19.12.2022 | Nikolay Valiotti

Looker: Overview, Features, Benefits

Looker Overview

Looker is a frontrunner among business intelligence tools, perfectly suited to exploring data of any size and performing on-demand analytics. If you haven’t used a tool of the same caliber, it can be a sharp departure from how you normally approach analytics, but for the better.

This article will be a comprehensive introduction to Looker, a discussion on how it can benefit your organization, a tutorial on how to leverage Looker’s modern analytics platform, and more.

What Is Looker?


Looker is a business intelligence and big data analytics solution that helps organizations pull useful insights from their data.

Users can choose between customer-hosted (self-service) and Looker-hosted deployments. The self-hosted option offers complete control over infrastructure administration, albeit at a higher initial cost and with hands-on maintenance. The Looker-hosted option is not a bespoke model but frees you from active management and updates.

Looker was acquired by Google in 2019 and became part of the Google Cloud Platform in 2020. Existing customers and partners at the time continued to support the tool, so the list of integrations only grew from there.

The pricing model is quote-based.

Traditional vs. Self-Service BI

Looker, like all self-service BI tools, brings analytics closer to business users. Instead of requiring a complex IT environment, Looker sits on top of your SQL database and uses built-in connectors.

It usually takes developers/engineers to handle the initial integration (into existing business processes and tools in the Google ecosystem) and the modeling configuration. But then, non-technical users can pull the insights they need without assistance from the IT department. That ultimately creates a collaborative environment for different stakeholders, once impossible for day-to-day activities.

Looker Features

​​Looker breaks down barriers to insights through the following:


Looker Blocks are prebuilt data models (or pieces of code). By reusing the work of others, users can accelerate the development of analytics, insights, workflows, and applications.

To use a block, be it interactive visualizations or pre-modeled external data, go to Looker Marketplace and customize your chosen block to your specifications.


Looker Components are prebuilt pieces of user interface code. Not only do they simplify development, but they also minimize maintenance and overhead and enhance data experiences.

There are also components for rich filter functionality in apps and embedded dashboards: sliders, tag lists, radio buttons, etc.


Looker Alerts are tied to configurable actions. Users can specify the conditions for data and receive an alert if the conditions are met or exceeded. Looker will automatically check data at the required time frequencies.

Extension Framework

The Looker Extension Framework is a fully hosted platform that takes care of certain aspects of web development, such as authentication, access control, API access, and more.

The goal is the same⁠—simplification of processes. In this case, it’s building custom JavaScript data applications.

Mobile App

Looker’s native app is built for on-the-go access to data. It also allows users to seamlessly switch from desktop to mobile (with limited functionality) and share content.

Multicloud Support

Looker runs on Microsoft Azure, Amazon Web Services, and Google Cloud. With 60+ database dialects and 60+ SQL dialects, Looker also connects to your data stored in on-premises systems.

Instructions are available for 45+ SQL dialects, from Google BigQuery data warehouse to Oracle data lake architecture.

Installation and Deployment

As mentioned, there are two architecture solutions: customer-hosted and self-service. There are different installation and deployment steps involved, so let’s go through them separately.

Customer-Hosted Instance

To install Looker for a customer-hosted deployment, you need to:

  1. Add Looker to your server.
  2. Configure the startup settings.
  3. Install and configure your SSL certificate to have proper HTTPS.
  4. Enable port forwarding to get a cleaner URL.
  5. Contact Looker Support to get access to your instance.
  6. Set up application monitoring.
  7. Create backups.
  8. Connect to specific outbound ports (depending on which Looker tools and services you use).
  9. Install Chromium rendering software.
  10. Configure the sign-in options.

Looker-Hosted Instance

With this option, Looker provides an instance of the application in a shared virtual private cloud. As you’ll see, getting started with a looker-hosted instance is much easier:

  1. Enable secure database access for your instance.
  2. Configure your database.
  3. Create a new database connection.
  4. Test your connectivity with Telnet.
  5. Configure the sign-in options.

Integrations: Connectors and Automation

Looker can be part of this popular modern data stack combination:

  • Snowflake as a data warehouse
  • DBT as an ETL/ELT tool
  • Looker as a data visualization tool

Of course, dbt with Snowflake is not the limit for Looker. You can browse applications, blocks, and custom plug-ins on the Marketplace Directory. Here are a couple of examples:

Data Dictionary

Data Dictionary

Force-Directed Graph

Force-Directed Graph

It’s important to note that some integrations are free and installed with a Git URL. But some are paid; for example, the Hubspot Segment integration costs $99/month for a once-a-day refresh.

