Setting up Looker Embedded Analytics Simplified

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Looker Embedded Analytics

Most organizations now make use of custom Web Applications internal to them for managing their business processes. Since the employees who use these applications for executing or initiating various organizational processes need data to plan their future strategies, it is natural for them to expect access to a Business Intelligence tool within such applications.

Embedded Analytics is aimed at bridging this gap. It helps an organization create unified User Experiences for their decision-makers by allowing them to access the internal business process applications and Dashboards from one place. A popular Web-based Business Intelligence platform that is used by a large number of businesses is Looker. It also houses support for Looker Embedded Analytics. Through Looker Embedded Analytics, an organization can bring Looker features to their internal applications, build custom applications using Looker API or even rebrand Looker as their internal application via White Labeling.

This article will provide you with an in-depth understanding of how you can easily set up Looker Embedded Analytics.

Table of Contents

Introduction to Looker

Looker is a Business Intelligence tool that gives organizations the ability to explore data and build visualizations to make data-driven business decisions. Looker is available as a completely managed online solution or as an application that can be deployed On-premise. Looker’s ability to connect to most of the Cloud-based as well as On-premise data sources makes it a favorite among organizations that use a mix of these services. In the case of On-premise data sources, Looker can create a secure channel that ensures Data Security.

Looker is capable of converting Graphical User Interface (GUI) based user input into SQL queries and sending it directly to the database in live mode. It houses numerous built-in visualizations that give users the ability to analyze their data in real-time. Since Looker is always working in live mode with the database, it works in harmony with Cloud-based Data Warehouses like Google BigQuery, Amazon Redshift, or Snowflake, which scale up or down as per requirements to manage the levels of user concurrency and query load.

Looker exposes its analytical features through a proprietary domain-specific language called LookML. LookML is an abstraction over SQL and helps users to accomplish more with a few lines of code. It also supports SQL-based Data Manipulation in case users find it more comfortable. Looker allows integration with the custom applications using Looker Embedded Analytics.

Looker Architecture
Image Source: https://looker.com/platform/overview

Looker houses a Data Modeling layer which is separate from the components that help visualize data. The functionalities offered by this layer can be leveraged by developers to transform data, perform numerous join operations across tables, etc., which can then be used by the business teams to perform the necessary analysis. This functionality is considered to be extremely important as it allows multiple developers to work parallelly on a model and merge their work with Github Sync.

More information on Looker can be found here.

Understanding the Key Features of Looker

The key features of Looker are as follows:

  • Modern Business Intelligence: Since Looker provides access to reliable and up-to-date data, it can be used to create real-time dashboards for an in-depth and consistent analysis resulting in more precise reporting.
  • Custom Applications: Looker gives users the ability to create custom applications that can deliver unique data experiences suited to each business. Looker also houses numerous embedded analytics solutions that provide users with the data they need to perform all necessary tasks.
  • Advanced Integrations: Looker supports seamless integration with Google BigQuery, Snowflake, AWS Redshift along with 50+ SQL dialects. This gives users the ability to link with multiple databases, avoid database lock-in, and maintain multi-cloud data environments. 
  • Advanced Hosting: Looker supports hosting on public Cloud-based platforms such as Google Cloud Platform (GCP) or Amazon Web Services (AWS) along with other multi-cloud and hybrid environments.
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Hevo is a No-code Data Pipeline that offers a fully-managed solution to set up data integration from 150+ data sources and will let you directly load data to a Data Warehouse, a Business Intelligence tool such as Looker, or the destination of your choice. It will automate your data flow in minutes without the user having to write any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully-automated solution to manage data in real-time and always have analysis-ready data allowing you to focus solely on setting up your Looker Embedded Analytics.

