Exploring Five Great Alternatives to Looker and LookML

Making data accessible can be a big hurdle to overcome. Luckily, Panoply makes it easy. No other platform makes syncing and storing data - without days of engineering work - so easy.  However, once the data is accessible, a new challenge arises: making it easy for business users to ask and answer their own questions.

If you’re familiar with Looker, you’ve probably heard of the term “self-service analytics.” Looker coined this term to describe how it enables ordinary, non-technical business users to perform meaningful analyses without the aid of a data analyst. 

What makes Looker so special in this regard? The answer is its semantic layer. 

The term semantic layer feels  vague, but you can think of it as a tool that “maps complex data into familiar business terms such as product, customer, or revenue to offer a unified, consolidated view of data across the organization.”  Looker has a semantic layer built into the tool, and it's coded in a proprietary markup language called LookML.

It’s the LookML semantic layer and the features built on top of LookML that make Looker such a unique and powerful tool. So, in exploring the alternatives to Looker, we must start with a semantic layer as “table stakes” for comparison. But before we get into LookML, let’s take a look at the other features that make Looker unique.

Looker’s core features

Since its origin as a simple way to pull data tables from a database to a full-fledged enterprise BI tool, Looker has built a full suite of BI tool capabilities. These are a few of the highlights. And yes, I’ll save LookML for later.

• Scalability and Cloud-Native: Looker is designed to be a cloud-native platform, which means it can scale with your organization's growth. It can leverage the power and flexibility of cloud computing infrastructure.
• Data Governance and Security: Looker offers robust data governance features, allowing organizations to control and manage access to data, apply security policies, and track data usage. This is crucial for maintaining data integrity and compliance.
• Collaboration Features: Looker includes collaboration tools that enable users to share insights, reports, and dashboards with colleagues just by sharing unique URLs.
• Community and Marketplace: Looker has an active community of users and a marketplace where users can share and integrate custom-built data models, visualizations, and integrations.
• Machine Learning Integration: Looker has integrated machine learning capabilities, which enable users to leverage predictive analytics and advanced data modeling for more sophisticated analysis.
• Support and Training: Looker offers a range of training and support options to help users make the most of the platform, including documentation, webinars, and a support community. Unfortunately, the tool dropped its chat support feature after the Google acquisition.

• Embedded analytics: Looker offers two options for embedding visualizations in internal and external applications: secure iframes and a robust API for flexible visualizations.

The LookML semantic layer

At the heart of Looker's distinctiveness is the LookML semantic layer.  In Google’s own words, “LookML stands for Looker Modeling Language; it's the language used in Looker to create semantic data models. You can use LookML to describe dimensions, aggregates, calculations, and data relationships in your SQL database.”

The modeling layer allows users to define data models and transformations in an intuitive and structured manner, making it easy to ensure metric consistency across numerous business units and users. LookML is the foundation on which its self-service capabilities are built.

• Abstraction and reusability: LookML code is modular and reusable. This reusability enhances productivity and maintainability by reducing redundancy and maintaining consistency across multiple reports and dashboards.

• Data governance:  Administrators can define fine-grained access provisioning, ensuring users can access the right data while maintaining data integrity and compliance. This level of control sets Looker apart, particularly in organizations with strict data governance requirements.

• Version control:  LookML models can be version-controlled, enabling organizations to track changes over time and maintain a history of data models. This feature is valuable for auditing and ensuring data integrity.

The one downside of the LookML semantic layer is that it takes a significant amount of planning and foresight to get right—and it’s pretty hard to get right without some prior experience. That’s why it is common to work with a Looker consultant when rolling out the semantic layer.

The top 5 alternatives to Looker and LookML

Considering the semantic layer, the presentation layer, and the package as a whole, here are the top 5 alternatives to Looker.

Omni Analytics

Omni Analytics was founded by two former Looker employees to pick up where Looker left off. Omni is also built atop a modeling layer. They promise that the tool will allow you to “focus on answering data questions, and Omni will build a data model as you go, allowing for reusability, governance, and performance optimization.”

