Google BigQuery vs. Microsoft Power BI

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.4 out of 10
N/A
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$0.04
Microsoft Power BI
Score 8.6 out of 10
N/A
Microsoft Power BI is a visualization and data discovery tool from Microsoft. It allows users to convert data into visuals and graphics, visually explore and analyze data, collaborate on interactive dashboards and reports, and scale across their organization with built-in governance and security.N/A
Pricing
Google BigQueryMicrosoft Power BI
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
No answers on this topic
Offerings
Pricing Offerings
Google BigQueryMicrosoft Power BI
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Google BigQueryMicrosoft Power BI
Considered Both Products
Google BigQuery
Chose Google BigQuery
is much better as it’s easily accessible provides velvet documentation and fulfils all our needs as well as easily integrated into clients, environment
Chose Google BigQuery
Google BigQuery is simpler and I say it has simpler UI too.
If you have a clear long term ask , mainly business intelligence needs then Google BigQuery offers you good.
If you need too much of features under a single cloud and you are ok to be lil clumsy then you can check …
Chose Google BigQuery
I have used most of the data analytics platforms. Based on my work, I have found that the user interface of Google BigQuery is simple to navigate. I like the front view - ease of joining tables, and integration with other platforms.
Chose Google BigQuery
Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale.
The price was also much lower for our use case (internal data analysis).
Chose Google BigQuery
For our usage, Google BigQuery is cheaper and more performant. The others have their place, but in certain scenarios, Google BigQuery is a better solution.
Chose Google BigQuery
We actually use Snowflake and BigQuery in tandem because they both currently meet various needs. Redshift, however, has barely been used since our migration away from it. In the case of both Snowflake and BigQuery, they beat Redshift by a long shot. The main reasons are their …
Chose Google BigQuery
I came to use BigQuery from a traditional system like MS SQL server, the features which are available in BigQuery as a cloud service far outweigh the features from SQL server. I have not used other similar tools like Amazon Redshift but Google BigQuery serves multiple use cases …
Chose Google BigQuery
Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than snowflake. Redshift is not easy …
Chose Google BigQuery
In my opinion, Google BigQuery is custom made to be the best data lake system that is easy to use, scalas to fit any business size, has inbuilt security, as well as tools for data integrity. Although a few other tools have some of the same functionality, Google BigQuery is the …
Chose Google BigQuery
It's easier to connect data between BigQuery and looker studio instead of connecting the data between BigQuery and tableau in terms of data explore or dashboard creating. Therefore we are considering migrating dashboards from tableau to looker studio for the whole company.
On …
Chose Google BigQuery
When comparing Google BigQuery and Databricks, both platforms are powerful tools for managing and analyzing large datasets. BQ is ideal for businesses requiring large-scale analytics, reporting, and dashboarding with minimal operational overhead. It’s also great for ad-hoc …
Chose Google BigQuery
Google BigQuery's main advantage over its direct competitors (Amazon Redshift and Azure Synapse) is that it is widely supported by non-Google software, while the others rely heavily on their own cloud ecosystems.
Chose Google BigQuery
I have used other data manipulation tools like SQL Server and Google BigQuery feels more intuitive, Google provides so much documentation and tutorials that getting to know the software is not only easy but even satisfactory, so I'd say Google BigQuery is very superior to that …
Chose Google BigQuery
Amazon Redshift was a likely alternative we were considering , but it needs to be provisioned on cluster and nodes, which increases infrastructure management, whereas Google BigQuery is serverless, so no infra management :) Also, I remember when comparing them we did found out …
Chose Google BigQuery
Its same as compared to Big query. We go with big query because of clients requirements in project.
Chose Google BigQuery
Google BigQuery as a platform allows for more integrations and customizability than many other offerings. Users mostly need to understand the basics of database and SQL programming in order to get the most from the product. However, other products like Hevo do have less of a …
Chose Google BigQuery
There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
Chose Google BigQuery
The data performance of Google BigQuery is best as per other software. Limitations on Google BigQuery's data size are superior to those of Microsoft SQL. Obtaining real-time data from several IoT devices is another benefit.
