Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.7 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)
Looker
Score 8.3 out of 10
N/A
Looker is a BI application with an analytics-oriented application server that sits on top of relational data stores. It includes an end-user interface for exploring data, a reusable development paradigm for data discovery, and an API for supporting data in other systems.N/A
Pricing
Google BigQueryLooker
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 BigQueryLooker
Free Trial
YesYes
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeRequired
Additional DetailsMust contact sales team for pricing.
More Pricing Information
Community Pulse
Google BigQueryLooker
Considered Both Products
Google BigQuery
Chose Google BigQuery
Suits well for Business Intellegence and vizualization with Looker. Cloud storage options and seamless integration with Google online products.
Chose Google BigQuery
Google BigQuery seemlessly integrates with all the Google services. In Looker Studio you directly have a connector for Google BigQuery which can help to create dashboards in few clicks.
For automating some stored procedures we have used Cloud Functions which are triggered by a …
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
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
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 …
Chose Google BigQuery
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.
Chose Google BigQuery
Main reason is how it integrates directly with the google ecosystem which really facilitates the automatization proceses for the whole company. This ensures that sales and all the other departments have the correct information on a daily bases with a ease of use with day to day …
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
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
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
I've used Domo while working for an advertising agency and the functionality was way worse and the user interface was not near as good.
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
Cost is the important factor for us compared with all of the other tools Google BigQuery stands top among all of them which charges very minimal charges for storage against all the apps that we have liked the most additionally, we can do query on our data, and can build …
Looker
Chose Looker
The biggest advantage that Looker Studio has is that it's really easy to use and to distribute to other use. You can have a really complex report set up in a couple of minutes with an extract data source that enables Looker to update really fast.
Chose Looker
Looker is less complex to use and links directly to google suite which we use across the business and personally think is a better user experience than microsoft.
Chose Looker
The learning curve for Tableau Cloud was too steep for our team. After watching a couple of YouTube videos, anyone can begin connecting data sources and creating reports with Looker. Looker is also free with Google Workspace, making the decision between Looker and Tableau a …
Chose Looker
Google Looker Studio is an online tool for converting data into customizable, informative reports and dashboards. It is a free tool that turns performance data into informative, easy-to-read, easy-to-share, and fully customizable dashboards & reports. Google Looker Studio turns …
Chose Looker
Mixpanel is the behemoth of the industry, but given Looker's acquisition by Google, it now offers better value in my opinion
Chose Looker
Tableau is also a great BI tool, but it felt a lot less flexible to me in terms of customization of data. As a visual platform, Tabluea is incredible; it can produce unbelievably rich visualizations and dashboards. It's also easier to get set up on Tableau too, but ultimately …
Features
Google BigQueryLooker
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
80 Ratings
1% below category average
Looker
-
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 metrics8.375 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
Looker
7.5
132 Ratings
9% below category average
Pixel Perfect reports00 Ratings6.7108 Ratings
Customizable dashboards00 Ratings8.3131 Ratings
Report Formatting Templates00 Ratings7.5113 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Looker
7.1
130 Ratings
12% below category average
Drill-down analysis00 Ratings6.9126 Ratings
Formatting capabilities00 Ratings6.8128 Ratings
Integration with R or other statistical packages00 Ratings5.954 Ratings
Report sharing and collaboration00 Ratings8.8129 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
Looker
8.0
126 Ratings
3% below category average
Publish to Web00 Ratings7.6104 Ratings
Publish to PDF00 Ratings8.3112 Ratings
Report Versioning00 Ratings7.782 Ratings
Report Delivery Scheduling00 Ratings8.4108 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
Looker
6.5
126 Ratings
21% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings7.6122 Ratings
Location Analytics / Geographic Visualization00 Ratings7.3108 Ratings
Predictive Analytics00 Ratings4.66 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
Looker
7.4
126 Ratings
14% below category average
Multi-User Support (named login)00 Ratings7.8118 Ratings
Role-Based Security Model00 Ratings7.2103 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings7.3120 Ratings
Report-Level Access Control00 Ratings7.358 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
Looker
5.5
94 Ratings
34% below category average
