Data visualization allows the brain to process a large amount of data in one glance.
However, data is only helpful if we are able to adequately communicate the information to our teams showing both the big picture and the steps necessary to achieve future goals. This is where tools such as Looker and Tableau come in handy.
There are a number of data visualization tools available, each with varying features. The task of picking which one can be overwhelming. We are taking a closer look at the difference between Looker vs. Tableau to compare which visualization is better for your business objectives.
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Marketers love these two platforms because of the simplicity of use, high standards of customer service, and the beautiful, interactive visualizations they are capable of producing.
The following comparison breaks down the benefits of both BI solutions.
Criteria to Consider When Deciding Between Looker and Tableau
When making the final decision between Looker vs Tableau consider the following:
- Your existing infrastructure
- Company size
- Data analytics needs
- Types of graphs, charts, or storyboards that work best to communicate with your team
- Types of accesses you will need, whether on-site or stored on the platform’s cloud
How to Transfer Data to Looker or Tableau
While Looker, Tableau, and other data visualization tools are great at presenting information in an easy-to-digest way, it can still be a tedious challenge to gather all your data, prepare it, and send it all to your visualization or BI tool.
That's where tools like Improvado step in. The Improvado solution automatically aggregates data from data sources, applies data transformations to prepare the data for visualization, and pushes it to the tool of your choice. It significantly reduces the amount of manual work needed to start visualizing data and minimizes the likelihood of misleading visualization or errors with data harmonization and deduplication features.
Both Looker and Tableau are compatible with Improvado. This means you can rely on Improvado to automatically serve harmonized, high-quality data from your data sources to your preferred visualization and BI tools.
Looker
Looker is a powerful data analytics platform that was acquired by Google in 2019 and is now part of the Google Cloud Platform. The platform helps analysts in data discovery, exploration, and visualization activities.
Looker is a browser-based platform. The data modeling process is based on Looker's unique modeling language, LookML. It's a language for describing dimensions, aggregates, calculations, and data relationships in the SQL database.
Querying is relatively fast once you get used to the language. Besides, Looker provides a turn-key integration with GitHub (and you don't even need to know how to use git itself).
Moreover, the platform provides Looker Blocks which are pre-built data models for common analytics patterns.
Instead of writing queries from scratch, you can use already existing models and modify them according to your needs.
Here are different types of blocks provided by Looker:
- Analytic blocks. This directory contains blocks for different types of analytics.
- Source blocks. Here you can find analytics for third-party sources (Twitter Analytics, YouTube Analytics, Improvado Advertising Analytics) based on data schemas created by Looker's partners.
- Data blocks. Pre-built public data patterns (e.g., weather and demographic data).
- Data tool blocks. Ready-made techniques for specific types of data analysis (e.g. cohort analysis).
- Viz blocks. Custom visualization types that you can use to visualize your query output.
- Embedded blocks. Pre-built blocks that allow you to embed data to custom applications.
Looker doesn't have native support of machine learning algorithms.
However, since Looker is a part of the Google Cloud Platform, it's closely linked with BigQuery. This enables analysts to use BQML (BigQuery Machine Learning) to build and execute Machine Learning models using SQL queries.
Later on, you can visualize the output of models in Looker.
Besides, Looker also works with other AI and machine learning tools from GCP, such as Vertex AI.
In one of the case studies, Google explains how to use BQML and Looker to create the explainable AI for the banking industry. The machine learning model can identify fraudulent transactions based on the transaction amount, time of the day, distance, etc.
| At a glance | What it does |
|---|---|
| BigQuery ML (BQML) | Builds and runs machine learning models with SQL queries, and Looker visualizes the output |
| Vertex AI | Works with Looker to build an interactive machine learning model |
| Google banking case study | Explainable AI that flags fraudulent transactions by amount, time of day and distance |
Another case study by Google shows how to build an interactive machine learning model using Looker and Vertex AI.
Tomasz Mazur, a Web Analytics Director at Challhoub Group, shared his thoughts about Looker in a Measure Slack channel (a closed community for digital marketing analysts).
"I'm running Looker instance for more than 400 users. From my experience.
Upsides:
- It scales really well. If you use a good warehouse, you don't need to build any aggregates, cubes, etc. Tools that use intermediate databases can't run analytics on very big datasets.
- Quite flexible in terms of modeling. Anything you can do with SQL you can do in Looker.