Dashboard Prep: Exploring Data

Explores are starting points for all queries, each built to cover a particular subject area. For example, a business with an e-commerce model can select Explores like Orders, Order Items, Products, and Users to get data on and answer questions about each category.

Here is how you can query the Order Items Explore:

Order Items Explore

Some Explores have a Quick Start analysis option, which allows you to quickly populate fields in modeled queries. The prebuilt analysis options for an Order Items Explore are Order count by month, CA order count by month, and Order count by state by month.

Adding Your Sources

Another key moment is source integration. You will likely want to populate your dashboards with data imported from different connected platforms. For this, you need to add resources to the tool:

  1. From the start page, click Create at the top left.
  2. Click Data Source.
  3. Choose your sources in the Connectors panel.

How to Create User-Defined Dashboards

User-defined dashboards are created and edited by most business users and Looker developers. The functionality is available in the Looker UI, but before you can begin, you must have the following permissions:

  • Manage Access, Edit for the folder where the dashboard will be placed
  • save_content and see_user_dashboards

Keep in mind that you can no longer create legacy dashboards from scratch (in Looker 7.18 and up). Instructions below explain how to manage the new experience. But if you specifically need legacy dashboards, you can revert a regular dashboard to a legacy one (provided the Looker admin has enabled the feature).

Creating Dashboards From Folders

This is the standard, most common way to create a dashboard (rather similar to creating a Looker report).

Creating Dashboards

  1. Open the folder where you want to place the dashboard.
  2. Click the New button, which is at the top right of the screen.
  3. Click Dashboard.

3.Click Dashboard

  1. In the window that opens, type the name of the dashboard.
  2. Click Create Dashboard.

These simple actions will create a new blank dashboard, which you can customize as you want.

Creating Dashboards From Looks or Explores

This process takes slightly longer because it has more inputs right away.  The Look or Explore will be saved as a query tile on the dashboard.

Creating Dashboards From Looks or Explores

  1. Open the Look or Explore you want to create a dashboard from.
  2. Open the menu and click Save.
  3. In the submenu, click As new dashboard.

As new dashboard

  1. In the new window, type the name of the new dashboard (the Settings tab).
  2. In the same window, select the folder where it will be located.

5.In the same window

  1. Go to the Filters tab and select the filters (you can add tile filters later).
  2. Click Save.

Adding Tiles to Dashboards

Now, you can add tiles to the dashboard. The first one will take up the entire width of the dashboard, and all other ones will be automatically resized, but you can change the size, as well as edit the name, the data visualization within it, or the Looks. The rows of tiles are added as necessary.

There are three types of tiles:

  • Query tiles (based on an independent query, helps avoid cluttering)
  • Look-linked tiles (optimal if you want to create and test a query in one dashboard but use it in multiple ones)
  • Text tiles (look like headings and descriptions)

To add a visualization, you need to:

  1. Click Add at the top left and choose Visualization.
  2. Select Explore.

2.Select Explore

  1. In the new Explore window, name your query (name of the tile), add filters, configure your visualization, and click Run.
  2. Click Save.

After you create the tikes, you can still add filters for all tiles or select tiles. New filters will narrow down the results for data, but each filter must show at least one query tile or Look-linked tile; otherwise, it won’t be added to the dashboard.

In the blue toolbar, you can configure dashboard settings. For example, set the timezone for each tile. And you can change the dashboard description.

How to Create LookML Dashboards

LookML dashboards are created by a select group of developers and are written and edited in a YAML-based dashboard file. These are the permissions required:

  • see_lookml_dashboards
  • develop

Also, any data used in the dashboard requires access to the LookML models for it.

Creating LookML Files

Here is how you create a dashboard file with the extension .dashboard.lookml:

Creating LookML Files

  1. Enable the Development Mode.
  2. Locate your project in the Develop menu.
  3. Click the Plus icon in the file browser (if you want the dashboard to be located outside existing project folders) or three dots folder options (if you want to place the dashboard in one of the existing project folders) and click Create Dashboard.

