Looker vs Power BI: A Comparison Between BI Tools

Published: June 7, 2024 - 14 min read

Julian Alvarado

Are you trying to decide between Looker and Power BI for your business intelligence needs? Both are powerful tools, but which one is the best fit for your organization?

Let’s compare them head-to-head to help you make an informed choice.

Looker And Power BI 101

Business intelligence software helps companies make sense of their data to drive better decisions. Looker and Power BI are two of the leading BI solutions on the market.

Looker is a cloud-based platform known for its data modeling capabilities and advanced analytics. It’s geared towards data analysts and developers. Power BI, part of the Microsoft suite, caters to a broader user base with its intuitive interface and AI-powered features.

Core Features And Functionality

Data Modeling

Looker’s strength lies in its sophisticated data modeling capabilities facilitated by LookML. This allows for efficient data exploration, transformation, and aggregation, providing users with a structured and scalable approach to data modeling.

Power BI, while offering robust data modeling features, adopts a more traditional approach with a focus on user-friendly interfaces and drag-and-drop functionality.

Looker Example:

A retail company wants to create a scalable data model to analyze sales data across multiple regions. Using LookML, the company can define data transformations, aggregations, and explore data efficiently. For example, Looker allows the team to create a data model that combines sales data with customer demographics to identify trends and optimize marketing strategies.

Power BI Example:

A finance department needs to create a budget forecasting model. With Power BI, users can utilize the drag-and-drop interface to import financial data, create relationships between datasets, and build interactive financial models. The user-friendly interface makes it easy for financial analysts to adjust variables and see the impact on the budget in real-time.

Data Visualization

Both Looker and Power BI excel in data visualization, offering a wide range of chart types, graphs, and interactive dashboards. Looker’s visualization library is extensive, allowing users to create visually appealing and insightful dashboards.

Power BI, leveraging its AI-driven analytics, provides dynamic and interactive visualizations with built-in insights and recommendations.

Looker Example:

A marketing team uses Looker to create dashboards that visualize the performance of different campaigns. They can use a variety of chart types, such as bar graphs, line charts, and heatmaps, to display key metrics like conversion rates, customer acquisition costs, and return on investment. The interactive dashboards allow the team to drill down into the data for deeper insights.

Power BI Example:

A sales team leverages Power BI to visualize sales performance across different regions and products. Using Power BI’s AI-driven analytics, they can create interactive dashboards that provide insights into sales trends, identify high-performing products, and highlight areas needing improvement. Built-in recommendations help the team optimize their sales strategies.

Integration Capabilities

Looker’s integration capabilities extend to Google products and services, making it a seamless choice for organizations leveraging Google Cloud Platform.

Power BI, on the other hand, boasts strong integration with Microsoft products such as Excel, SharePoint, and Teams, providing a cohesive ecosystem for data management and analysis. Additionally, Power BI can connect to over 100 data sources, including cloud services, databases, and third-party applications, enhancing its data integration capabilities.

Looker Example:

A tech company using Google Cloud Platform integrates Looker with its cloud services to analyze application performance data. By connecting Looker to BigQuery, they can create detailed reports on application uptime, response times, and user behavior, ensuring seamless data flow and comprehensive analysis within the Google ecosystem.

Power BI Example:

A manufacturing firm uses Power BI to integrate data from various sources, including Excel, SQL databases, and Azure services. Power BI connects these disparate data sources, allowing the firm to create unified reports on production efficiency, inventory levels, and supply chain performance, enhancing decision-making across the organization.

Collaboration Features

Both Looker and Power BI offer collaboration features for team-based data analysis and reporting. Looker provides shared workspaces, collaborative editing, and version control, facilitating collaboration among data teams.

Power BI emphasizes collaboration through its integration with Microsoft Teams, allowing users to share reports, collaborate in real-time, and receive notifications within the familiar Microsoft environment.

Looker Example:

A data science team collaborates on a complex data analysis project using Looker. They share a workspace where they can collaboratively edit reports and dashboards. Version control ensures that changes are tracked, and team members can revert to previous versions if necessary. This collaboration helps the team deliver accurate and comprehensive analyses faster.