Chose Google BigQuery
I personally find it by far simpler than Amazon redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily …
Chose Google BigQuery
Compared to SingleStore, BigQuery has a big advantage of being completely serverless, and without practical limitations.

Compared to RedShift, we found the cost model to be more fitted to our needs.
Chose Google BigQuery
BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.
Chose Google BigQuery
We focused more on data volume and less on full application capabilities. All in all, we found that the two solutions complement each other. For integration, some sources were better handled in SAP HANA, particularly other SAP systems where Google Big Query was more suitable …
Chose Google BigQuery
SingleStore has a much lower query latency compared to BigQuery. Thus, we segregate faster tasks to SingleStore, and use BigQuery has our main database to store all historical data.
Chose Google BigQuery
Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of …
Chose Google BigQuery
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Microsoft Power BI
Chose Microsoft Power BI
All others apps are enablers and Microsoft Power BI is the visual that end user sees which often adds more value to the end user to make strategic decisions from this. All are equally great but Microsoft Power BI is the end result
Chose Microsoft Power BI
Microsoft Power BI vs. Tableau Power BI cheaper and works well with Microsoft tools. Tableau has better visuals but is costly. Microsoft Power BII vs. Looker Looker is good for big data, but Microsoft Power BI is easier to use. Microsoft Power BI vs. Qlik Qlik is fast for big …
Chose Microsoft Power BI
Microsoft Power BI provide a user friendly in many ways to analyse the data and visualization of the data .Stacking makes the works easier easily attach the table ,chart row and column through the various available tool ..it also reduces the work part and manpower mechanically …
Chose Microsoft Power BI
Microsoft Power BI is more flexible and it requires less effort in order to create a working environment. The results have an attractive and customizable display. The model is a star schema therefore it do not require specific database adaptation. Excel and csv files could be …
Chose Microsoft Power BI
Microsoft Power BI is free. If I didn't want to create a custom platform (i.e. my organization insisted on an existing platform that I *had* to use), I'd use Microsoft Power BI. For any start-up or SMB, I'd just use Claude & Grok to build it quickly, also for free. Would …
Chose Microsoft Power BI
Same need to be large scale.
Chose Microsoft Power BI
Microsoft Power BI is easier to learn, a lot of examples on YouTube, internet documentation books helps to the implementation and personalization of the dashboards easier to use than other platforms.
Chose Microsoft Power BI
While excel can be useful for a very quick data dump, Microsoft Power BI is able to bring that data to life, and show trends and the actual story of what is happening. Microsoft Power BI is the ultimate display tool, and allows us to share information quickly to the CORRECT …
Chose Microsoft Power BI
Compared to this tool, Microsoft Power BI doesn't involve heavy coding and provides user friendly interface to visualize data. Microsoft Power BI reports can be shared and published without exposing your source code. Also, the reports are interactive providing drill down …
Chose Microsoft Power BI
Power BI is much simple to use, and more modern than BusinessObjects, which has now been discontinued. It costs much less to license than Tableau which is perhaps more niche and designed better. It is also much more powerful for data analysis than excel, smartsheet, airtable, …
Chose Microsoft Power BI
Some of the strengths are 1. User-Friendliness 2. Self-Service BI (Caters to all levels of the employees 3. Cost-effective. 4. Easy integration with Microsoft Suits. 5. DAX Calculations 6. Familiar Interface like traditional Excel. 7. Easier Self-Service platform for …
Chose Microsoft Power BI
It's got a larger user base and seems better supported. Personally it feels like alot more work getting comparable results from Tableau. Microsoft Power BI will be WAY easier to work with all your various data sources, if you are working in an environment with M365 already. …
Chose Microsoft Power BI
After several years using Google Looker Studio and BigQuery, Microsoft Power BI is a step-up in terms of visualizations. It is also much more powerful, leading to less errors and has a more intuitive interface. Looker studio has a focus on google analytics whereas Microsoft …
Chose Microsoft Power BI
NA - did not use any other software, yet. Happy with the services and features provided by Microsoft Power BI which helps us navigate through the client requests on a daily basis while also providing actionable insights / solutions with maximum efficiency and that too in a …
Chose Microsoft Power BI
It's the most widely used tool in most organizations
Chose Microsoft Power BI
Each has a different function. I think Microsoft Power BI is easier to use than Tableau and cheaper but SQL and databricks have so much more versatility
Chose Microsoft Power BI
We use Microsoft Power BI in addition to other software.
