Looker

What differentiates Looker from other similar products?

Our organization's management and strategy team needs to review the daily data on clicks, impressions, and buyer engagement to keep track of the GMV impact. We use Looker Studio to obtain this information on a daily basis.

What is your favorite aspect of this product?

The interface is user-friendly and flexible, allowing us to choose from a wide range of fonts and colors. Additionally, there are several options for presenting data visually, and it is easy to update information from various sources.

What do you dislike most about this product?

One of the drawbacks of Looker Studio is its limited capability to handle large datasets efficiently. When working with extensive data, the platform tends to slow down, which negatively affects productivity during crucial hours. As a performance marketer, I frequently need to work with substantial amounts of data, and this performance limitation significantly impacts my workflow.

What recommendations would you give to someone considering this product?

If you are dealing with large datasets, Looker can be a great tool to handle it. However, it is worth noting that if you have complex data models or require advanced analytics, you may want to test Looker's capabilities with your specific data. By doing so, you can ensure that it can deliver the expected results and meet your business needs. It is always a good practice to test any tool's capabilities before implementing it into your workflow to optimize your data analysis process.

What differentiates Looker from other similar products?

Looker stands out for its LookML semantic modeling layer, which centralizes business logic and ensures consistent metrics across all dashboards. Unlike traditional BI tools focused on visualizations, Looker emphasizes data governance, scalability, and integration with modern data warehouses like BigQuery and Snowflake, making it ideal for enterprise-grade analytics and embedded insights.

What is your favorite aspect of this product?

My favorite aspect of Looker is its LookML modeling layer, which allows teams to define business metrics and data relationships in one governed place. This ensures consistent, reusable, and transparent analytics across the organization. It bridges the gap between data engineers and business users, enabling accurate, scalable, and collaborative decision-making.

What do you dislike most about this product?

What I dislike most about Looker is its steep learning curve and reliance on LookML coding, which can be challenging for non-technical users. Setting up models and optimizing queries requires data-engineering knowledge, and dashboard performance depends heavily on the underlying data warehouse. This can make initial setup and adoption slower compared to more drag-and-drop BI tools.

What recommendations would you give to someone considering this product?

I recommend Looker for teams with a strong data foundation and a need for governed, scalable analytics. Invest time in learning LookML and building a solid data model upfront—it pays off with consistent metrics and reusable insights. Ensure your data warehouse is optimized for performance, and provide training for both technical and business users to fully leverage Looker’s capabilities.

What differentiates Looker from other similar products?

As per my experience, the Looker modeling layer and centralized governance keep metrics consistent across teams, with solid API integration and embedding for productized analytics. The visualization is easy to understand for every user.

What is your favorite aspect of this product?

I used this tool a lot of time in my day-to-day activity, but my favorite part is Clean, fast dashboards backed by a semantic model—stakeholders trust the numbers, and I can schedule and share effortlessly.

What do you dislike most about this product?

Steep learning curve for Looker and sometimes rigid permissions; pricing scales quickly for lots of viewer seats.

What recommendations would you give to someone considering this product?

Run a real POC with your production data, define 5–10 core metrics in LookML, test row-level permissions, and validate total cost (including viewer growth).