Looker vs. Whaly

What is Looker?

Looker claims to be a business intelligence solution that drives better outcomes through smarter data-driven experiences. Their purpose is to enable organizations to equip their employees, customers, operational workflows, products, and services with timely and trusted data. Originally designed for data engineers, Looker has expanded to focus on self-service capabilities for business users. Looker launched in 2012, and was acquired by Google in 2019.

What is Whaly?

Whaly is an integrated, self-service business intelligence platform that enables business teams and data teams to work better together - streamlining the path from data to decisions. Our mission is to grow data adoption and company-wide trust in data by empowering business teams to access and analyze data, whenever and however they need it. Whaly includes data connection, modeling, and visualization all in one. Our robust modeling layer is also user-friendly enough for data-savvy business users to participate.

TL;DR

A good self-service BI tool should always enable clear, ongoing visibility into data at all times, and should be accessible and beneficial to everyone. These factors are vital for business teams to make informed, timely decisions. As such, things like learning curve, lengthy training time, implementation time, slow performance and load time, and limitations for non-technical business users are red flags.

While Looker is a robust, powerful BI solution that equally plays in the self-service analytics space, there are certain downsides that negatively impact the ongoing accessibility of data for all users. In this comparison, we’re considering the perspective of a SMB / mid-market company that’s less mature in their data journey, with a small data team. The main downsides include LookML proprietary language requirements, slowness, overall learning curve and complexity, and cost.

Whaly, on the other hand, is fast to implement, learn, and load, uses raw SQL with no specific requirements. Plus, we’re significantly cheaper with free viewer licenses.

At a glance

Cautions for Looker

• There’s a super steep learning curve to using Looker, which will slow down the total time you need to get up and running, and for Looker to start adding value.
  - Proprietary language: LookML is their Looker-specific proprietary language that data engineers and analysts must learn to get started, which adds a lot of time and resources to the process of getting set up. It’s unnecessarily time-consuming that a whole new language needs to be mastered before anyone can start using Looker.
  - Roll-out time: 6-18 months including training and implementation. It would take even longer including the time to learn LookML.
  - Set-up: Once training is complete, it could take up to 6 hours to get explorations set up.
• Looker is complex, and is more suited for companies with large data teams. They require you to work like a company that’s mature in their data journey, evident in the technical requirements. If you’re just starting out or have made your first data analyst hires, Looker will be overly complex and difficult to set up.
  - If your first data hires are non-technical data analysts (as opposed to data engineers who can code), there will be a barrier to entry.
  - Oftentimes, the complexity requires that SMB / mid-market companies leverage a consulting firm or third party to help with Looker. In this case, you don’t own the tool and you won’t get the ongoing data visibility with the consulting firm in the middle.
  - Maintenance requires a lot of time and resources on an ongoing basis
• With LookML and heavy coding requirements across many layers (modeling, iteration), Looker is not conducive to collaboration, and creates a divide between the business and data teams. This inevitably results in adoption issues across the company.
• Looker locks you into their own technologies and processes (with LookML), as opposed to adapting into your current ecosystem and workflows.
• Looker is not self-service at every layer and level of the BI.
  - Modeling isn’t self-service for business users, neither are explorations - even adding new dimensions and iterating on a query requires code, which is inaccessible. You would need to go back and forth with your data team, which is inefficient.
• Looker doesn’t allow you to test the data, which means there’s no “security belt” for understanding the outcome before queries are performed.
  - Data might be stale without anyone even knowing it, with no way of communicating this downstream so everyone is aware. This is a surefire way to break trust between data producers and consumers.
• Slow load time. This drags out the amount of time it takes to get data out to people who need it, which means critical business decisions are delayed, or not derived from data.
  - Takes a long time to load large data sets
  - Delay in data reflection in reporting, sometimes up to 24h
  - When the load time is too lengthy, business users try to find their own workarounds like spreadsheets to make data-driven decisions on their own.
• Expensive with enterprise-level pricing. This may be suitable for larger companies, but most SMB and mid-market companies would get a better bang for their buck with a more affordable solution, that is more suited to their needs and where they’re at in their data journey.
  - Training is paid at $20k
  - Pay for Viewer licenses
  - Opaque pricing that has a tendency to go up as you add viewers and use Looker more extensively. Roughly $60k for Enterprise plan for 1 year, but most of the first year would be spent on training and getting up to speed on the platform.

Whaly benefits

• Whaly is much simpler and faster to learn and set up. It doesn’t demand a massive amount of time, resources, or upfront investment to start getting value from your data.
  - No proprietary language requirements. Whaly uses raw SQL for modeling by data-savvy builders, which is an industry standard that many data professionals already know.
  - Faster roll-out time by 5 months minimum (only 1-3 weeks)
  - Once trained, it takes 1 hour to get explorations set up.
  - Implementation takes under an hour
• Whaly bridges the gap between data teams and business teams, providing them with the same framework of communication. Whaly enables collaboration and streamlines the path from data to decisions.
• Whaly adapts to your current data team size / status on data journey - not the other way around.
• Whaly is self-service beyond the surface level. Whaly’s easy drag & drop interface empowers non-technical business teams to do more with the data.
  - Data-savvy business users can even participate in our modeling layer
  - Non-technical business users can explore, run queries, and drill down to get deeper insights from the data, autonomously going beyond the dashboards. No need for back and forth with the data teams.
• Whaly has a native integration with dbt, which means data can be tested before materialization, so everyone can be alerted downstream if there are any issues.
• Whaly embraces and relies on the best open source technology. No locking in, just trying to give you the best of the best.
• Our processing power and latest technology make for much faster load time and queries.
• Push and embed features means reporting access is tailored to how you already work.
• Significantly cheaper, with free viewer licenses

Summary

In summary, Whaly offers a self-service BI platform with the same robust and powerful capabilities as Looker, but at a fraction of the cost. Plus, we offer a lighter, faster, and more flexible platform that scales with fast-growing companies like you, enabling ongoing collaboration and trust between data teams and business teams. Our SQL-based modeling layer also makes us more accessible and truly self-service at every level. Lastly, you need data-driven insights today, so why wait 18 months?