Let’s Look at Some Salient Features of Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. 
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

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Understanding Looker Embedded Analytics

Looker Embedded Analytics can be implemented using one of the four following ways:

  • Private Embedding: By providing access to Looker through an internal application by a separate Looker login.
  • Public Embedding: By enabling access to Looker without a separate Looker sign-in.
  • Custom Applications: By building custom applications using Looker APIs.
  • White Labeling: By creating a rebranded Looker platform for your organization.

It can be observed that the last two methods for setting up Looker Embedded Analytics are a bit more complex and cannot be executed without a dedicated development team. Embedding Looker Analytics features into your applications through Public or Private Embedding is fairly simple and can be accomplished quickly. 

Steps to Set up Looker Embedded Analytics

The basic premise of setting up Looker Embedded Analytics is to have access to a custom application. This example will be making use of a simple HTML page as the custom application. The code for the HTML page is as follows:

<!DOCTYPE html>
<html>
<head>
<title>View Customer Complaints</title>
</head>
<body>
<h1>Customer Complaint Dashboard</h1>
<h3>Top Issues</h3>
<div id=”demo-dashboard-area”>
<div id = “dashboard”>
</div>
</div>
</body>
</html>

The div tag marked as demo-dashboard-area is where you will try to set up Looker Embedded Analytics. 

Looker Embedded Analytics Step 1: Before embedding the URL, you need to let Looker know that such an application will try to access the Looker Dashboard. This is because Looker’s default behavior is to reject all such requests from external applications. To get around this, add the URL of your web application in the Embedded Domain whitelist section. Also, ensure that the Same-Origin Protection in Looker is disabled.

Looker Embedded Domain Whitelist

Looker Embedded Analytics Step 2: Now head to your Looker instance and click on the Dashboard that you want to embed within your Web Application. Copy the URL from the browser top bar and paste it inside the div tag named dashboard as an iframe tag as shown below.

<!DOCTYPE html>
<html>
<head>
<title>View Customer Complaints</title>
</head>
<body>
<h1>Customer Complaint Dashboard</h1>
<h3>Top Issues</h3>
<div id=”demo-dashboard-area”>
<div id = “dashboard”>
<iframe src=”LOOKER_DASHBOARD_URL””></iframe>
</div>
</div>
</body>
</html>

That is the simplest method that can be leveraged to set up Looker Embedded Analytics. A caveat here is that this web application instead of directly displaying the dashboard will first take users to the Looker login page. This is because this process makes use of the Private Embedding method.

If you want to make the User Experience even smoother, you can implement the Public Embedding method, which requires you to use the Looker SDK and a Secret API Key generated from the Looker Admin portal. The idea is that if this Secret Key is passed along with the request to Looker, it allows one to display the Dashboard without logging in. This also means that you will need more involved development to handle the Secure Storage and the Secret Key. 

Conclusion

Looker Embedding Analytics feature allows organizations to create a smooth User Experience by letting them have access to the Dashboards from within the internal applications. Having this ability reduces development costs and helps build custom applications faster. This is one of the most powerful features that make Looker a favorite among organizations. 

That said, Looker does have its share of weaknesses. If there is one weakness in the otherwise decorated feature set, it is that Looker’s ability to integrate with different types of data sources is still not comprehensive. You will find that Looker does not have native integration capabilities with some common Cloud-based data sources like Hubspot, Slack, etc. If you get stuck with a missing integration in Looker, you can consider using automated Data Integration platforms like Hevo

Hevo helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools such as Looker, etc., or desired destination in a fully automated and secure manner without having to write the code. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.

Details on Hevo’s pricing can be found here. Give Hevo a try by signing up for the 14-day free trial today.

Vivek Sinha
Director of Product Management, Hevo Data

Vivek Sinha has more than 10 years of experience in real-time analytics and cloud-native technologies. With a focus on Apache Pinot, he was a driving force in shaping innovation and defensible differentiators, including enhanced query processing, data mutability support, and cost-effective tiered storage solutions at Hevo. He also demonstrates a passion for exploring and implementing innovative trends within the dynamic data industry landscape.

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