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)
Tableau Cloud
Score 8.0 out of 10
N/A
Tableau Cloud (formerly Tableau Online) is a self-service analytics platform that is fully hosted in the cloud. Tableau Cloud enables users to publish dashboards and invite colleagues to explore hidden opportunities with interactive visualizations and accurate data, from any browser or mobile device.
Both Tableau Online and BI solutions provide visualizations. In Power BI we choose the visualization first, then drag the data into it. In Tableau, we select the data and switch between visualizations on the fly. It’s easier to jump between visualizations in Tableau. Power BI …
Features
Google BigQuery
Tableau Cloud
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.4
79 Ratings
2% below category average
Tableau Cloud
-
Ratings
Automatic software patching
8.017 Ratings
00 Ratings
Database scalability
9.078 Ratings
00 Ratings
Automated backups
8.524 Ratings
00 Ratings
Database security provisions
8.772 Ratings
00 Ratings
Monitoring and metrics
8.274 Ratings
00 Ratings
Automatic host deployment
8.013 Ratings
00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Tableau Cloud
7.7
75 Ratings
6% below category average
Pixel Perfect reports
00 Ratings
7.957 Ratings
Customizable dashboards
00 Ratings
8.875 Ratings
Report Formatting Templates
00 Ratings
6.564 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Tableau Cloud
7.7
75 Ratings
4% below category average
Drill-down analysis
00 Ratings
8.575 Ratings
Formatting capabilities
00 Ratings
7.271 Ratings
Integration with R or other statistical packages
00 Ratings
6.548 Ratings
Report sharing and collaboration
00 Ratings
8.573 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
Tableau Cloud
7.6
73 Ratings
8% below category average
Publish to Web
00 Ratings
8.469 Ratings
Publish to PDF
00 Ratings
7.667 Ratings
Report Versioning
00 Ratings
7.656 Ratings
Report Delivery Scheduling
00 Ratings
8.160 Ratings
Delivery to Remote Servers
00 Ratings
6.339 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
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).
If you're using Tableau as the primary BI tool, then Tableau Cloud is well suited to publish and share the results with a wide(r) audience. It is well suited for various degrees of self-service proficiency, from pure consumers of analytical work to more advanced users who can use web editing for smaller or larger adjustments, and even for desktop power users who will publish their work to Tableau Cloud. It has many good ways to organize the content and make it easily accessible via search, favorites, folders, collections ("playlists for your data"), or history ("recents"). It might not be ideally suited if there are many on-prem sources to be used (even though there are options to connect them) or if you have very special requirements regarding custom server setup, which is limited in a shared cloud environment like Tableau Cloud.
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.
Tableau Online is completely cloud based and that's why the reports and dashboards are accessible even on the go. One doesn't always need to access the office laptop to access the reports.
The visualizations are interactive and one can quickly change the level at which they want to view the information. For example, one person might be more interested in looking at the country level performances rather than client level. This is intuitive and one doesn't need to create multiple reports for the same.
The feature to ask questions in plain vanilla English language is great and helpful. For quick adhoc fact checks one can simply type what they are looking for and the Natural Language Programming algorithms under the hood parse the query, interpret it and then fetch the results accordingly in a visual form.
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.
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.
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.
Based on comments from our clients, I awarded it this grade. Non-technical customers frequently compliment us on the ease with which they can utilize Tableau Online. Usability is rarely a source of contention amongst our customers. Few complaints have come from me as a user of our internal products.
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.
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.
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.
I have not had any issues that require customer support from Tableau at this time, which speaks well to Tableau. I have taken an online course with Tableau and it was very professional and well done, so based on that I would assume a similar level of quality for their customer service.
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.
In determining whether to go with Tableau Online versus Alteryx, two important factors stood out in determining our go-to solution. First, while Alteryx is an impressive tool for data cleansing, it did not stack up in terms of data visualization capabilities. Tableau, on the other hand, provided us everything we needed in terms of visualizing our data and analytics. The second factor is cost. Well neither solution would be considered cheap, Tableau was the more cost effective solution for our needs.
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.
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.
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.