope that break-even point is in the near-future. Then you can of course argue that with Looker being part of the google product suite, you've got some architectural benefits and licensing benefits and maybe some machine learning awesomeness you can make use of, potentially... down the line... if it turns out to be useful...
So without knowing your specifics, you'll need to make a pretty strong "architectural " case in addition to your business case. In addition you can possibly argue for a "process optimization" / "time to delivery" case, by having a single platform within the company for people to build expertise in. Basically saying "we're placing all bets on G-suite including BI, that's the direction, your reports may end up looking a bit worse, but we'll save money and streamline our BI process in the mid-term, and long-term we think it'll be a better product".
r/PowerBI
Posted on September 2023 View source
Power BI VS Domo? Which is better?
Which is better, Power BI or Domo? I recently joined a team that is all about Domo and may have to leave Power BI behind. I am curious as to which BI tool is better, as my experience in Domo is just beginning. So far, I don’t love it and I’m hoping there’s some gems in there I just haven’t discovered yet.
2top comments
hopkinswyn September 2023 Haven't heard of Domo but if the Gartner Magic quadrant is considered a good measure then it's a way behind Micrososft.
https://www.domo.com/learn/report/domo-named-a-challenger-in-2023-gartner-magic-quadrant
I'm always suspicious of vendors that say "contact sales for pricing". Any idea what the cost is for licencing? Looks llike you need to write some SQL to connect to data source.
bAcidwits October 2023 Domo's Marketing strategy involves getting their salesmen in the room with C-Suite execs and keeping "technical people out of the room until we're talking about implementation".
I'm currently with a company that paid a small fortune for domo and it's almost universally hated by everyone. The analysts have to spend hours trying to get it to work, datasets collapse regularly under the weight of compute. It's absolutely not intuitive, the native MONITORING suite is an app you have to install from the marketplace.
At its best, it's a way to go get an excel export. We're currently trying to transition away from it and one of the lead contenders in the meanwhile is PowerBI.
r/PowerBI
Posted on February 2025 View source
Tableau vs. Power BI ⚔️ Clash of the Analytics Titans
The big debate in the business intelligence (BI) and analytics world right now is Tableau vs. Power BI, with AI BI being a wildcard influence both inside and outside of these leading platforms.
There are countless BI platforms out there, but Tableau and Power BI are the dominant players — as you can see from Google Search Trends chart pictured here, Power BI has seen steady growth, and search volume has surpassed Tableau — the reason for this change has multiple causes:
1. Tableau interest peaked with the SFDC acquisition and then stagnated — it’s likely leadership, marketing, and business changes led to this.
2. During the start of the COVID pandemic, we saw a steep decline in Tableau interest, whereas Power BI only experienced a temporary dip. Both orgs rebounded in Feb 2022. However, Power BI search volume has continuously surpassed Tableau since that time.
3. Post-pandemic, what was going on? Microsoft’s marketing and sales machine continues to hammer their “Fabric” data infrastructure — Azure, Power BI, etc. If a team was migrated to Azure for their data warehouse, Power BI becomes an upsell. During higher interest rate times, savvy teams are auditing their entire tech stack, and analytics tools like Tableau are no exception. Leading data and IT teams are exploring build vs buy for self-serve analytics and long-term growth. For many enterprises already on a Microsoft stack both for 365 and Fabric, it’s natural to evaluate Power BI —
4. The Salesforce roadmap with Tableau remains lose and uncertain — people are speculating Tableau will be rolled into SFDC and at some point, but there is no clear and public plan — enterprises investing millions in their data stack and 5–10 year tech stack road map do not like uncertainty.
There are countless BI platforms out there, but Tableau and Power BI are the dominant players — as you can see from Google Search Trends chart pictured here, Power BI has seen steady growth, and search volume has surpassed Tableau — the reason for this change has multiple causes:
1. Tableau interest peaked with the SFDC acquisition and then stagnated — it’s likely leadership, marketing, and business changes led to this.
2. During the start of the COVID pandemic, we saw a steep decline in Tableau interest, whereas Power BI only experienced a temporary dip. Both orgs rebounded in Feb 2022. However, Power BI search volume has continuously surpassed Tableau since that time.