Power BI Example:

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A project management team uses Power BI integrated with Microsoft Teams to track project progress. They share Power BI reports within Teams, allowing team members to collaborate in real-time, discuss insights, and make decisions based on up-to-date data. Notifications and alerts within Teams keep everyone informed about important updates and milestones.

Ease Of Use And Implementation

User Interface

Power BI is known for its intuitive and user-friendly interface, making it accessible to both technical and non-technical users. Its drag-and-drop functionality, pre-built templates, and guided workflows simplify the data analysis process. Looker, while offering a customizable and powerful interface, may have a steeper learning curve due to its reliance on LookML for data modeling and customization.

Implementation Process

The setup and implementation process for both Looker and Power BI vary based on factors such as deployment options, data sources, and organizational requirements. Power BI offers quick and straightforward setup, especially for organizations already using Microsoft technologies. Looker’s implementation may require more technical expertise, particularly in configuring data models and permissions.

Advanced Data Analytics and Predictive Features

Looker’s Capabilities

Looker offers advanced data analytics capabilities, including predictive modeling, anomaly detection, and trend analysis. Its integration with machine learning platforms allows users to leverage predictive models for forecasting and decision-making. Looker’s data governance features ensure data accuracy and consistency, crucial for reliable predictive analytics.

Power BI’s Predictive And AI-Driven Analytics

Power BI provides robust support for predictive and AI-driven analytics through its Power BI Premium and Pro versions. Users can leverage built-in machine learning algorithms, predictive forecasting, and automated insights for predictive modeling. Power BI’s integration with Azure Machine Learning and other AI platforms enhances its predictive capabilities.

Programming Languages And Advanced Analytics Capabilities

  • Looker’s Language Support: Looker supports SQL-based queries and LookML for data modeling, providing a familiar environment for SQL developers and analysts. Its support for Python and R enables advanced analytics and statistical modeling within the Looker platform. Looker’s API integration allows for custom scripting and automation of data workflows.
  • Power BI’s Capabilities: Power BI supports a wide range of programming languages, including DAX (Data Analysis Expressions), M, and R. DAX is particularly useful for data manipulation and calculation within Power BI reports. Power BI’s integration with Azure services and open-source libraries expands its capabilities for advanced analytics, data transformation, and predictive modeling.


Both Looker and Power BI excel in advanced data analytics and predictive features. Looker’s strength lies in its flexibility and integration with machine learning tools, allowing for customized predictive models. Power BI, with its seamless integration with Azure AI services, offers a comprehensive suite of predictive analytics capabilities tailored to different business needs.

Pricing and Cost Considerations


  1. Pricing Models: Looker offers custom pricing based on the number of users and the scale of deployment. They do not publicly provide specific prices, as the cost is tailored to each company’s needs and the range of functionalities required.
  2. Versions: Since Looker customizes pricing, it doesn’t have standard ‘free’, ‘pro’, or ‘premium’ tiers. Instead, they offer a fully customizable platform where the cost depends on the modules selected, user count, and deployment scale.
  3. Cost-Effectiveness: Looker’s strong integration capabilities, especially within the Google Cloud ecosystem, add value for enterprises needing robust data management and analytics. It is considered cost-effective for large organizations that can fully utilize its expansive BI capabilities and detailed customization.

Power BI

  1. Pricing Models:
    • Free Version: Offers basic data exploration and reporting features, suitable for individual users or small teams. It includes data connectivity, visualizations, and dashboard creation but has data capacity and refresh rate limitations.
    • Pro Version ($9.99/user/month): Includes collaboration features, greater data refresh rates, more storage capacity, and additional data connectivity options. Suitable for teams within larger organizations.
    • Premium Version (pricing starts at $4,995/month per dedicated cloud compute and storage resource): Offers advanced AI capabilities, big data analytics, on-premises reporting, and more frequent data refreshes. It is aimed at large enterprises requiring extensive data processing and storage.
  1. Cost-Effectiveness: Power BI provides a scalable solution from free users to large enterprises. The Pro version is generally sufficient for most mid-sized companies, making it a cost-effective option when compared to other BI tools that require larger initial investments..

