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 Server
Score 8.0 out of 10
N/A
Tableau Server allows Tableau Desktop users to publish dashboards to a central server to be shared across their organizations. The product is designed to facilitate collaboration across the organization. It can be deployed on a server in the data center, or it can be deployed on a public cloud.
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 …
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 …
Other locally hosted solutions are capable of providing the required level of performance, but the administration requirements are significantly more involved than with BigQuery. Additionally, there are capacity and availability concerns with locally hosted platforms that are a …
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).
Whole funnel and specific channel performance from upper to lower funnel metrics. The ability to view full channel performance for some time, such as weekly, monthly, or quarterly, has truly been monumental in how my team optimizes specific channels and campaigns. Daily performance tracking is a bit overwhelming, with load times and having to refresh specific live views over time. It can be challenging to do so at times, as extensive dashboards take much longer to load.
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.
It's good at doing what it is designed for: accessing visualizations without having to download and open a workbook in Tableau Desktop. The latter would be a very inefficient method for sharing our metrics, so I am glad that we have Tableau Server to serve this function.
Publishing to Tableau Server is quick and easy. Just a few clicks from Tableau Desktop and a few seconds of publishing through an average speed network, and the new visualizations are live!
Seeing details on who has viewed the visualization and when. This is something particularly useful to me for trying to drive adoption of some new pages, so I really appreciate the granularity provided in Tableau Server
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.
Tableau Server has had some issue handling some of our larger data sets. Our extract refreshes fail intermittently with no obvious error that we can fix
Tableau Server has been hard to work with before they launched their new Rest API, which is also a little tricky to work with
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.
It simply is used all the time by more and more people. Migrating to something else would involve lots of work and lots of training. The renewal fee being fair, it simply isn't worth migrating to a different tool for now.
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.
Tableau Server takes training and experience in order to unlock the application's full potential. This is best handled by a qualified data scientist or data analytics manager. Tableau user interface layout, nomenclature, and command structure take time and training to become proficient with. Integration and connectivity require proper IT developer support.
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.
Our instance of Tableau Server was hosted on premises (I believe all instances are) so if there were any outages it was normally due to scheduled maintenance on our end. If the Tableau server ever went down, a quick restart solved most issues
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.
While there are definitely cases where a user can do things that will make a particular worksheet or dashboard run slowly, overall the performance is extremely fast. The user experience of exploratory analysis particularly shines, there's nothing out there with the polish of Tableau.
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.
We have consistently had highly satisfactory results every time we've reached out for help. Our contractor, used for Tableau server maintenance and dashboard development is very technically skilled. When he hits a roadblock on how to do something with Tableau, the support staff have provided timely and useful guidance. He frequently compares it to Cognos and says that while Cognos has capabilities Tableau doesn't, the bottom line value for us is a no-brainer
In our case, they hired a private third party consultant to train our dept. It was extremely boring and felt like it dragged on. Everything I learned was self taught so I was not really paying attention. But I do think that you can easily spend a week on the tool and go over every nook and cranny. We only had the consultant in for a day or two.
The Tableau website is full of videos that you can follow at your own pace. As a very small company with a Tableau install, access to these free resources was incredibly useful to allowing me to implement Tableau to its potential in a reasonable and proportionate manner.
Implementation was over the phone with the vendor, and did not go particularly well. Again, think this was our fault as our integration and IT oversight was poor, and we made errors. Would they have happened had a vendor been onsite? Not sure, probably not, but we probably wouldn't have paid for that either
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.
Today, if my shop is largely Microsoft-centric, I would be hard pressed to choose a product other than Power BI. Tableau was the visualization leader for years, but Microsoft has caught up with them in many areas, and surpassed them in some. Its ability to source, transform, and model data is superior to Tableau. Tableau still has the lead in some visualizations, but Power BI's rise is evidenced by its ever-increasing position in the leadership section of the Gartner Magic Quadrant.
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.
Tableau does take dedicated FTE to create and analyze the data. It's too complex (and powerful) a product not to have someone dedicated to developing with it.
There are some significant setup for the server product.
Once sever setup is complete, it's largely "fire and forget" until an update is necessary. The server update process is cumbersome.