Responsive Design for Web Access00 Ratings5.890 Ratings
Mobile Application00 Ratings5.01 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings6.184 Ratings
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Google BigQueryLooker
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
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User Ratings
Google BigQueryLooker
Likelihood to Recommend
8.8
(78 ratings)
8.3
(132 ratings)
Likelihood to Renew
8.1
(5 ratings)
9.3
(8 ratings)
Usability
7.1
(6 ratings)
8.8
(12 ratings)
Availability
7.3
(1 ratings)
10.0
(1 ratings)
Performance
6.4
(1 ratings)
6.0
(1 ratings)
Support Rating
5.6
(11 ratings)
8.8
(14 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
Configurability
6.4
(1 ratings)
10.0
(1 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
10.0
(1 ratings)
Ease of integration
7.3
(1 ratings)
10.0
(1 ratings)
Product Scalability
7.3
(1 ratings)
10.0
(1 ratings)
Professional Services
8.2
(2 ratings)
10.0
(1 ratings)
Vendor post-sale
-
(0 ratings)
10.0
(1 ratings)
Vendor pre-sale
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Google BigQueryLooker
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
Google
When data drives potential for new orders, Looker earns its place in our tech stack. If, on the other hand, we are hoping for pipeline generation, Looker is useful if you are willing to repeatedly go check customer utilizations .... it is not appropriate if you are hoping to automate data analysis for this purpose.
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
Google
  • Show visited pages - sessions, pageviews - which programs are viewed the most.
  • Displays session source/medium views to see where users are coming from.
  • It shows the video titles, URLs, and event counts so we can monitor the performance of our videos.
  • It gives a graphic face to the numbers, such as using bar charts, pie graphs, and other charts to show user trends or which channels are driving engagement.
  • Our clients like to see the top pages visited for a month.
  • I like the drop-and-drag approach, and building charts is a little easier than it was before.
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
Google
  • Documentation is scarce, and very difficult to find when you need it.
  • Pricing is unclear, particularly as you look to scale your reports across the business.
  • Data from other sources is not represented in the system as well as first party Google services.
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
Google
I give it this rating because it deems as effective, I am able to complete majority of my tasks using this app. It is very helpful when analyzing the data provided and shown in the app and it's just overall a great app for Operational use, despite the small hiccups it has (live data).
Read full review
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.
Read full review
Google
Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.
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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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Google
No objections
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
Google
Somehow resources heavy, both on server and client. I recommned at least 50Mbs data rate and high performance desktop comouter to be abke to run comolex tasks and configure larger amount of data. On the other hand, the client does not need to worry when viewing, the performance is usually ok
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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Google
Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.
Read full review
Implementation Rating
Google
No answers on this topic
Google
Very satisfied, easy to implement
Read full review
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.
Read full review
Google
Looker Studio, you can easily report on data from various sources without programming. Looker Studio is available at no charge for creators and report viewers. Enterprise customers who upgrade to Looker Studio Pro will receive support and expanded administrative features, including team content management. So it's good.
Read full review
Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review
Google
Perfect price to performance
Read full review
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.
Read full review
Google
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.
Read full review
Google
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
Google
  • Looker has a poignant impact on our business's ROI objectives. As an advertising exchange we have specific goals for daily requests and fill, and having premade Looks to monitor this is an integral piece of our operational capability
  • To facilitate an efficient monthly billing cycle in our organization, Looker is essential to track estimated revenue and impression delivery by publisher. Without the Looks we have set up, we would spend considerably more time and effort segmenting revenue by vertical.
  • Looker's unique value proposition is making analytical tools more digestible to people without conventional analytical experience. Other competing tools like Tableau require considerably more training and context to successfully use, and the ability to easily plot different visualizations is one of its greatest selling points.
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.

Looker Screenshots

Screenshot of a Looker dashboard with a geo chart.