Google BigQuery vs. Microsoft BI (MSBI)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.8 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.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Microsoft BI (MSBI)
Score 8.9 out of 10
N/A
Microsoft BI is a business intelligence product used for data analysis and generating reports on server-based data. It features unlimited data analysis capacity with its reporting engine, SQL Server Reporting Services alongside ETL, master data management, and data cleansing.
$14
per month per user
Pricing
Google BigQueryMicrosoft BI (MSBI)
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Power BI Pro
$14
per month per user
Power BI Premium
$24
per month per user
Offerings
Pricing Offerings
Google BigQueryMicrosoft BI (MSBI)
Free Trial
YesNo
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 BI (MSBI)
Features
Google BigQueryMicrosoft BI (MSBI)
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
80 Ratings
0% above category average
Microsoft BI (MSBI)
-
Ratings
Automatic software patching8.017 Ratings00 Ratings
Database scalability9.179 Ratings00 Ratings
Automated backups8.524 Ratings00 Ratings
Database security provisions8.773 Ratings00 Ratings
Monitoring and metrics8.475 Ratings00 Ratings
Automatic host deployment8.013 Ratings00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
9.5
50 Ratings
15% above category average
Pixel Perfect reports00 Ratings9.543 Ratings
Customizable dashboards00 Ratings9.450 Ratings
Report Formatting Templates00 Ratings9.548 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
9.6
50 Ratings
18% above category average
Drill-down analysis00 Ratings9.545 Ratings
Formatting capabilities00 Ratings9.450 Ratings
Integration with R or other statistical packages00 Ratings9.939 Ratings
Report sharing and collaboration00 Ratings9.550 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
9.6
49 Ratings
15% above category average
Publish to Web00 Ratings9.545 Ratings
Publish to PDF00 Ratings9.545 Ratings
Report Versioning00 Ratings9.541 Ratings
Report Delivery Scheduling00 Ratings9.544 Ratings
Delivery to Remote Servers00 Ratings9.924 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
9.6
49 Ratings
18% above category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings9.548 Ratings
Location Analytics / Geographic Visualization00 Ratings9.545 Ratings
Predictive Analytics00 Ratings9.942 Ratings
Pattern Recognition and Data Mining00 Ratings9.53 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
9.5
50 Ratings
11% above category average
Multi-User Support (named login)00 Ratings9.547 Ratings
Role-Based Security Model00 Ratings9.544 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings9.547 Ratings
Report-Level Access Control00 Ratings9.53 Ratings
Single Sign-On (SSO)00 Ratings9.529 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
8.8
39 Ratings
13% above category average
Responsive Design for Web Access00 Ratings8.936 Ratings
Mobile Application00 Ratings8.027 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings10.036 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Google BigQuery
-
Ratings
Microsoft BI (MSBI)
9.4
22 Ratings
19% above category average
REST API00 Ratings9.919 Ratings
Javascript API00 Ratings9.919 Ratings
iFrames00 Ratings9.918 Ratings
Java API00 Ratings9.917 Ratings
Themeable User Interface (UI)00 Ratings9.519 Ratings
Customizable Platform (Open Source)00 Ratings6.918 Ratings
Best Alternatives
Google BigQueryMicrosoft BI (MSBI)
Small Businesses
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Yellowfin
Yellowfin
Score 8.7 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
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Kyvos Semantic Layer
Kyvos Semantic Layer
Score 9.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryMicrosoft BI (MSBI)
Likelihood to Recommend
8.8
(77 ratings)
9.9
(73 ratings)
Likelihood to Renew
8.1
(5 ratings)
8.0
(25 ratings)
Usability
7.0
(6 ratings)
10.0
(15 ratings)
Availability
7.3
(1 ratings)
9.5
(2 ratings)
Performance
6.4
(1 ratings)
7.0
(2 ratings)
Support Rating
5.4
(11 ratings)
8.9
(15 ratings)
In-Person Training
-
(0 ratings)
6.9
(3 ratings)
Online Training
-
(0 ratings)
8.5
(2 ratings)
Implementation Rating
-
(0 ratings)
9.6
(7 ratings)
Configurability
6.4
(1 ratings)
10.0
(2 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
-
(0 ratings)
Ease of integration
7.3
(1 ratings)
-
(0 ratings)
Product Scalability
7.3
(1 ratings)
-
(0 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryMicrosoft BI (MSBI)
Likelihood to Recommend
Google
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
Read full review
Microsoft
Microsoft BI is well suited for Stream analytics, easy data integration, report creation and UI/UX designs (limited but what all available are great ones) Microsoft BI may be less appropriate for handling huge number of datasets and difficult queries. It may also be difficult for a company with heavy data.