Pricing Comparison Table

Feature/VersionLookerPower BI FreePower BI ProPower BI Premium
Pricing ModelCustomizedFree$9.99/user/monthStarts at $4,995/month
User BaseLarge enterprisesIndividual usersTeams within large organizationsLarge enterprises
Data RefreshCustomizableHourly30 minutesUp to 48x per day
StorageDepends on deployment1 GB10 GB/user100 TB overall
AI CapabilitiesAdvanced, customizableBasicModerateAdvanced
ComplianceISO/IEC 27001, HIPAABasic securityEnhanced securityTop-tier security

Security and Compliance

Both Looker and Power BI prioritize data security with features such as role-based access control, data encryption, and audit logging. Looker provides granular access controls and data governance capabilities, ensuring data integrity and compliance with regulatory standards. Power BI integrates seamlessly with Microsoft’s security features, offering robust data protection and compliance measures.

  • Looker: Being part of the Google Cloud, it inherits robust security measures including data encryption at rest and in transit, compliance certifications such as ISO/IEC 27001, and support for HIPAA compliance.
  • Power BI: Offers advanced security features through Azure, including Active Directory integration, data sensitivity classification, and compliance with standards like GDPR, HIPAA, and FedRAMP.

Customer Support and Training

Looker Support:

Looker offers comprehensive customer support, including documentation, tutorials, and online resources. Users can access training materials, community forums, and technical support for troubleshooting and assistance with platform features.

Power BI Support:

Power BI provides extensive support through Microsoft’s ecosystem, including documentation, forums, and virtual training sessions. Users benefit from Microsoft’s global support network, ensuring timely assistance and updates on platform enhancements.

Collaboration and Sharing 


  1. Shared Dashboards and Reports: Looker allows users to create dashboards and visualizations that can be shared across the organization. These shared dashboards ensure that all team members have access to the same data, fostering a unified view of key metrics and insights.
  2. Live Collaboration and Commenting: Team members can collaborate in real-time by commenting on specific parts of a dashboard or visualization. This feature facilitates discussion directly in the context of the data, enhancing communication and decision-making processes.
  3. Access Controls and Permissions: Looker provides detailed control over who can view or edit various elements within the tool. Administrators can set permissions at granular levels, which is crucial for maintaining data security while promoting collaboration.
  4. Integration with Third-Party Tools: Looker integrates with popular collaboration tools like Slack and email platforms, enabling users to share insights and reports directly through these channels. This integration helps streamline workflows and communication.

Power BI

  1. Integration with Microsoft Teams and SharePoint: Being a part of the Microsoft ecosystem, Power BI offers seamless integration with Microsoft Teams and SharePoint. This allows users to publish reports within Teams channels or SharePoint sites where team members can view and interact with the data.
  2. App Workspace: Power BI users can create workspaces that serve as collaboration hubs where teams can work together on dashboards and reports. These workspaces facilitate the management of data access and content distribution within a group.
  3. Real-Time Dashboard Updates: Any changes made to a Power BI report or dashboard are updated in real-time. This feature ensures that all team members are looking at the most current data, which is essential for timely and informed decision-making.
  4. Row-Level Security: Power BI supports row-level security (RLS) that controls data access at the row level based on which user is viewing the report. This is particularly useful in organizations where data needs to be secured and selectively shared among different departments or roles.

Mobile Accessibility and User Experience

For data on the go, both Looker and Power BI offer mobile apps for iOS and Android.

Power BI Mobile

Power BI’s mobile experience is particularly robust. The app is fully featured and optimized for touch. Users can access reports and dashboards, get alerts, and even do light authoring from their phones.

Looker Mobile

Looker’s mobile app is a bit more bare-bones in comparison. It’s great for reference, but less suited for heavier analytical tasks.