- Good for data governance. The whole workflow is very simple. All of the KPIs in dashboards come from data models. If something is wrong, you change it in one place, and once you push it production, the dashboards get the update KPI, so it's much easier and faster.
- Self-service BI. Looker is more about self-service bi than data visualization, so your users will drag&drop dimensions and measures, LookML will generate a SQL that is executed in your warehouse. Then you select visualization on top. If your data warehouse is fast, they'll get quick results in real-time so they can do a lot on their own.
- Version controlling / git. You can collaborate on projects, so if you have a bigger company, it's kinda must, in my opinion.
- Automated testing, ci/cd. We do quite a lot of that, have a separate beta instance, test your content via API, etc.
- Works in a browser. You don't have to install extra software. You can work on Mac, Linux, etc. Develop code in any tool you want.
- Enterprise features. single sign-on (SSO), identity and access management (IAM), row-level security, etc.
- Good API. You can do quite a lot via API (accessing the data, managing users, instances, testing content, etc.).
- Good community, support, and marketplace. It's not the biggest community, but I've had a good experience so far. In the marketplace, you can find re-usable models, so if you use Shopify, you just copy the model into your repository and get the basic dimensions and KPIs.
Drawbacks:
- You need to invest in a tool and learning. Like it's definitely a large investment, which might be a blocker for startups. I think not a lot of people really know the tool.
- Requires a large team to get the most out of it. Looker is not a one-stop shop. We still model our data in dataflow&dbt and then hit Looker to do the last mile.
- Some development tasks take more time compared to other tools. Tools with legacy architecture that load your data into the intermediate database have some built-in calculations that you can run really quick, like all of "period vs. period comparison", "running sums like a year to date, etc.". In Looker, you need to write that in SQL from scratch. You'll re-use it later, but you have to do the initial work. I prefer it like that because I can control the code of the function but again takes more time.
- Visualizations are not as good as in Tableau. This is not a blocker for us, and there are like 20 visualization types, but for some companies, it's a blocker.
- Vendor lock-in. I guess with every tool there is this risk that switching to the other tool might be painful but yeah. Like going from something like Looker to a different architecture might be complicated.
- It might be overkill. I wouldn't use it for a small team, where I don't need the whole governance, version control, and scalability. There is a cost associated with it and a learning curve to implement, so yeah, just not for every size. I mean, I would still use it for medium-sized and large companies, but not for a "budget" or "super small" project"."
Looker Pricing:
Looker's prices adjust according to your needs and your team size. That's why, the company doesn't reveal the price on its website.
Google Cloud's Looker pricing page lists the Standard, Enterprise and Embed platform editions with Call sales as the cost, and each platform includes 10 Standard Users and 2 Developer Users.
Another good thing to know is that if you have an investor from Google, you can get Google platform credits, which can be used to pay for Looker.
The pricing differs from company to company, so it's better to contact the company directly.
How to Transfer Marketing Data to Looker
One of the main questions remains unanswered: how to transfer data from your data sources to Looker without spending half of the workweek?
- You can connect Improvado and Looker In under two minutes
- You can use Improvado's pre-built data extraction templates Connect any available data source
- After you configure all required connections The data will automatically flow to your warehouse
- Build comprehensive dashboards in Looker You can use Improvado's set of marketing dashboard templates
Of course, you can do everything manually, but with so many companies implementing a modern data stack, you'll be wasting your competitive advantage and time on manual data management.
Companies that are first to get insights, are first to get more revenue and boost their growth.
A reliable ETL platform is an essential part of a healthy analytics process. Instead of decluttering metrics aggregated from tens of sources and trying to organize them in a data warehouse, data scientists can focus on the actual analysis.
Here's how we approach this problem at Improvado. Improvado is an ETL platform for sales and marketing data that allows users to merge insights from sources in minutes.
The data pipeline normalizes gathered insights, loads all data to the user's data warehouse (such as BigQuery, S3, Snowflake, etc.), and automatically transfers data to Looker.
Here's how you can connect Improvado and Looker:
From this point on, you can use Improvado's pre-built data extraction templates that allow you to connect any available data source by making just a few clicks.
After you configure all required connections, the data will automatically flow to your warehouse. Mind that if you use BigQuery, you can also use its machine learning functionalities to create more complex queries and create more detailed dashboards.