Read full review
Pros
Google
  • Realtime integration with Google Sheets.
  • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
  • Seamless integration with other GCP products.
  • A simple pipeline might look like this:-
  • GForms -> GSheets -> BigQuery -> Looker
  • It all links up really well and with ease.
  • One instance holds many projects.
  • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Read full review
Microsoft
  • Comparatively easy to use compared to other data analytics solutions, collaborating with other colleagues on data work is simple.
  • Using Visual Studio for database, ETL, reporting, and analytics development save time and money.
  • Transfer of data from one application to another via Excel and comparison of data attributes between applications
  • Dashboard functionality, as well as Python support, are available, allowing you to add additional charts and graphs.
Read full review
Cons
Google
  • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
  • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
  • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
Read full review
Microsoft
  • The race to perfect gathering of Non-Traditional datasets is on-going; with Microsoft arguably not the leader of the pack in this category.
  • Licensing options for PowerBI visualizations may be a factor. I.e. if you need to implement B2C PowerBI visualizations, the cost is considerably high especially for startups.
  • Some clients are still resistant putting their data on the cloud, which restricts lots of functionality to Power BI.
Read full review
Likelihood to Renew
Google
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.
Read full review
Microsoft
Microsoft BI is fundamental to our suite of BI applications. That being said, Northcraft Analytics is focused on delighting our customers, so if the underlying factors of our decision change, we would choose to re-write our BI applications on a different stack. Luckily, mathematics are the fundamental IP of our technology... and is portable across all BI platforms for the foreseeable future.
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Usability
Google
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
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Microsoft
The Microsoft BI tools have great usability for both developers and end users alike. For developers familiar with Visual Studio, there is little learning curve. For those not, the single Visual Studio IDE means not having to learn separate tools for each component. For end-users, the web interface for SSRS is simple to navigate with intuitive controls. For ad-hoc analysis, Excel can connect directly to SSAS and provide a pivot table like experience which is familiar to many users. For database development, there is beginning to be some confusion, as there are now three tool choices (VS, SSMS, Azure Data Studio) for developers. I would like to see Azure Data Studio become the superset of SSMS and eventually supplant it.
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Reliability and Availability
Google
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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Microsoft
The product has been reliable.
Read full review
Performance
Google
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.
Read full review
Microsoft
SQL Server Reporting Services (SSRS) can drag at times. We created two report servers and placed them under an F5 load balancer. This configuration has worked well. We have seen sluggish performance at times due to the Windows Firewall.
Read full review
Support Rating
Google
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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Microsoft
While support from Microsoft isn't necessarily always best of breed, you're also not paying the price for premium support that you would on other platforms. The strength of the stack is in the ecosystem that surrounds it. In contrast to other products, there are hundreds, even thousands of bloggers that post daily as well as vibrant user communities that surround the tool. I've had much better luck finding help with SQL Server related issues than I have with any other product, but that help doesn't always come directly from Microsoft.
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In-Person Training
Google
No answers on this topic
Microsoft
This training was more directed toward what the product was capable of rather than actual programming.
Read full review
Online Training
Google
No answers on this topic
Microsoft
I have used on-line training from Microsoft and from Pragmatic Works. I would recommend Pragmatic Works as the best way to get up to speed quickly, and then use the Microsoft on-line training to deep dive into specific features that you need to get depth with.
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Implementation Rating
Google
No answers on this topic
Microsoft
We are a consulting firm and as such our best resources are always billing on client projects. Our internal implementation has weaknesses, but that's true for any company like ours. My rating is based on the product's ease of implementation.
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Alternatives Considered
Google
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
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Microsoft
We have used the built in ConnectWise Manager reports and custom reports. The reports provide static data. PowerBI shows us live data we can drill down into and easily adjust parameters. It's much more useful than a static PDF report.
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Microsoft
No answers on this topic
Scalability
Google
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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Microsoft
No answers on this topic
Professional Services
Google
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
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Microsoft
No answers on this topic
Return on Investment
Google
  • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
  • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
  • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
Read full review
Microsoft
  • As a SaaS provider we see being able to provide self-service BI to our client users as a competitive advantage. In fact the MSSQL enabled BI is a contributing factor to many winning RFPs we have done for prospective client organisations.
  • However MSSQL BI requires extensive knowledge and skills to design and develop data warehouses & data models as a foundation to support business analysts and users to interrogate data effectively and efficiently. Often times we find having strong in-house MSSQL expertise is a bless.
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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.