Looker vs Power BI: A Detailed Comparison Guide in 2023

Looker vs Power BI – which data visualization tool is right for your business? These two software offers different approaches to data visualization, and each tool has its strengths and weaknesses.

Both are popular business intelligence tools and help businesses generate insightful data visualizations about their performance. But there are some key differences between these two tools that can help you decide which one is right for your organization.

So, which tool should you choose for your business?

This blog will help you learn the differences between Looker and Power BI on 7 different criteria. So, you can choose the business intelligence tool that will best fit your business requirements. By the end of this blog, you will learn what Microsoft Power BI does and what are the capabilities of the Google Looker tool.

Difference Between Looker vs Power BI Tools

Check the following comparison table between Looker and Power BI to quickly understand the overall differences.

Factors Looker Power BI
Price comparison Looker price ranges from $30 to $70 per user/month. And it can go even higher depending on the requirements of the organization Power BI price ranges from $9.99 to $20 per user/month, and the Power BI Desktop version is free to download. In addition to that, the minimum licensing fee is $4995
General features Looker leans on its Google roots to provide high-end analytics and visualization features. The native data mining techniques are also a bonus for the users. Microsoft is constantly updating Power BI with high-level connectors and APIs. Anomaly detection, AI-infused experience, and smart narratives are the latest addition.
Analytical power LookML allows the user to bypass SQL to establish dataset relationships. But executing queries can be a bit tedious. AutoML makes predictive analytics more accessible to the users. You can choose between binary, classification, and regression data models.
Integration Looker is compatible with all Google Tools, Microsoft, Linux, and Mac devices. Power BI works well with Microsoft, Mac, and Linux. The connectors allow the user to connect with SQL and R integration.
Cloud compatibility Looker entirely operates from the cloud and has no on-premise software. Power BI works only works with the Azure cloud and requires on-premise software for complex calculations.
Data visualization Users can create unique charts and data with real-time data monitoring capabilities. Users highly praise the templates and prebuilt charts available in the library.
Data handling Looker is limited to 5000 rows. The data handling capability of Power BI is limited to 10 GB per session.

As you got the core differences, now it’s easy for you to know what is the uniqueness of each tool. Furthermore, you can also have an idea about which tool to choose in specific conditions like large data management and cloud requirements. For an in-depth understanding of each tool, explore the following difference based on different criteria in detail.

Detailed Comparison Between Looker vs Power BI

Let’s start comparison with the pricing factor.

  1. Looker vs Power BI: Price Comparison

    Power BI:

    The price of Power BI varies depending on the packages. The Power BI desktop variant is free for users but comes with some restrictions. Users can connect to over 70 data sources and access files through the cloud data storage feature. The Power BI Pro version encourages more collaboration amongst the analysts and costs around $9.99 per user/month. The Power BI Premium version is the most potent version of Power BI, priced around $20 per user/month. However, to access it, organizations need to pay a minimum licensing fee of $4995 per year. This version allows the professional to share and publish across the organization easily.

    The pricing model of most packages of Looker is bespoke and designed specifically for the organization. According to avid software users, Looker can cost you anywhere between $50-$70 per user/month, depending on the package size. The product costs around $30 per user/month for read-only users. If you want to use Looker as the BI tool of your choice, you can ask for a quote on their official website.

    Winner: Power BI

    Power BI wins here. The predetermined pricing structure of Power BI helps you make better decisions instantly to know which package is the best suitable. Also, the packages for Power BI are comparatively cheaper than Looker.

  2. Looker vs Power BI: General Features

    Power BI:
    Since Microsoft owns Power BI, its compatibility with Microsoft Office, Teams, Office 365, SharePoint, and Azure cloud is unmatched. In addition, Microsoft releases weekly and monthly updates to increase its integration capabilities with other platforms. These updates include connectors and APIs, amongst other features. Recent updates include smart narratives (NLG), anomaly detection capabilities, and AI-infused experiences. The Power BI Premium version can tap into the storage capabilities of any of Microsoft’s numerous data centers worldwide. One of the significant disadvantages is that you can only use Power BI with an Azure cloud system.

    To compete with Microsoft, Google is providing the best upgrades to Looker. The tool already possesses stunning visualization and analytical capabilities. In addition, Looker leverages its Google connections by increasing its compatibility with other tools, like Google Analytics. Some businesses use Looker for its wide array of data mining techniques. Analysts can bypass SQL while establishing relationships using Looker-specific data modeling language.

    Winner: Tie

    While users praise Looker for its data mining and Google features, it is not as potent as Power BI. But Power BI’s compulsory Azure integration puts it at a disadvantage.

  3. Looker vs Power BI: Analytical Power

    Power BI:
    Power BI has countless machine learning features to assist professionals with predictive analytics. Power BI offers proprietary AutoML (Automatic Machine Learning) features. This feature makes Power BI more accessible to less code-savvy analysts. You just have to enter the required data and choose the ML model of your choice. Finance analysts use the regression model to compute the future cash flow. While the binary model is used to derive simple binary outcomes. The classification model divides the function into multiple datasets and generates multiple outcomes. Additionally, you can also use a natural language query tool to get quick answers to your questions.

    Unlike Power BI, Looker has a proprietary modeling language called the LookerML. Once mastered, it becomes simple to unlock the platform’s analytical capabilities. As a bonus, with LookerML, you can easily bypass SQL to establish data relationships. However, the visualization feature is too basic, and it can be a chore to execute the queries. But you will praise its drag-and-drop interface and pre-built templates because of its excellent capabilities. Also, Power users can also create models and save them in the library.

