What is the main difference between Sigma and Looker?

Looker centralizes logic in LookML, a modeling language that engineers maintain, which gives strong consistency but puts most changes in the data team's hands. Sigma keeps a governed semantic layer while letting business users build and adjust analysis themselves with spreadsheet formulas, so fewer requests queue behind the modeling team.

Can Sigma build apps and agents, or is it just BI?

Sigma covers governed BI, and it also builds AI apps with writeback and runs agentic workflows on the same warehouse under one control plane. Looker centers on the governed semantic layer and embedded analytics, so if you want to act on data and not only model and view it, Sigma's build-and-act layer is the larger difference.

Is Sigma a good Looker alternative?

For teams who value Looker's governed metrics but feel the maintenance and the wait for changes, Sigma is a common alternative. It keeps shared definitions while giving business users room to do ad-hoc work without opening a modeling ticket.

Do I need to learn LookML to use Sigma?

No. Sigma has no equivalent modeling language to learn. Governed metrics and relationships are defined in data models through a visual interface, and everyday analysis happens in spreadsheet formulas, SQL, or Python, so the data team is not the bottleneck for routine changes.

Does Sigma give the same governance as Looker?

Sigma offers shared metrics, version control, and permissions for a single source of truth, while allowing exploration on top of that foundation. Looker's governance is rigorous but rigid, which is exactly the trade some teams want to loosen so users can answer their own questions.

How does Sigma handle ad-hoc analysis versus Looker?

Looker tends to route ad-hoc requests through someone who knows LookML, which adds a wait. Because Sigma lets users drill, pivot, and add calculations directly on governed data, many of those questions get answered in the moment.

Can we migrate from Looker to Sigma?

Yes, and it often happens during a warehouse move. Druva, for example, left Looker for Sigma, tripled adoption, and now has more than 400 weekly active users. Migrations connect Sigma to the same warehouse, translate the core LookML metrics into Sigma's data models, and rebuild the most-used dashboards first.

What is the main difference between Sigma and Looker?

Looker centralizes logic in LookML, a modeling language that engineers maintain, which gives strong consistency but puts most changes in the data team's hands. Sigma keeps a governed semantic layer while letting business users build and adjust analysis themselves with spreadsheet formulas, so fewer requests queue behind the modeling team.

Can Sigma build apps and agents, or is it just BI?

Sigma covers governed BI, and it also builds AI apps with writeback and runs agentic workflows on the same warehouse under one control plane. Looker centers on the governed semantic layer and embedded analytics, so if you want to act on data and not only model and view it, Sigma's build-and-act layer is the larger difference.

Is Sigma a good Looker alternative?

For teams who value Looker's governed metrics but feel the maintenance and the wait for changes, Sigma is a common alternative. It keeps shared definitions while giving business users room to do ad-hoc work without opening a modeling ticket.

Do I need to learn LookML to use Sigma?

No. Sigma has no equivalent modeling language to learn. Governed metrics and relationships are defined in data models through a visual interface, and everyday analysis happens in spreadsheet formulas, SQL, or Python, so the data team is not the bottleneck for routine changes.

Does Sigma give the same governance as Looker?

Sigma offers shared metrics, version control, and permissions for a single source of truth, while allowing exploration on top of that foundation. Looker's governance is rigorous but rigid, which is exactly the trade some teams want to loosen so users can answer their own questions.

How does Sigma handle ad-hoc analysis versus Looker?

Looker tends to route ad-hoc requests through someone who knows LookML, which adds a wait. Because Sigma lets users drill, pivot, and add calculations directly on governed data, many of those questions get answered in the moment.

Can we migrate from Looker to Sigma?

Yes, and it often happens during a warehouse move. Druva, for example, left Looker for Sigma, tripled adoption, and now has more than 400 weekly active users. Migrations connect Sigma to the same warehouse, translate the core LookML metrics into Sigma's data models, and rebuild the most-used dashboards first.

Why teams choose Sigma vs Looker

Sigma is the enterprise leader in self-service analytics and operational workflows.

