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Google Data Studio VS Looker: An In-Depth Comparison

by Amirali Marvian Mashhad

Google Data Studio VS Looker: An In-Depth Comparison

As the variety of business intelligence (BI) tools increases in the market, choosing the right platform that suits your budget, business objectives and your team’s technical expertise, becomes more difficult. Each brand offers different value propositions and features and this makes the decision making process tricky. BI tools are for unifying and visualizing data and are normally used by different departments and stakeholders across the company. So the BI platform you choose must be suitable to use in different teams in the organization to help them answer business questions and measure their KPIs.

In this article, we are going to focus on Looker vs Data Studio and compare these two platforms from 5 main criteria so you can make a better decision about which of these BI tools is a better fit to fulfill your business objectives. The comparison is going to evolve around the following aspects: Price, Data Source Integration, Deployment, Data Modeling and Analytics Capabilities. Finally, We will end this article with a conclusion.

Before we get into the comparison details, let’s just get to know Data Studio and Looker as some readers might not be familiar with them.

Google Data Studio is  part of the Google Marketing Platform (GMP) that allows users to connect to data sources and create sharable interactive dashboards and engaging reports that can help business users make better decisions.

Looker is an enterprise level BI and visualization tool recently bought over by Google that offers customized visuals, collaborative dashboards and advanced analytics. Before we get into details, let’s have look at the table below for a high level feature comparison between Data Studio and Looker:

Summarized Comparison Table

Features 

Data Studio 

Looker

Price

Free

From USD3K per month

Data Source Integration

SQL & non-SQL 

SQL 

Data Source Merging

Yes

Yes 

Fully-Hosted on Cloud

Yes

Yes

On-Prem Deployment

No

Yes

Linux / Windows Deployment

No

No

Basic Data Modeling

Yes

Yes

Advanced Data Modeling

No

Yes

Predictive Analytics

No

Yes

Programming Languages Support

Yes

Yes

Embedded Analytics

Yes

Yes

Free Version

Yes

No

SQL Support

Yes

Yes

API Access

Yes

Yes

Custom Visualization 

Yes

Yes


1) Price

What is the Data Studio pricing structure?

As of this time, Google Data Studio is free of charge for all users. All you need is a Google account. To get started, just go to datastudio.google.com where you can begin creating your first dashboard.

What is the Looker pricing structure?

Looker is generally an expensive, enterprise-graded solution. It's designed for enterprises with advanced data requirements and use cases. The cost of Looker will be somewhere between USD 3000 to USD 5000 per month for 10 users. This is just an estimate as Looker offers tailored pricing based on organization’s requirements.

For more details about Looker pricing, check out this article.


2) Data Source Integration

How does data integration work in Data Studio?

  • Data Source Types
    Since data studio is a Google product, there are various native integrations to sources such as Google Analytics, Google Ads, Google Sheets, BigQuery, Cloud Storage and many more. In addition, there are over 400 partner connectors available that enable you to connect to data sources such as Adobe Analytics, JASON and Facebook. One cool thing about data studio connectors is that you can build your own if you can’t find the suitable one for your use case. Take note that unlike many other BI tools that require a SQL data source to be connected, Google Data Studio can connect to both SQL and noSQL based data sources.
  • Merging Data Sources
    Some of you may ask: Can I merge various data sources in a dashboard to create tables and graphs? The answer is yes. In Data Studio, there is an option called data blending which is basically a left outer join that lets you create charts based on multiple data sources. You may blend up to 4 data sources. To join the data, each data source in the blend must share a set of one or more dimensions, known as a join key. It is important to note that this operation is only at report level and you won’t see them in your data sources home page.

How does data integration work in Looker?

  • Data Source Types
    You need a SQL database to integrate your data source with Looker. This means that whatever format your raw data is in, you need to transfer it to a SQL database and only then Looker will be able to read the data. Currently Looker supports more than 50 databases including Google BigQuery, Amazon Redshift and Snowflake.
  • Merging Data Sources
    In Looker you can use Explores to merge data sources together. An Explore is the starting point for queries. Using the Merged Results feature, you can create a query from an Explore and then add queries from other Explores to display the merged results in a single table. From there, you can examine the data, pivot fields, and create visualizations.

3) Deployment

What deployment options does Data Studio offer?

Currently Data Studio only offers browser-based (fully-hosted on cloud) access to the platform. This means that you need to be connected to the internet and open Data Studio with a Google account. The dashboards you create will be saved and can be accessed whenever you are online.

What deployment options does Looker offer

Looker is a browser-based BI tool that is fully hosted on cloud. This means that users do not require to go through the installation, configuration and maintenance of Looker application. However, for the companies that have tight security measures, they can host Looker instances on-prem.


4) Data Modeling

Before we get into analyzing data modeling capabilities for Looker and Data Studio, It is crucial to understand what data modeling is and why it is important.

What is data modeling?

In short, Data modeling is the process of analyzing and defining all the different data your business collects and produces, as well as the relationships between those bits of data. There are broadly 3 types of data models: conceptual, logical and physical.

Why is data modeling important?

Data modeling is crucial because it prepares data for analysis. In data modeling stage you get to define relationships between each dataset so that you can use different datasets together when performing analysis. In addition, you can create new fields within the existing datasets to be used in your visualization and analysis to enhance insights and find opportunities for improving business processes that otherwise wouldn’t be discovered.

