Holistics vs Looker

Looker (acquired by Google) is a powerful business intelligence and analytics software that enables self service analytics.

This article is written for experienced Looker professionals to know the similarities and differences between Holistics and Looker, and shares the use-cases the two tools best fit.

High-level Comparison

Aspect	Looker	Holistics
General
High-level Approach	Take similar approach: 1. Analysts define analytics logic centrally 2. Business users perform self-service data exploration
Evaluation Process	Sales-led; Compulsory to speak to Sales	Self-service trial; Speak to sales for further questions.
Deployment	Cloud hosted or self-hosted	Cloud hosted
Compliance	GDPR; SOC2; HIPAA	GDPR; SOC2 Type II
Pricing	Opaque (need to speak to sales); Require high upfront commitments; Annual payment	Transparent; Can start small ($150/mo), scale up as usage grows; Pay monthly & annually
Support	Dedicated Customer Love team	Highly praised
Learning Curve	Steep. Require analysts to learn LookML, and work with code interface.	Gentler. Provide both visual interface (GUI) and code interface (AML)
Modeling Layer
Central Modeling Layer to define Business Logic	Yes, with LookML.	Yes. With AML.
GUI to model data	No. Only code-based interface.	Yes. Dual interface (both code-based and GUI-based).
Prebuilt Data Models	Yes, with Looker Blocks	No.
Integration with data transformation tool	No	Yes. Native integration with dbt.
Work with CSV / Google Sheets source	No.	Yes. Native support.
Developer Experience
Code-based approach	Yes (LookML)	Yes (AML)
Version Control with Git	Yes	Yes
API	Yes	Yes
Reporting & Visualization
Self-service Exploration	Yes	Yes
Visualizations	Rich visualization	Rich visualization
Custom Visualizations	Yes, through Looker Marketplace	Yes, through Vega-Lite
Pivot Table	Yes	More advanced, with subgroup calculation
Date Comparison	No native support (Workaround)	Native support with Period-over-Period Comparison
Embedding	Yes	Yes

Holistics and Looker are a very close comparison as both tools take a very similar approach to BI: code-based modeling layer with self-service data exploration.

Both tools are 100% cloud-based, provide a centralized data modeling approach for BI teams, and empower business users who don’t know SQL can do true self-service.

Looker is generally more mature than Holistics in terms of analytics functionality, but Holistics is also catching up fast. You can think about Holistics as a mini-Looker, where you get around 80% of the functionalities for probably 20% of the price.

A downside of Looker is that it requires your data team to learn LookML (which can be expensive to hire or train), while Holistics provides a gentler learning curve towards setting up data modeling.

Trial Experience and Pricing

While both tools are directionally similar, the go-to-market approaches are different, thus what the end customers experience throghout the evaluation process is different.

Looker’s target market are mainly enterprise customers, thus their entry price may not be affordable for some companies. They also do not provide a self-service trial experience, you need to contact a salesperson to even start to try out Looker.

On the other hand, Holistics provides an affordable alternative to Looker for SMBs and mid-markets. It provides a fully self-service trial process. Pricing is also transparent and starts at $100, making it easier to try out.

Data Modeling Layer

Looker and Holistics are similar in that both provide a data modeling layer that translates between business users’ drag-and-drop inputs to a database query.

The data modeling layer not only provides self-service analytics for business users but also provide a centralized management interface for data analysts to ensure that the data exposed is accurate, maintainable and reusable.

Both tools invented their own modeling language. Looker with LookML (Looker Modeling Language) and Holistics with AML (Analytics Modeling Language).

Terminologies: While the approaches are similar, both tools use slightly different terminologies

Looker	Holistics	Purpose
Looker View	Holistics Data Model	A data entity, usually a logical mapping of a database table
Looker Explore	Holistics Dataset	A collection of Looker views / Holistics models. Shared with end users for data exploration purpose.
Looker Persisted Derived Table (PDT)	Holistics Transform Model	Persist results of a SQL query back into data warehouse
Looker Join	Holistics Relationship	Define the join/relationship between 2 views/models

Initial Learning Curve

A key requirement for Looker users is the need to invest time to learn & setup the data modeling step. For Looker, this means learning and preparing the modeling work in LookML first, as an abstraction on top of a SQL database.

Learning LookML is not rocket science but still takes significant effort, thus creating a fairly high barrier of entry. As a result, only analysts who are willing to invest in Looker’s approach would go through the process of learning it.

Holistics’ data modeling, while also code-based, provide a simple, easy to use user interface to perform GUI-based data modeling. This removes the need to learn a new language for initial users. The only requirement is some minimal SQL knowledge to model some advanced use cases.

LookML vs AML

With both Looker and Holistics offering code-based, you can perform data modeling with tons of flexibility. Holistics AML tries to optimize for a better developer experience through auto-complete and instant feedback of errors. Their developer-first approach makes AML much easier to pick up and start developing than LookML. This similarity of the languages between the tool makes it easier for the developer to switch and adopt.

Conclusion

The above comparison shares a few highlights in the similarities between Holistics and Looker. Sign up for a Holistics trial and try it out yourself.