Unlike Looker, Omni offers a no-code modeling layer with an option to edit the code directly. And frankly, the ease of development and the magic behind the scenes is remarkable. The semantic layer offers a Git integration and many of the governance capabilities of LookML.

As for visuals, similar to Looker, Omni started with a focus on modeling first, and viz came second. That said, the tool stands on the shoulders of a giant by employing Vega open-source data visualizations. And when it comes time to present, Omni shares many of its URL-based sharing features with Looker.

Zenlytic

Zenlytic also started with a modeling layer but with the intention of heading in a very different direction. Zenlytic’s philosophy is that a well-defined semantic layer is the perfect foundation for an easily accessible LLM-based chat interface. They promise that working with their natural language chatbot named Zoe will allow you to “explore, pivot, and ask your data questions like you’re talking to an analyst.”

Zenlytic’s AI capabilities also extend to the semantic layer, claiming to be the world's first self-deploying semantic layer. In other words, upon connecting a database, Zenlytic will browse the tables and columns in your database and come up with a reasonable first pass of the metrics you’d be interested in analyzing. Of course, they offer complete control over the YAML-based semantic layer as well.

To continue the ease-of-use theme, Zenlytic also offers premium support that they call Data-Team-as-a-Service. This is an excellent solution for early-stage companies that need analytics but aren’t ready to invest in full-time hires just yet. Zenlytic’s solution brings in human and AI analysts on demand.

Tableau + LookML

One, albeit pricey, option is to take the best of both worlds. Tableau has long been the leading data visualization tool, providing unmatched visualization flexibility and analytics features. However, because it was borne out of a pre-cloud era, it was not designed to sit atop a cloud data warehouse or take advantage of a semantic layer. 

To close this gap, Google introduced what they’ve called the Looker Modeler. Google claims, “By defining metrics and storing them in Looker Modeler, metrics can be consumed everywhere, including across popular BI tools such as Connected Sheets, Looker Studio, Looker Studio Pro, Microsoft Power BI, Tableau, and ThoughtSpot.” In addition to native integrations with many BI tools, the Looker Modeler offers SQL and API interfaces just as the Looker product does.

I’ll reiterate: this is a pricey option, but if you’re looking for a time-tested one-two punch for enterprise, it’s worth considering.

Your BI tool + dbt Semantic Layer

If you’ve already deployed dbt as a data transformation tool, it’s worth considering dbt’s fully integrated semantic layer with one of their supported BI tools for an experience comparable to Looker’s semantic and presentation layers. Although the integration between the semantic layer and the presentation layer is not as complete as with Looker, there are a few benefits: you’ll be able to manage all your transformations and metrics in one place, and you’ll be able to pick and choose the tools on top that suite your needs.

This integration works with a handful of BI tools, but they all work generally the same. The tools all surface metadata and relationships about the metrics and dimensions in your semantic layer so that you can analyze the data without writing custom SQL code.

PowerBI

What list of alternatives would be complete without listing PowerBI? While the tool doesn’t feature a semantic layer per se, the combination of the data modeling capabilities provided by Power Query and the Data Analysis Expressions (DAX) language, along with PowerBI’s enterprise governance features, offers a reasonable alternative for a price tag that is likely to be much less expensive than Looker. If you’re IT or data infrastructure is on Microsoft, you’re undoubtedly aware of PowerBI, and probably worth considering.

Step up to self-serve analytics

With so many options in the wake of Looker, there’s no doubt it led to a revolution in business intelligence. That said, it's not a perfect fit for every business in every case. Cost, complexity, and familiarity are serious considerations in finding the right solution. If you need help, I’ve also rounded up a list of five great consultancies that specialize in Looker. And if you have any questions, feel free to reach me on Linkedin.

No matter which way you go, deploying a semantic layer will certainly be a great investment toward future speed and scale. Good luck!