Furthermore, data modeling creates a structure so the IT team and business team collaborate more effectively. It also reduces errors (and error-prone redundant data entry), while improving data integrity.

So if the above is something you want to achieve with your data, It is important to choose a BI tool that can support advanced data modeling.

How does Data Studio support data modeling?

Data Studio allows you to customize your fields in your data source in order to support your business use case. Once you successfully connect your data source to Data Studio, you’ll see a list of fields. Fields consist of dimensions and metrics. Each field has a name, data type and default aggregation that are derived from the underlying data set.

  • Change Field Details
    You can change the name, data type and aggregation type of each field in order to customize it for your business objective. For example, you can use a dimension as a metric.
  • Calculated Fields
    In addition to above, you can create calculated fields that let you create new metrics and dimensions derived from your data.calculated field is a formula that performs some action on one or more other fields in your data source. Calculated fields can perform arithmetic and math, manipulate text, date, and geographic information, and use branching logic to evaluate your data and return different results. The output of a calculated field can then be displayed for every row of data in charts that include that field.

How does Looker support data modeling?

Looker has its own language for data modeling called LookML. LookML is a language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. Looker uses a model written in LookML to construct SQL queries against a particular database.

By default, Looker generates a basic LookML model once you connect Looker to a SQL database. You can then use LookML to further develop your data model as required. There are also 100+ pre-built LookML modeling patterns (Looker Blocks) to help accelerate development.


5) Analytics Capabilities

What are the analytics capabilities of Data Studio?

  • Predictive / Advanced Analytics
    Unlike many other BI tools in the market, Google Data Studio does not have any significant predictive analytics capabilities which allows you to do things like classification regression or time series forecasts. So in a sense, lacking this feature could make Data Studio not a suitable tool for those companies with advanced use cases that include predictions and forecasts.
  • Supporting Programming Languages
    Data studio does not natively support languages such as Python and R. However, there are 3rd-party tools such as Panoply that allows users to integrate R and Python with Data Studio. In addition, you can use a Google sheet that is updated using Python and connect that to Data Studio. This solution is more suitable for smaller datasets. For larger datasets you can use BigQuery.
  • Embedded Analytics
    You can embed a report in any site or app that supports the HTML iframe tag. The iframe code includes a link to your report, and is automatically generated by Data Studio. No knowledge of HTML is required. The embedded report appears in view mode (Viewers can’t edit the report).

What are the analytics capabilities of Looker?

  • Predictive / Advanced Analytics
    In Looker, users can find and install machine learning models from the Looker marketplace for various use cases including classification, regression, and time series forecasting models, among others. Once the ML model is applied and result is produced, users then can use the data to create relative tables or charts and export to other platforms such as Google Analytics for data activation.
  • Supporting Programming Languages
    Looker has a variety of programming language SDKs that utilizes Looker’s API.

    Some languages such as Ruby, Python and TypeScript are directly supported by Looker and some others like Kotlin, Swift and R are supported by community,
  • Embedded Analytics
    Looker offers embedded analytics options which is part of the SaaS product that you subscribe to. When you want to create an embedded analytics solution using Looker, there are a few strategies that you can employ in order to increase the off of success:
  1. Create a few engaging and easy to understand dashboard and expose it to your customers as part of the customer portal through Looker’s SSO embedding. The goal here is to get customer feedback so you know where you should invest time and resources next. The initial investment should be in engaging with your customers after you release these dashboards
  2. Add new functionality and increase the user base as you solicit your customer’s feedback.
  3. Use Looker’s API as a backend engine to customize application experience based on what your customers want.

Conclusion

Both Data Studio and Looker are Google’s products and at their core, both offer collaborative dashboards and visualizations. Data Studio satisfies the basic needs of organizations that just need a BI tool to create interactive dashboards. Looker on the other hand offers more than the ability to create interactive dashboards. It is built for enterprises that want to have a centralized source of truth, create advanced data models and deploy machine learning models. Looker can also be used as a customer data platform (CDP) where you create a complete profile of your customers using both PII and non-PII data from offline and online sources.

Furthermore, since Looker is a Google product, the manipulated data can be imported back to platforms such as Google Analytics 360 which is Google's web analytics tool for further analysis and ad targeting. Working with Looker requires broad  knowledge of SQL so that you can take advantage of LookML for data modeling and defining schemas. So if you can afford Looker, have the technical knowledge to set it up and there are advanced use cases that you want to experiment with, Looker is your best choice.


Frequently Asked Questions

1) Is Looker  better than Google Data Studio?

It really depends on your use case. If your data source is within Google (e.g. Google Analytics) Stack and you just want a free BI tool that everyone in your organization can access and create interactive charts or tables, Google Data Studio is for you. If you need something more advanced to support your custom data modeling and more custom visualization options with ability to support predictive analytics, then Looker is a more suitable choice.

2) Can I use Looker and Data Studio Offline?

For Data Studio, currently the service is all web based and requires an internet connection to work. However you can export data into CSV and Excel now.

If you’re going to run Looker on a network that does not connect to the Internet, you may need to set up a proxy server to communicate with Looker’s license server or use serverless web services that only make web calls, like BigQuery.

3) Are there any hidden / additional fees for Data Studio and Looker?

For Data Studio there is no paywall. All users (in countries in which the product is available) have access to create reports and utilize the full product.

For Looker, there is no hidden costs after you purchase a subscription and all the features are included in your package.

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