Chose Microsoft Power BI
I prefer Power BI because of its affordability and fewer complicated tools than Tableau. It's easy to use and compatible with other Microsoft products, which are mostly used in the IT industry. It's not limited to only one platform like Looker Studio, which is mostly used in …
Chose Microsoft Power BI
Microsoft Power BI is more flexible and can also handle more complex reporting scenarios. While Microsoft Excel is a great tool for analyzing data building visuals is not what it is made for. It is complicated to create Excel reports that will be interactive as a use wants to …
Chose Microsoft Power BI
Qlik also builds dashboards upon your data. It's more expensive as Microsoft Power BI and needs as much training as Microsoft Power BI
Chose Microsoft Power BI
Being Microsoft environment it is extremely easy to interconnect the application with other Microsoft tools, such as power point or excel. On top of that, also communication through Microsoft teams is enabled which can result in very good collaboration and exchange of feedback …
Chose Microsoft Power BI
We actually chose both Power BI and Quantum Metric. We use them for different things, but they are very similar tools. Quantum does a good job of integrating with our website and app traffic, whereas we use Power BI for data analysis and importing data sets for further review. …
Chose Microsoft Power BI
We have used Microsoft Power BI for almost seven years. When looking for a reporting platform, remember that you are about to make a long-term choice. Moving reports to another system isn't easy. If you have, for example, more than 100 reports, the move to a different platform …
Features
Google BigQueryMicrosoft Power BI
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.4
Ratings
3% below category average
Microsoft Power BI
-
Ratings
Automatic software patching8.00 Ratings00 Ratings
Database scalability9.20 Ratings00 Ratings
Automated backups8.50 Ratings00 Ratings
Database security provisions8.60 Ratings00 Ratings
Monitoring and metrics8.00 Ratings00 Ratings
Automatic host deployment8.00 Ratings00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.8
Ratings
8% above category average
Pixel Perfect reports00 Ratings8.50 Ratings
Customizable dashboards00 Ratings10.00 Ratings
Report Formatting Templates00 Ratings8.00 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.2
Ratings
2% above category average
Drill-down analysis00 Ratings7.00 Ratings
Formatting capabilities00 Ratings9.00 Ratings
Integration with R or other statistical packages00 Ratings8.00 Ratings
Report sharing and collaboration00 Ratings9.00 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.6
Ratings
3% above category average
Publish to Web00 Ratings8.00 Ratings
Publish to PDF00 Ratings9.00 Ratings
Report Versioning00 Ratings9.00 Ratings
Report Delivery Scheduling00 Ratings8.00 Ratings
Delivery to Remote Servers00 Ratings9.00 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.7
Ratings
9% above category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings9.00 Ratings
Location Analytics / Geographic Visualization00 Ratings8.00 Ratings
Predictive Analytics00 Ratings9.00 Ratings
Pattern Recognition and Data Mining00 Ratings9.00 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.6
Ratings
1% above category average
Multi-User Support (named login)00 Ratings9.00 Ratings
Role-Based Security Model00 Ratings9.00 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings10.00 Ratings
Report-Level Access Control00 Ratings7.00 Ratings
Single Sign-On (SSO)00 Ratings8.30 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.0
Ratings
2% above category average
Responsive Design for Web Access00 Ratings8.00 Ratings
Mobile Application00 Ratings7.70 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings7.70 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft Power BI
8.4
Ratings
7% above category average
REST API00 Ratings8.00 Ratings
Javascript API00 Ratings9.90 Ratings
iFrames00 Ratings8.00 Ratings
Java API00 Ratings8.00 Ratings
Themeable User Interface (UI)00 Ratings8.10 Ratings
Customizable Platform (Open Source)00 Ratings8.40 Ratings
Best Alternatives
Google BigQueryMicrosoft Power BI
Small Businesses
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
BrightGauge
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Score 9.1 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Reveal
Reveal
Score 10.0 out of 10
Enterprises
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IBM Cloudant
Score 7.4 out of 10
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Score 9.9 out of 10
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User Ratings
Google BigQueryMicrosoft Power BI
Likelihood to Recommend
8.6
(0 ratings)
8.5
(0 ratings)
Likelihood to Renew
8.1
(0 ratings)
9.0
(0 ratings)
Usability
7.7
(0 ratings)
7.9
(0 ratings)
Support Rating
7.3
(0 ratings)
10.0
(0 ratings)
Implementation Rating
-
(0 ratings)
9.0
(0 ratings)
User Testimonials
Google BigQueryMicrosoft Power BI
Likelihood to Recommend
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
Read full review