3. Post-pandemic, what was going on? Microsoft’s marketing and sales machine continues to hammer their “Fabric” data infrastructure — Azure, Power BI, etc. If a team was migrated to Azure for their data warehouse, Power BI becomes an upsell. During higher interest rate times, savvy teams are auditing their entire tech stack, and analytics tools like Tableau are no exception. Leading data and IT teams are exploring build vs buy for self-serve analytics and long-term growth. For many enterprises already on a Microsoft stack both for 365 and Fabric, it’s natural to evaluate Power BI —
4. The Salesforce roadmap with Tableau remains lose and uncertain — people are speculating Tableau will be rolled into SFDC and at some point, but there is no clear and public plan — enterprises investing millions in their data stack and 5–10 year tech stack road map do not like uncertainty.
2top comments
PhiladeIphia-Eagles February 2025 This helps reinforce what we all know: Salesforce killed Tableau (so far).
Also, Tableau is materially more expensive. As they become closer to parity, more companies are going to choose PBI because BI is not revenue-generating (technically). Half the price is a no-brainer unless your usecase is ONLY supported by Tableau.
<br. Honestly, Microsoft should just spend what it takes to match Tableau for viz specifically. Then charge 15-30% more. They would bury Tableau forever because the lack of visual customization is really the only concrete thing Tableau is still significantly better at.
redman334 February 2025 Tableau calculated fields are way more intuitive and simple than DAX. They cannot accomplish everything DAX can, but almost everything. And for the things it can't, it's almost niche.
Still, both tools are quite good though.
r/PowerBI
Posted on September 2023 View source
Superset/Metabase vs Power BI/Tableau
I am currently evaluating which BI tool will be implemented across the company. In my earlier companies I have used Superset where I wrote SQL queries for each dashboard or insight requirement and created a visualisation directly from them.
I realised that in Power BI I can't write SQL queries directly, I can only connect directly to the database tables and do the transformation inside Power BI. A workaround would be to write sql queries separately and create a view in my db and then connect power BI to the view to create a dashboard.
I want to ask for your opinions on this. 1. Should I move to a tool where I can write queries and make visualisations. 2. Use views with power BI. 3. Simply abandon SQL queries and just do transformations entirely in power BI.
I realised that in Power BI I can't write SQL queries directly, I can only connect directly to the database tables and do the transformation inside Power BI. A workaround would be to write sql queries separately and create a view in my db and then connect power BI to the view to create a dashboard.
I want to ask for your opinions on this. 1. Should I move to a tool where I can write queries and make visualisations. 2. Use views with power BI. 3. Simply abandon SQL queries and just do transformations entirely in power BI.
2top comments
AmericanGrimm February 2025 You can write sql queries directly in power bi, in the option to import or direct query, extend the window and you can write there. Use it for import or direct query.
I advise building views anyway for reusability and maintainability outside of Power BI desktop app. Im sure Tableau has similar options.
redman334 February 2025 Take into consideration how your end user interacts with the final product and if the aesthetics of one tool or another increase adoption. Those pretty charts are worthless if no one in the company leverages their insights.
For some reason every time I started at a new place, I was thrown right into the BI evaluation process. Having gone so far down the rabbit hole, here are the tools that really impressed me.
- Holistics (we use this)
- Looker
- Sigma
- Hex
Hex is more of a notebook-style tool. It’s really fast, perfect for exploratory analysis.Sigma is spreadsheet-focused, so I imagine finance folks (or any Excel lovers) would find it particularly intuitive, plus it's got solid visualization features.
Looker is an old favourite around here, you'll see plenty of love (and sometimes hate) for it in this sub.
Holistics builds on the ideas Looker established, where data team defines the relationship between tables and how metrics are calculated on a semantic layer, and then everyone else can use the tool to build their own reports. What I really like is how they’re innovating where Looker is lacking, like with their visualizations. They've got this "dashboard as code" feature that opens up a lot of creative possibilities for dashboard designs.
Superset has been a fantastic BI tool in our company as an alternative to Tableau or Looker.
Since it is free and fully open sourced it has been great to have our R+D team extend and embed it within our own product, and also empower the BI team to create custom dashboards to replace default ones for enterprise clients.