Mobile App Capabilities And User Experience Comparison

FeatureLookerPower BI
Platform SupportiOS, AndroidiOS, Android, Windows
Real-time UpdatesYesYes
Interactive DashboardsHighly interactive, touch-optimizedHighly interactive with annotations
Offline AccessLimitedYes
User ExperienceOptimized for mobile use, responsive designDesigned for deep interaction, supports offline mode

This comparison shows that while both tools offer robust mobile support, Power BI provides additional functionalities like support for Windows devices and offline access, which might be crucial for users who frequently work without stable internet access. On the other hand, Looker’s mobile app is well-optimized for touch interactions, making it user-friendly for on-the-go data exploration.

Pros and Cons

Before we wrap up, let’s glance at the pros and cons of each platform.

FeatureLooker AdvantagesLooker DisadvantagesPower BI AdvantagesPower BI Disadvantages
FlexibilityHighly customizable with the ability to write custom SQL.Requires technical expertise to fully exploit its capabilities.Wide range of built-in visuals and easy integration of custom visuals.Less flexibility in custom query languages compared to Looker.
IntegrationStrong integration with Google Cloud and other databases.Integrations can be complex and require careful setup.Excellent integration with other Microsoft products and services.Primarily optimized for Microsoft environments.
ScalabilityScales well for large datasets and complex analytical needs.Cost may increase significantly as scaling requirements grow.Scales easily within the Microsoft cloud ecosystem.Cost scaling can be unpredictable depending on usage patterns.
User InterfaceModern interface that is clean and highly customizable.Steeper learning curve for non-technical users.User-friendly interface, especially for users familiar with Microsoft tools.Some users may find the interface clunky or less intuitive.
AnalyticsAdvanced data modeling capabilities; strong in data governance.Requires more initial setup and maintenance.Deep integration with AI and machine learning through Azure.Basic version has limited analytics capabilities.
SupportHigh-quality support but can be costly.Smaller user community compared to Power BI.Extensive documentation and large community for support.Quality of support can vary; higher tiers get better support.
CostCustom pricing model which can be a pro or con depending on the organization’s size and needs.Custom pricing can be opaque and expensive.Free tier available; Pro and Premium versions offer reasonable costs for added features.Premium features can be costly for small to mid-sized businesses.

Situational Benefits and Drawbacks

  • Looker
    • Benefits: Best suited for large enterprises with complex data needs and those heavily invested in Google Cloud.
    • Drawbacks: Not as cost-effective for small businesses or those with simpler data visualization needs. Requires users to have a good understanding of SQL and data modeling.
  • Power BI
    • Benefits: Ideal for organizations already using Microsoft products. It’s particularly beneficial for users who require seamless integration with tools like Excel and Azure. The learning curve is lower for those familiar with the Microsoft ecosystem.
    • Drawbacks: Organizations not using Microsoft products might not benefit as much from its full capabilities. Advanced features are locked behind higher payment tiers, which might be a barrier for smaller companies.

This table and discussion should help in assessing which tool might be the best fit based on specific organizational needs, user base, and technical capabilities.

FeatureLookerPower BI
Strengths– Advanced data governance
– Robust data modeling capabilities
– Efficient handling of large data volumes
– Seamless integration with Google products
– Wide integration with Microsoft products
– Accessibility for non-technical users
– Advanced visualization and customization capabilities
– Budget-friendly for smaller organizations
Weaknesses– Steep learning curve for LookML
– Limited visualization customization options
– Limited customization options for visualizations
– Challenges with handling larger datasets

Looker vs Power BI: Conclusion and Recommendations

When deciding between Looker and Power BI, consider your specific business needs, existing tech ecosystem, and the scale of data handling. If you require advanced data governance and robust data modeling capabilities, Looker may be the better choice. If you prioritize ease of use and wide integration with Microsoft products, Power BI could be the way to go.

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Julian Alvarado Content Marketing
Julian is a dynamic B2B marketer with 8+ years of experience creating full-funnel marketing journeys, leveraging an analytical background in biological sciences to examine customer needs.
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