Book a demo Start a free trial

Build without writing LookML
Analysts and business users model and explore in a spreadsheet interface, with SQL when they want it.
Governed AI on the data you trust
Sigma Assistant and Sigma Agents use each person’s warehouse permissions on every answer.
Billions of rows, live in your warehouse
Queries run at full detail in the warehouse, down to the row behind any number.

Fulfillment OpsSave as

Fulfillment

DC: All

On-time delivery

87.1%

8.7 ptsvs week 37

Orders shipped

48,210

3.2%vs week 37

Avg. days to ship

2.6

0.4 daysvs week 37

On-time delivery by week

Shipments by DC

DC	Shipped	On time
Reno	9,820	81.4%
Columbus	12,410	94.2%
Dallas	11,035	93.8%
Atlanta	8,760	95.1%
Allentown	6,185	96.0%
Phoenix	5,940	95.7%

Trusted by teams across industries

How Sigma and Looker compare

As of March 26, 2026.

Spreadsheet InterfaceLookerNo spreadsheet view, forces users into rigid, read-only dashboards. Any spreadsheet-style exploration requires an engineer to write proprietary LookML. SigmaFamiliar spreadsheet UI for accessible analysis and no-code AI App building without added licenses or waiting on IT.

Required SkillsLookerMust know LookML to write formulas and requires manual adjustments. Even basic metric creation or formula changes require data engineers trained in LookML syntax.SigmaCombine familiar spreadsheet formulas, SQL, and Python on a single governed canvas. Everyone from non technical users to data engineers can build immediately.

Warehouse IntegrationLookerRelies on proprietary caching layers to mask performance issues, moving data outside the warehouse and complicating enterprise security governance.SigmaLeverages your cloud data warehouse’s native caching. Performance accelerates automatically without ever extracting data, duplicating logic, or breaking inherited security policies.

Real-Time CollaborationLookerLacks true multiplayer co-editing. Development is siloed behind rigid Git commits and Developer Mode branches, creating version conflicts and slowing down team analysis.SigmaCloud-native architecture enables real-time, synchronous collaboration. Multiple users can build, edit, and explore the same live workbook simultaneously without locking files or overwriting work.

WritebackLookerFundamentally a read-only reporting tool. Capturing user inputs or triggering actions requires exporting data or buying expensive, disconnected third-party workflow extensions.SigmaSafely write governed decisions back to the warehouse and instantly trigger enterprise workflows from one environment.

AI ApplicationsLookerStops at dead-end reporting. Cannot build applications or workflows, forcing teams to rely on separate engineering-heavy platforms to take action.SigmaMove beyond read-only dashboards. Empower all teams to build interactive, no-code AI Apps, safely writing decisions and actions to your cloud data warehouse.

Python + SQLLookerSQL requires LookML. No native Python support so teams must execute more sophisticated data science in disconnected notebooks, breaking governance and isolating data.SigmaCombine SQL, Python, and spreadsheet formulas in one secure canvas. Technical teams can build advanced Python logic that all users can easily pivot and explore.

Drilldowns & PivotsLookerSeverely restricts data discovery. Every customized drill path and pivot must be painstakingly predefined in LookML by an engineer before a user can explore it.SigmaEmpower users to instantly drill down to the underlying row level or pivot massive datasets on the fly, directly against live warehouse data.

Lineage & GovernanceLookerIT must rebuild and maintain a duplicate semantic security model within LookML, creating dangerous gaps between warehouse permissions and access policies.SigmaVisual lineage traces data origin and transformations at data-element level.

Customer SupportLookerAvailable for admins only.SigmaAll users have access to live, in-product chat support averaging a 23-second initial response time from a real human, ensuring zero lost momentum.

