Google BigQuery vs. Tableau Server

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.

Incentivized

Tableau Server is well suited for a data warehouse build and handling big data. Tableau data aggregation, transformation, clustering capability is powerful and easy to implement. The choice of charts and visualisation tools is outstanding. Customisation and dynamic data visualisation capability is superb. The user interface takes some time getting used to.

Incentivized

• 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.

Incentivized

• 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

Incentivized

• 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.

Incentivized

• While it took little time for our data analysts to crank out visualizations, it did take some time(longer than I expected) for our technology operations team to configure the server to share the sizes.
• The server update process is rather cumbersome -- requires a full uninstall/re-install.
• Again, while it took our data analysts next to no time to start creating, I've been in other organizations that have struggled with the feature-rich interface and complexity of the Tableau client. So, it requires the right personnel, with dedicated time, to fully leverage the tool.

Incentivized

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.

Incentivized

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.

Incentivized

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

User experience is the most important factor to consider whenever considering capabilities for non-technical business users. If the learning curve is so steep business users must be advanced users to be productive, you hit the wall of diminishing returns, this is exceptionally true when it comes to analyzing data. Transforming data analysts into BI development experts shifts the focus of the analyst from analyzing data to mastering software. Tableau does a masterful job at minimizing the technology and maximizing the users understanding of their data.

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.

Incentivized

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.

Incentivized

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.

Incentivized

I think the folks that work in support are generally pretty good at what they do (when you get them on a WebEx). But the process of reporting issues to them and waiting for a response (via email only) is a hassle. I never understood why you can't just call them up and discuss the issues with them. It would take a handful of email exchanges before they would agree to a WebEx session. That was frustrating.

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 sales consultants do an amazing job of introducing the tool and its capabilities. They are also helpful in explaining the layout of the desktop client and its different functionality. Keep in mind that they use a sample data source (MS Excel) with a very small amount of data to show off what it can do. What you have to remember is that you are buying the tool so that you can connect to large amounts of data (and possibly blend data together from different databases).

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

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.

Incentivized

Looker and Tableau are quite similar products. I think Tableau's ability to view data visually is more comprehensive. The different breakdowns in UTM level versus first touch and last touch are shown in a visual format, making it much easier to view and interpret the results. Tableau also has faster load times compared to Looker for larger datasets.

Incentivized

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.

Incentivized

• 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.

Incentivized

• Tableau Server has had a huge positive impact. It has allowed us to quickly distribute complex dashboards across the company
• Tableau Server has allowed us to easily manage our entire employee base at the company and control the dashboards they view and interact with

Incentivized