Data Visualization Tools We Love to Hate: Redash vs Looker Studio vs Looker

Here at Arpeely, we love data. We love using it, creating it, and analyzing it. In this world of data analytics and business intelligence, the right visualization tool can make all the difference between insightful decision-making and missed opportunities and, therefore, are in everyday use.

Due to their immense value and hours spent using them, these tools spark VERY strong opinions and feelings from their users. Love and hate, in this case, aren’t exaggerations.

Don’t believe us? Try asking your analysts, BI-developers, and business analysts. I assure you they all feel strongly about this matter.

Tools of the Trade

At Arpeely, we have Noach - a BI developer. He is looking for scalability, consistency, data governance (control over the tables and metrics being used), flexibility, quick loading times, and availability for non-technical users. There’s also myself, an ex-data analyst (turned Product Markting Manager), who is now focused on quick delivery time, freedom of creation and rapid visualization. We decided to call a truce and to review our currently most used visualization tools from both our perspectives.

Redash , Looker, and Looker Studio (formerly Google Data Studio) are the three most widely used platforms at Arpeely at the moment. While all three tools promise to simplify the process of transforming data into actionable insights, they come with their own set of advantages and challenges. By comparing the three, Noach and I will try to offer a comprehensive overview of what makes these platforms beloved by some and hated by others and maybe even manage to convince each other to like, or at least accept, the other’s favorite.

Redash

Redash is an open-source visualization toolkit that enables extremely quick development of dashboards with easy access to technical and non-technical users. It is known for its simplicity and SQL-based interface.

Redash strengths:

• Built with SQL in mind; in most cases you can write queries in their natural syntax

• Easy templating (ability to change query or dashboard via global parameters and filters. Read more here) , supports procedures (T-SQL). What you write is what you get.

• Enables parametrized queries and parametrized dashboards

• Allows you to move quickly between different visualizations of each query when in Edit mode

• Lightweight

• Quick and easy to set up and create dashboards

• Minimal data governance

• Has an alert function that integrates with Slack and Email

• Open source and free

Redash drawbacks:

• Requires a solid knowledge of SQL; every visualization requires a query to run in the background and someone has to write it

• A limited and outdated visualization library

• Struggles with large or complex datasets

• Limited design of the structure and organization of data across the entire system to ensure consistency (data modeling)

• Doesn’t have version control or content validators

• Minimal data Governance 

• What you write is what you get; it can contain errors, bugs and more.

• Creates a lot of redundant SQL

  
  

Redash is a short term solution, not a scalable and limited tool. However it’s great for ad-hoc queries for people with a solid SQL knowledge who are looking for a way to visualize their data save and share it. Here at Arpeely, Redash is LOVED by data analysts who need a lightweight tool for querying and visualization.

Looker Studio (formerly Google Data Studio)

Looker Studio is a free, user-friendly BI tool that is designed for quick, interactive report creation rather than complex data modeling or governance.

Looker Studio strengths:

• A user friendly UI with a drag-and-drop interface; relatively easy to use, even for analysts without coding skills. Moreover, the component creation UI is simple and resembles those used in Excel\Google sheets.

• Limited visualizations, but still better than Redash’s

• Has version control

• Lower skill requirements (no actual need for SQL or data modeling)

Looker Studio drawbacks:

• Unlike Looker, Looker Studio lacks a developer-friendly API, making it less suitable for advanced customization and automation

• May struggle with large or complex datasets

• Limited data modeling and automations

  
  

Looker Studio is suitable for small organizations that need a quick, basic, easy-to-use reporting and dashboard tool but don’t require data modeling or governance. Looker Studio is perhaps the most comfortable tool for non-technical users.

Looker

Looker, a full-fledged BI and data modeling platform, is widely regarded for its structured data layer (LookML) which helps structure and manage data logic in a maintainable way. Looker makes data analysis accessible for various teams.

Looker strengths:

• Handles large data volumes with centralized data governance, ensuring consistency and accuracy across analytics and teams

• LookML language allows BI developers to build reusable data models and define consistent metrics across the organization

• Reduces redundant SQL and ensures that reporting logic is standardized

• Advanced and customized visualization: you can enter costume HTML or write your own visualization if you want (tedious, but possible)

• Has an API access for the looker platform (list all dashboards, manage users and permissions and more)

• Content validators easily check what your changes affect

• Has version control

• Merged results (you can combine results of 2 or more queries with similar dimensions to a single result)

• Advanced Automations, such as alerting, report scheduling, and automated workflows by integrations with other tools

  
  

Looker drawbacks:

• Looker is complex, and LookML requires knowledge in data modeling

• Enterprise pricing (you have to go through a sale process)

• Not as easy for quick ad-hoc needs. It does have a SQL IDE, however it is limited to one visualization and is not suitable for long-term use.

• Difficult templating

• Since Looker is an entire BI tool set that allows so much, it can be a little daunting for new users

  
  

Looker is ideal for medium to large organizations that need centralized data governance, robust and scalable data modeling and extensive integration options. Or in other words, it’s great for organizations and BI developers who are looking for the “whole package.”

For us at Arpeely, since we had several tools before joining Looker and for users who are “ad-hoc”er data analysts (and are too lazy to learn something new), it’s a little more difficult and cumbersome.

So… Who won?

You can probably guess which is Noach’s favorite tool and which is mine. As we tried to put our opinions aside, we agreed that choosing the right visualization tool depends largely on your specific needs and understanding of the strengths and limitations of each tool and the stage of your organization and its business needs.

If your organization has many tech-savvy users proficient in SQL and standardized reporting logic who desire freedom to create and deploy quickly, Redash might be ideal for you. Users are familiar with the syntax, as it's the same as their usual querying tool and can easily template their data on each query they write. There's no need to supervise data consistency, so the amount of redundant SQL code is less of an issue.

For those looking to quickly create visualizations and dashboards, also without SQL expertise, Looker Studio might be the right choice. No queries or modeling required—just an email address and a database connection, and you're ready to go.

As your organization grows and data governance becomes crucial, with a need for a single source of truth and access for less technical staff members, or if you're seeking a "complete" BI tool, then Looker might be what you're looking for. While Looker can be complex and requires a deeper learning curve, its powerful data modeling layer and scalability make it ideal for large organizations with complex data needs.

Now in Tabular Format ;)

	Redash	Looker Studio	Looker
Skill Requirements	SQL knowledge	Minimal (drag-and-drop)	SQL + LookML
Visualizations	Basic	Limited	Advanced
Data Modeling	Limited	None	Strong, with LookML
Data Governance	Minimal	Minimal	Robust (role-based, reusable models)
Cost	Free (open-source)	Free	High (licensed)
Ideal Use	Lightweight, ad hoc reporting	Quick Google-based reporting	Structured, scalable BI solutions
Best For	SQL-savvy teams	Quick, basic reporting	Scalable, governed analytics

Peace Amongst the BIs

Even after writing this blog and really trying to like Looker, it will probably never be my go-to, and Noach will never fully approve of my endless list of Redash dashboards.

Yet, maybe, just for a while, we won’t hate the other options, just slightly dislike them, and we will be able to discuss other subjects, such as how do you pronounce the word data. Dàta or datà?