Create Dashboard

  1. Type the name of the dashboard file in the new window.
  2. Click Create.

Editing LookML Files

A new LookML dashboard file is already pre-populated with several basic dashboard (the layout, preferred viewers, tile sizes, etc. ) parameters and element parameters (the appearance and function of dashboard tiles, text, and buttons). But you can edit the file within the IDE, an integrated development environment for LookML developers, as needed.

You can also add visualizations by building queries on the Explore page and pasting the LookML under the elements parameter. To add filters, you can hard-code them into the dashboard elements or create the filters that users interact with and apply them using the listen element parameter.

Considerations When Building Looker Dashboards

A dashboard can’t serve its purpose unless it’s designed with effective performance in mind. The first thing you should focus on is building performant queries. Check out the backend tips for optimizing the underlying SQL query performance here.

  • But not all performance issues are SQL-related. For memory-intensive components that slow down the Looker performance, we have the following tips:
  • Go easy on the elements. But there is no recommended number of elements because a single element can impact memory consumption differently.
  • Use the data your actually need. A dashboard that needs to return many thousands of data points will consume significant resources.
  • Reduce the number of results within an element. A high volume of rows and columns will have a similar slow-down effect.
  • Be strategic about your settings. For example, avoid setting your autorefresh faster than your ETL process.
  • Cache results of prior queries. You can use a datagroup to quickly return identical queries.
  • Minimize post-query processing.

As you continue building, refresh the page to confirm every element is working as intended and performance is not lagging. And when you are finished, test the performance and troubleshoot as needed. If you’re unsure what to fix or how to do it, Looker Support can lend a hand.

Common Uses

Common Uses

Businesses, from startups to enterprises, use Looker to uncover value in their data. What’s more, the level of customization and the amount of support make the solution suitable for practically any industry or company department.

Tailored to Industry

The official website points out several industries with the highest Looker adoption:

  • eCommerce⁠—used to analyze sales cycles, product availability, pain points on the path to purchase, customer and shopping cart behavior, and top-performing products
  • Media⁠—to get a holistic view of content and maximize the media team’s client campaigns
  • Adtech⁠—to understand engagement across multiple outlets and improve returns on ad space
  • SaaS⁠—to monitor performance and accelerate decision-making on product features
  • Healthcare⁠—for better access to medical records and improved patient outcomes (supports HIPAA compliance)
  • Gaming⁠—to determine player behaviors and level up monetization
  • Fintech⁠—to spot fraudulent behavior and keep sensitive data safe and compliant
  • Retail⁠—to centralize customer-centric retail data and manage supply chains

Tailored to Department

Looker’s real-time insights can be useful in departments such as:

  • Marketing: to answer questions like “how is this campaign performing?” or “what influences key user behaviors in A/B tests?”
  • Product: “how can engineers make better products?”, “what are the biggest friction points?”
  • Sales: “which actions boost conversions?”, “how can we optimize resource allocation based on customer attributes?”
  • Customer service: “how can we respond quickly?”, “how can we prevent customer complaints?”
  • Web analytics: “how can we map out user interactions?”, “what do clicks say about customer preferences?”
  • Human resources: “how do we increase employee retention?”, “how can we increase the quality of new hires?”

Pros and Cons of Looker

Before summarizing what we’ve learned about Looker, let’s list its strengths and weaknesses.

  • High-performing, scalable analytics on a near-real-time basis
  • High level of customization
  • Multiple options for data delivery
  • Well-suited for dashboard collaboration
  • Advanced data governance
  • Great customer support and quick response times
  • Resources for non-technical users
  • Meant for larger-scale deployment
  • Can be slow in high traffic
  • Uses its own proprietary language (LookML)
  • Limited visualization
  • Expensive and non-transparent pricing (reportedly $2,900/month per company)

In Closing

Looker is one of the most robust and innovative pieces of software for data analytics. But it’s not perfect for every business user and has a lot of powerful competitors. Consider your business needs and whether Looker justifies its expense before committing to it.

Hopefully, this guide helps you understand the basics of Looker’s features, common uses, strengths and weaknesses, and the overall operations of Looker. If you want to integrate it into your business processes, you’ll definitely need to gain a more in-depth knowledge of the tool. Many users report a steep learning curve.

You need this guide, because it includes:

  • evolution of data stack
  • reasons why many well-known tools do not meet the challenges of the new world
  • vital structure of modern data stack
  • the newest tools for every part of modern data stack

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