The last thing you need is to build comprehensive dashboards in Looker. You can use Improvado's set of marketing dashboard templates and customize them in any way you need.
Tableau
If you are looking to transform data into beautiful visualizations with insights that make an impact then Tableau may be the BI tool you need.
As a visualization tool, Tableau offers instantaneous and insightful visualizations. The platform provides drag-and-drop functionalities that significantly support the dashboard creation process.
The scope of possible visualizations is large, ranging from basic bar charts to complex maps, scatter plots, and more. For example, here's the expenses audit dashboard created by Luke Donovan.
There are different ways how you can use Tableau:
- On-premise Tableau Server hosted on the company's personal computer (PC).
- Tableau Server deployed in the public cloud.
- Tableau Online is fully hosted on Tableau's servers.
Apart from visualization, Tableau took an interesting approach to data exploration. Tableau used to offer Ask Data, a feature that built data views from questions typed in plain language. Tableau retired Ask Data in Tableau Cloud in February 2024 and in Tableau Server version 2024.2, and says it is developing a replacement with an improved interface.
| At a glance | What it does |
|---|---|
| Ask Data | Retired in Tableau Cloud in February 2024 and in Tableau Server version 2024.2 |
| Aible integration | Builds predictive models with a cloud-based AI solution |
| Python integration | Runs predictive analytics but needs deep engineering expertise |
Ask Data status from Tableau's help page, checked 27 October 2026.
As for machine learning functionalities, Tableau falls behind Looker in this matter. Even though Tableau has incorporated some AI features for data analysis, it doesn't have the same capabilities as Looker in a bundle with BigQuery.
However, third-party integrations can slightly improve the situation. Tableau integrates with Aible, a cloud-based AI solution. With its help, you can build predictive models. Another way to run predictive analytics is by integrating Python. However, it requires your analysts to have deep expertise in engineering, which is often a luxury that companies can't afford.
Experienced analysts and data scientists aren't always happy with Tableau despite broad visualization capabilities.
Some specialists consider Tableau a weak tool in terms of data exploration.
Others believe that it's only good for very basic analytical tasks.
Opinions differ. You can try free trials of both tools and decide which will be better for your specific tasks.
Here's a list of benefits and drawbacks according to our personal experience.
Pros:
- Beautiful, interactive visualizations that are easy to interpret
- Additional visualization features include storyboarding and Spatial File Connector (which extracts geospatial data)
- Intuitive analysis with a point-and-click user interface that provides the big picture of company trends and future possibilities
- Fully customizable dashboards
- Easy embedding
- Single sign-on
- Intuitive and user-friendly
- Online analytical processing functionalities provide access to databases and web-based analysis
- Allows business users to convert reports into different file formats and share the analytical findings
- Integrates with a large number of data sources
Cons:
- Sometimes large data files can take several minutes to load
- Graphs are somewhat limited at times
- Insufficient capabilities in terms of machine learning
- The dashboard can be slow at times.
Tableau Pricing:
Tableau offers two different sets of prices. The first set of prices is for installation on-premise or on a public cloud. Both sets are charged per user per month, so check Tableau's pricing page for current rates.
Two price sets
On-premise or public cloud, and Tableau's own servers
Tableau article
Call sales
Platform cost for the Standard, Enterprise and Embed editions
Looker Google Cloud Looker pricing page
Looker from Google Cloud's Looker pricing page, checked 27 October 2026.
Tableau also offers a separate set of prices for using the platform deployed on Tableau's servers.
How to Transfer Marketing Data to Tableau
Despite all the differences, there's one absolutely similar characteristic for both Tableau and Looker. You need to supply your dashboards with fresh data continuously.
There's no better solution for this issue than the ETL platform. A well-built data pipeline will help you get data from all your data sources to Tableau and a data warehouse of your choice.
Improvado can help you transfer data from marketing and sales sources right to your Tableau dashboard.
First, you'll have to connect the ETL platform to the BI tool, which normally takes less than a couple of minutes.
Then, you have to establish a connection for all of your data sources to load it in a unified data warehouse. As in the case of Looker, you might use BigQuery, Snowflake, and 10+ more data warehouses.
However, querying data in Google BigQuery (GBQ) might be too expensive since the platform charges you for each processed data row. That's why, Improvado offers a ClickHouse-based managed data warehouse, where you can execute as many queries as you want without burning a hole in your pocket.