Straight from the source

Sigma on G2 Sigma on Gartner Peer Insights

“Sigma is intuitive and allows users to build reports and dashboards quickly without needing to maintain an additional modeling layer (like LookML).”
Analytics EngineerSoftware · Gartner Peer Insights (opens in a new tab)
“Created for those with SQL, Python, & Excel knowledge. Allows for transferring those skills directly over to the platform without learning some domain-specific modelling language (LookML, DAX, M, etc.)”
Finance AssociateSoftware · Gartner Peer Insights (opens in a new tab)
“Sigma is the only tool that makes it easy to create dashboards and reports that are what my customers are looking for without workarounds. Plus, I'm not stuck with having to model my data in the BI tool, but can do all my modeling in dbt and connect directly to the Snowflake tables/views and not have to pay for additional storage or compute from the BI tool.”
Senior Manager, Data and Analytics EngineeringConstruction · Gartner Peer Insights (opens in a new tab)
“Writeback functionality: Unlike our prior BI tool, Sigma provides writeback functionality. This has given our internal users the ability to collaborate in real time within workbooks and even build their own apps.”
Director of OperationsBanking · Gartner Peer Insights (opens in a new tab)
“Through Sigma's MCP server, anyone with Sigma access can query governed data from our AI assistant (Claude) in plain language, and builders can have the AI create workbooks. A lot of one-off questions now become ephemeral reports that answer the question and then get out of the way. Because Sigma connects to Snowflake with 0Auth, people only see what the warehouse already permits, which keeps governance as AI use grows.”
VP, Data and AnalyticsConstruction · Gartner Peer Insights (opens in a new tab)
“One of the standout features for me is Sigma's drill-through functionality. It enables users to effortlessly explore data from high-level dashboards down to granular transaction-level details. This is especially useful when working with large datasets, where traditional tools are often slow or unusable.”
Data AnalystHealthcare and Biotech · Gartner Peer Insights (opens in a new tab)
“I use Sigma to embed analytics for customers on live Snowflake data, not a synced copy. RLS ensures customers see only their data, and flexible access handles different user levels. Non-engineers build reports, so our data team can focus on modeling and better solutions instead of one-off requests.”
Ryan M.Mid-Market (51-1000 emp.) · G2 (opens in a new tab)
“A lot of our end-users export data into gsheets or excel. While some of that still happens, Sigma's Excel-like interface meets users where they're at by enabling the use of Excel-like calculations. This increases usage and keeps data in one place.”
Director of Data and AnalyticsConsumer Goods · Gartner Peer Insights (opens in a new tab)

AI, Apps, and Agents with all the BI that you expect

Familiar formulas

We excel in the cloud

Analyze billions of rows of live warehouse data using spreadsheet formulas you already know. No stale extracts, row limits, or proprietary coding languages. Ask Sigma Assistant if you have a question.

Sales detail

Sales by region and product

Rolled up from 48,213,906 order lines

Region	Product	Sum of Revenue	Sum of Cost	Margin %
East	Laptops	$184,612,409	$141,238,774	23.5%
	Monitors	$72,418,236	$51,907,412	28.3%
	Docks	$31,806,551	$19,622,908	38.3%
	East total	$288,837,196	$212,769,094	26.3%
Central	Laptops	$121,344,870	$94,817,263	21.9%
	Monitors	$48,706,129	$35,588,417	26.9%
	Docks	$22,391,046	$14,066,372	37.2%
	Central total	$192,442,045	$144,472,052	24.9%
West	Laptops	$209,517,338	$158,326,045	24.4%
	Monitors	$88,143,702	$62,019,554	29.6%
	Docks	$39,228,815	$23,471,906	40.2%
	West total	$336,889,855	$243,817,505	27.6%
Total		$818,169,096	$601,058,651	26.5%

Governed AI

AI that works on the data you trust

Sigma Assistant answers questions and builds workbooks, and Sigma Agents take action on live warehouse data, with each person’s permissions applied to every step.

Configure agent

Hi there!

I'm here to help you configure your Renewal Risk agent. What would you like to adjust?

Add more data sources

How can I help you build your agent?

AI can make mistakes. Please double-check responses.

Renewal Risk

Flags renewal risk, explains the drivers, and alerts the account's CSM.