    Winner: Power BI

    Power BI takes the lead in number crunching and extensive visualization abilities. However, companies that exclusively deal with Google products can consider Looker for easier integration.

  4. Looker vs Power BI: Integration

    Power BI:
    Apart from all the Microsoft tools, Power BI is also compatible with SQL and R integration. The analysts do not have to download any third-party connectors. Power BI hosts all the connectors and APIs to make connections with data sources easy. Power BI also supports platforms like Linux and Mac to offer better usability to users according to their device. In addition, Power BI supports data sources including flat files, SQL-based databases, Azure Cloud Platform, Excel Workbook, SAP HANA Database, IMB Db2 Database, and Oracle database.

    Looker is compatible with Windows, Linux, and Mac. It is also tightly integrated with Google tools and 60+ data sources. As Power BI, Looker also offers multiple APIs and connectors to enable integration and data connectivity with other software and data sources like Google BigQuery, Amazon Redshift, Snowflake, MySQL, SAP HANA, and Amazon Aurora. One of the significant qualities of Looker is its own trusted data model. Analysts can use the connectors to export/ import pre-built data models to other visualization platforms.

    Winner: Power BI

    The vast breadth of Power BI’s connectors and APIs is incomparable to that of Looker.

  5. Looker vs Power BI: Cloud Compatibility

    Power BI:
    Power BI is only compatible with enterprise systems on the Azure cloud platform. The fully functional SaaS version of Power BI runs on the Azure cloud. Another version resides in the Power BI report server. The Desktop version works independently, and you can store projects in the cloud servers. The on-premise version of the software is not as potent as the cloud version. However, when you want to perform complex computations with multiple data sources, you have to download the Power BI on your desktop. Moreover, you can learn about the difference between Power BI Cloud and On-premise to know which option is better for your business.

    On the other hand, Looker entirely operates from the cloud and requires no additional downloading of software. In other words, there is no on-premise version of the software. Once your organization gets the Looker license, you can directly access it through browsers.

    Winner: Looker

    The flexibility of using the cloud-based Looker is far superior to that of Power BI.

  6. Looker vs Power BI: Data Visualization

    Power BI:
    Power BI is one of the most powerful data visualization tools in the market. There are several templates available that you can utilize to create visual data—striking reports, charts, and graphs. Furthermore, Power BI can hold around 3500 data points. There is a visualization pane to help you arrange the data and prep it for reports. If pre built templates do not match the aesthetics of your reports, you can always build your custom models. Easily a great tool to create data visualizations.

    Analysts can also create unique interactive visualizations/ visualize data in Looker. Moreover, Looker features streaming analysis that allows the user to monitor the real-time data changes in the report. A visualization tab can add or remove any customizations to your LookML project. The ‘read-only’ users can view the projects and their customization on the Explore Visualization page. But you must create these custom visualizations using a JavaScript file. Hence, you can directly integrate the data visualizations into your LookML files or hosted elsewhere using JavaScript.

    Winner: Power BI

    Microsoft Power BI is one of the best visualization tools because of its features. The customization features of Looker cannot easily compete with it.

  7. Looker vs Power BI: Data Handling

    Power BI:
    Microsoft Power BI handles large data amounts by analyzing data. It has capped its data handling capabilities at around 10GB of data per user. This means that while uploading and downloading databases and models, the users must adhere to the maximum limit. To bypass this, most analysts need to depend on data reduction methods. For example, instead of creating swanky charts and reports, create basic tables for their clients. The Power BI community also adjusts the total volume of the data depending on the type of work.

    There is no such limit in Looker. But the explore page is limited to 5000 rows. Users actively use filters and other tools to narrow down the data. For some functions, like filtering different relationships, users need to create a query using LookML (unique modeling language).

    Winner: Looker

    Looker is just marginally better than Power BI here. Users can customize their charts and reports without worrying about the data limit.

So, which is the best business intelligence tool for your business? As you see, both tools have strengths and weaknesses like availability of features and cloud compatibility. So, it totally depends on your requirements and what solution comes under your budget and whether it’s the best fit to achieve your goals.

Frequently Asked Questions

Is Power BI better than Looker? If so, how?

Power BI is better than Looker in the business intelligence tools market. This is because Looker is relatively new, and Google is still updating it to compete with the more established players.

Which BI tool is better than Power BI?

While Power BI is the most used data visualization tool, Tableau’s functionality is more advanced such as data exploration, interactive visuals, and deployments.

Is it hard to learn Power BI for beginners?

No. Power BI is easy to learn because of its easy-to-use user interface. Also, beginners do not require prior knowledge of BI to use this tool. To know the detailed answer, check our guide — is it easy to learn the Power BI tool?

Does Looker use SQL?

Technically, no. However, it generates SQL database queries and establishes dataset connections using the LookML modeling language.

Which is Better Between Looker vs Power BI?

Now that we’ve comprehensively discussed the differences between Looker and Power BI, the choice lies with you and your unique requirements. If you are in the market for a proper BI tool with advanced visualization features, you should go for the Power BI service. It has already established itself as one of the market leaders. Looker is still developing and lacks some crucial features like ETL capabilities. Looker stores all business logic data with numerous capabilities. But if your organization primarily uses Google Tools, using Looker would be better for the setup and compatibility.

Gaurang Bhatt

Written by

Gaurang Bhatt

Gaurang has 15+ years of experience solving complex business problems and enabling businesses with data-driven decisions using data analysis and predictive modeling tools like Tableau, Power BI, Looker, and Google Data Studio. His expertise lies in data visualization, reporting, and creating ETL pipelines. In addition, he is passionate about exploring different technologies like machine learning and AI. He shares his knowledge and learnings on the LabsMedia platform.