Microsoft Power BI is great for sales tracking, financial reporting, and real-time operations monitoring. It integrates data from multiple sources, creating interactive dashboards for better decision-making. However, it's less ideal for real-time big data processing, offline access, or when deep customization is needed. It works best for structured reporting but struggles with highly complex data models.
Read full review
Pros
  • Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
  • Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
  • Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
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  • It is great at taking in large amounts of data and housing it all.
  • Microsoft Power BI is so user friendly and it's dashboards are very easy on the eye.
  • The ease of sharing dashboards makes it great for use across a large company.
Read full review
Cons
  • It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
  • The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
Read full review
  • It would be easier for users could Microsoft Power BI and Excel used the same programming languages.
  • Would like to see the online version of Microsoft Power BI be as powerful as the desktop version.
  • Publishing a Microsoft Power BI file online and then having to save the file is somewhat redundant.
  • Would like to export each page or chart as an image.
Read full review
Likelihood to Renew
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
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Microsoft Power BI is an excellent and scalable tool. It has a learning curve, but once you get past that, the sky is the limit and you can build from the most simple to the most complex dashboards. I have built everything from simple reports with only a few data points to complex reports with many pages and advanced filtering.
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Usability
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
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Takes a little bit to get used to it. Not natively intuitive but fairly straight forward to pick up. Also docking it a few points because you can create a really clean, simple UI in Claude very quickly that's faster than building all of this yourself in Microsoft Power BI.
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Reliability and Availability
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
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No answers on this topic
Performance
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
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No answers on this topic
Support Rating
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
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It is a fantastic tool, you can do almost everything related with data and reports, it is a perfect substitutive of Power Point and Excel with a high evolution and flexibility, and also it is very friendly and easy to share. I think all companies should have Power BI (or other BI tool) in their software package and if they are in the MS Suite, for sure Power BI should be the one due to all the benefits of the MS ecosystem.
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Implementation Rating
No answers on this topic
It was integrated with our erp easily and was accessible on cloud.
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Alternatives Considered
Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.
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All others apps are enablers and Microsoft Power BI is the visual that end user sees which often adds more value to the end user to make strategic decisions from this. All are equally great but Microsoft Power BI is the end result
Read full review
Scalability
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
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No answers on this topic
Return on Investment
  • In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
  • Scalability is up-to-date and really helpful in multiple places.
  • Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
  • Also, it gives us more insights from our data, helping us make smarter decisions for our business.
Read full review
  • We're still early in the adoption process at this company, but we've illustrated how bad data keeps us from being more productive. ~25% of a team's work week was dedicated to effectively cleaning up entries, but it was always seen as a normal to them.
Read full review
ScreenShots

Google BigQuery Screenshots

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.

Microsoft Power BI Screenshots

Screenshot of Microsoft Power BI - Turns insights into impact for business usersScreenshot of Power BI integrates easily with Microsoft 365Screenshot of Microsoft Power BI - AI-Powered CapabilitiesScreenshot of Microsoft Power BI - Copilot can be used to create reportsScreenshot of Microsoft Power BI - Data HubScreenshot of Microsoft Power BI - Scales as organizational needs grow