Data sourcesAccount Health

Tools

InstructionsNormal text ƒxGreeting

You are a Renewal Risk Agent for customer success managers.

Your Focus

Find accounts renewing in the next 120 days, rate their churn risk, and explain the drivers behind it.

Data

Use Account Health via database_execute_query for every risk question.

How to Respond

Lead with the accounts most at risk
Back every rating with usage and ticket data

Live data

Live on your warehouse, with no extracts

Every query runs in your warehouse, so analysis reads current data at full detail. There are no extracts to schedule or reload, and drilling from a total to the rows behind it takes a click.

Digital Commerce

Digital commerce

WEB_EVENTS · 3,812,447,209 rows, live on the warehouse

Date range

Jan 1, 2023 – Oct 9, 2026

Channel

AllWebApp

Metro

All

Events

Every page view, cart, and order since 2023

Sessions

212.6M

18%vs last year

17.9 events per session

Device mix

iOS46%
Web33%
Android21%

Return rate

Target 7.0%

8.4%

Sessions by metro

Click a metro to filter

Conversion funnel

All metros

Page views2.96B17%↓

Product views504.9M46%↓

Add to cart231.9M31%↓

Checkout71.4M58%↓

Purchase41.2M

Events by day and hour

All metros, all years

Captured decisions

Write directly back to your warehouse

If you know how to use a spreadsheet, you can safely capture data, run live scenarios, and trigger downstream workflows. Deploy Sigma Agents to fully automate those actions with a complete audit trail.

FY27 Headcount Plan

Total budget

$120.0M

4.8%vs FY26

Total forecast

$121.6M

1.3%vs budget

Departments with notes

6 of 12

1vs last week

FY27 Budget vs. Forecast

Edit data

	Department	Budget	Forecast	Notes	Last updated by	Last updated at
1	Engineering	$44,000,000	$45,184,600	Watch engineering backfills	Nora Kim	10/06/2026
2	Product	$9,000,000	$8,912,400		Nora Kim	10/06/2026
3	Design	$4,000,000	$4,063,250		Luis Ortega	10/07/2026
4	Support	$8,000,000	$7,431,800	Two senior hires slipped to Q1	Luis Ortega	10/07/2026
5	Marketing	$10,000,000	$10,486,900	Agency work moves in-house	Nora Kim	10/06/2026
6	Finance	$6,000,000	$5,978,150		Luis Ortega	10/07/2026
7	People	$5,000,000	$5,342,700	Recruiting adds two roles	Nora Kim	10/06/2026
8	Legal	$3,000,000	$2,961,400		Priya Shah	10/08/2026
9	IT	$7,000,000	$7,108,350		Nora Kim	10/06/2026
10	Operations	$11,000,000	$10,524,600	Holding two roles open	Luis Ortega	10/07/2026
11	Security	$4,000,000	$4,318,900	Above plan on contractors	Priya Shah	10/08/2026
12	Data	$9,000,000	$9,237,500		Priya Shah	10/08/2026

Inherited controls

The security your warehouse already enforces

Sigma applies your warehouse roles and row- and column-level policies at query time. Version tags and workbook history show what changed, and who changed it, before it reaches everyone.

Store SalesStaging

Store sales

Q3 2026

Net sales

$12.4M

7.2%vs Q2 2026

Orders

184,000

4.8%vs Q2 2026

Avg. order value

$67.40

$1.60vs Q2 2026

Net sales by month

AprMayJunJulAugSep

Returns peak online

Return rate by channel, Q3 2026

Online
Marketplace
In store
Pickup

Avg. 5.1%

More reasons teams choose Sigma

Dashboards anyone can explore
Viewers filter, drill and pivot on the dashboard itself, down to row-level detail, so a new question doesn’t become a new request.
Analytics and apps inside your product
Embed live dashboards and apps in your own product, with each customer’s data kept apart by the same warehouse security.
Formulas, Python and SQL on one table
Analysts and data scientists work side by side on the same live table in a formula, Python, SQL or a prompt, then share one workbook with the business.