Learn how Improvado uses ClickHouse to process queries faster and cheaper compared to BigQuery
With all connections configured and insights prepared you can start visualizing. Build the dashboards and Improvado will automatically fill them with data.
Visualization, Analytics, Online Analytical Processing and Integration
Visualization
It’s hard to make a clear call into which platform has better visualization capabilities since they both do it so well. Tableau uses structured and unstructured data to create visualizations and has the added features of storyboarding and Spatial File Connector. Improvado provides a number of Marketing Dashboard templates that are available as Looker Blocks.
| Aspect | Looker | Tableau |
|---|---|---|
| Visualization | Custom visualization types | Storyboarding and Spatial File Connector |
| Analytics | Looker Blocks | Forecasting, etc. |
| OLAP | Custom solutions | OLAP cubes |
| Integration | More integrations with third-party software | Large number of data sources |
| Machine learning | BigQuery Machine Learning | Aible |
Analytics
Looker is better when it comes to analytics. While Tableau is excellent with analytics Looker has the advantage due to its platform-exclusive analytics function, Looker Blocks. The blocks are pre-built but fully customizable to the user's needs.
As for Tableau, it offers vast opportunities for different data manipulations, SQL queries, forecasting, etc. It’s still a great solution if you plan to work with large datasets, complex data views and integrate third-party tools for your analysis needs.
Online Analytical Processing (OLAP)
Tableau is the clear winner of this category because it works with OLAP cubes, while Looker attempts to work around OLAP. For companies that require the use of OLAP then the choice is clear.
Looker tries to replace conventional OLAP functionalities with custom solutions for data examination. However, if OLAP is irreplaceable for your analysis, Tableau suits you the best.
Integration
While you can use or build custom API connections to various outside systems and platforms, most people opt to utilize Improvado as a way to automate the process of extracting and aggregating data into their tool.
As for the comparison of Looker vs. Tableau, Looker provides more integrations with third-party software and it’s easier to connect it with other solutions compared to Tableau.
More research to decide which tool to choose
If you still need more research to decide which tool to choose, this might help you:
How to Choose Viualization Platform [CheatSheet]
Get free consulting from experts who have done it before
The Final Decision
The final decision of whether to use Tableau vs Looker rests on your specific needs. After taking into consideration your marketing and data needs, company size, data analysis, and preference for visualization which of the two BI tools (business intelligence) will you choose?
| If you need | Pick |
|---|---|
| Pre-built data models for common analytics patterns | Looker |
| More integrations with third-party software | Looker |
| If you plan to work with large datasets, complex data views | Tableau |
| If OLAP is irreplaceable for your analysis | Tableau |
Whether you settle on Tableau vs Looker the good news is that both are fully compatible with Improvado.
FAQ
What is Looker good for?
Looker is a business intelligence and visualization tool that helps companies create illustrative charts and diagrams for a better understanding of their efforts. From the user’s standpoint, the app provides a clear and straightforward interface, works in a browser tab, and improves team productivity when it comes to dashboarding.
How much does Looker cost?
Looker doesn’t disclose pricing information on their website. To get the price tailored to your requirements, you should contact the company directly. Google Cloud's Looker pricing page lists the Standard, Enterprise and Embed platform editions with Call sales as the cost.
Is Looker owned by Google?
Yes, Google acquired Looker in February 2020.
Is Looker the same as Tableau?
Even though both of these tools have the same purpose, they still have some differences. It’s hard to compare Looker vs. Tableau because the real benefits of these solutions rely heavily on your business requirements. Due to Looker’s simplicity, the software doesn’t require a desktop version. However, it lacks flexibility in several points.
Tableau is a solution with more advanced features. Still, the large data files may take up to several minutes to load, and massive dashboards can be slow at times.
How is Looker different from Tableau?
Tableau utilizes both structured and unstructured data to visualize data and has the added features of storyboarding and Spatial File Connector. Looker creates customized visuals and also allows you to choose from a library full of blocks with pre-made dashboard and visualization templates.
How Improvado integrates Tableau or Looker with my data ecosystem?
Improvado moves marketing data from all your current data sources to load it into the data warehouse. From there, normalized and cleansed insights are sent to Looker or Tableau. With the help of prebuilt marketing dashboards, analysts get detailed charts and graphs in seconds. Thus, they can focus on the analysis instead of routine data manipulations and data cleansing.
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