Ready to move

Moving from Looker

Guides for planning the move from Looker and rebuilding on live warehouse data.

What teams build on Sigma

Live data, apps that write back, and agents, on the warehouse they already run

View all

3x adoption increase. 400+ weekly active users.Why Druva left Looker — then tripled internal adoption using Sigma

30K+ users onboarded. 30% more queries.DoorDash Logs a 30% Increase in Queries while Keeping the Snowflake Cost Constant with Sigma

91% active user adoption. 300+ concurrent ab tests.How Duolingo Democratized Data Access While Protecting Sensitive Information

Related resources

All comparison posts

Why Druva left Looker — then tripled internal adoption using Sigma
Druva left Looker behind for faster performance with Sigma.
Read the story: Why Druva left Looker — then tripled internal adoption using Sigma
Why Looker Leaves Teams Lagging and How Sigma Empowers Ad Hoc Data Exploration
Explore how Sigma enables teams to easily harness the full power and potential of their data, without any data ever leaving the warehouse.
Read the post: Why Looker Leaves Teams Lagging and How Sigma Empowers Ad Hoc Data Exploration

Sigma and Looker FAQ

What teams ask when they compare Sigma with Looker.

What is the main difference between Sigma and Looker?
Looker centralizes logic in LookML, a modeling language that engineers maintain, which gives strong consistency but puts most changes in the data team's hands. Sigma keeps a governed semantic layer while letting business users build and adjust analysis themselves with spreadsheet formulas, so fewer requests queue behind the modeling team.
Can Sigma build apps and agents, or is it just BI?
Sigma covers governed BI, and it also builds AI apps with writeback and runs agentic workflows on the same warehouse under one control plane. Looker centers on the governed semantic layer and embedded analytics, so if you want to act on data and not only model and view it, Sigma's build-and-act layer is the larger difference.
Is Sigma a good Looker alternative?
For teams who value Looker's governed metrics but feel the maintenance and the wait for changes, Sigma is a common alternative. It keeps shared definitions while giving business users room to do ad-hoc work without opening a modeling ticket.
Do I need to learn LookML to use Sigma?
No. Sigma has no equivalent modeling language to learn. Governed metrics and relationships are defined in data models through a visual interface, and everyday analysis happens in spreadsheet formulas, SQL, or Python, so the data team is not the bottleneck for routine changes.
Does Sigma give the same governance as Looker?
Sigma offers shared metrics, version control, and permissions for a single source of truth, while allowing exploration on top of that foundation. Looker's governance is rigorous but rigid, which is exactly the trade some teams want to loosen so users can answer their own questions.
How does Sigma handle ad-hoc analysis versus Looker?
Looker tends to route ad-hoc requests through someone who knows LookML, which adds a wait. Because Sigma lets users drill, pivot, and add calculations directly on governed data, many of those questions get answered in the moment.
Can we migrate from Looker to Sigma?
Yes, and it often happens during a warehouse move. Druva, for example, left Looker for Sigma, tripled adoption, and now has more than 400 weekly active users. Migrations connect Sigma to the same warehouse, translate the core LookML metrics into Sigma's data models, and rebuild the most-used dashboards first.

Why teams choose Sigma vs Looker

Build without writing LookML

Governed AI on the data you trust

Billions of rows, live in your warehouse

How Sigma and Looker compare

Straight from the source

AI, Apps, and Agents with all the BI that you expect

We excel in the cloud

AI that works on the data you trust

Live on your warehouse, with no extracts

Write directly back to your warehouse

The security your warehouse already enforces

More reasons teams choose Sigma

Dashboards anyone can explore

Analytics and apps inside your product

Formulas, Python and SQL on one table

Moving from Looker

Migrate from Looker to Sigma

Migrating from Looker, step by step

The Looker converter

What teams build on Sigma

Related resources

Why Druva left Looker — then tripled internal adoption using Sigma

Why Looker Leaves Teams Lagging and How Sigma Empowers Ad Hoc Data Exploration

Sigma and Looker FAQ

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