Powered by Looker is the term coined by Looker for embedding entire analytics dashboards into your proprietary software, i.e. white labeling Looker as your own solution.

A lot of SaaS systems, especially enterprise grade, need a reporting layer. Analytics and Reporting is an important if not the most essential aspect of a business system. Few people would argue that building your own reporting layer is better than buying a best-of-breed one. Having recently been added to the Gartner Magic Quadrant for Analytics and BI Platforms, Looker is the perfect solution for white labeling reporting into your product. This is the story of an embedded analytics dashboard by Looker (5 min read).


The project was to provide a set of canned user activity reports together with specific business reports embedded into a multi-tenant SaaS platform. Security and data privacy is of the utmost concern. Reporting needs to scale to tens of thousands of users and be realtime as much as possible. The user experience must be seamless, i.e. no hint of an interruption between system boundaries allowed. 


Data Pipeline and Data Warehouse

The origin of all business data is in a clustered Mongo database self-hosted in AWS. Data is extracted with the cloud ETL tool StitchData described here.

StitchData allows creation of instant analytics data pipelines without writing a single line of code. It has an intuitive realtime dashboard with timely information of replication status for each pipeline. Here is a screenshot of a database connector:

AWS Redshift is a fast, scaleable, inexpensive data warehouse solution in the Amazon cloud. It is SQL based and can be used as the persistent datastore for the Looker BI platform. The data warehouse is the consolidating single point of truth where data from various sources is collected, cleaned, normalized, and brought into a usable form for reporting. This data pipeline is not quite realtime, but “near realtime”. A connector in StitchData can be set up to replicate new data as frequently as every 5 minutes (up to once a day). There are more realtime solution such as AWS Kinesis, Kinesis Firehose, and others, though they are usually more involved to set up and/or at a higher price.

An interesting fact of this setup is it successfully transports MongoDB data (noSQL) over to AWS Redshift (SQL). StitchData unpacks the nested arrays of a JSON document into child tables in SQL with a synthetic foreign key for the parent-child relationship injected during replication. It correctly and automatically normalizes noSQL data into SQL data. Other data pipeline providers (which I leave unnamed here) wouldn’t get past reading a nested JSON object out as String, which means all followup work of parsing out to a useful data structure is on you.


Authentication to the hosted Looker account must be provided on the fly. The solution was a web service endpoint which provided the URL of the embedded Looker dashboard, customized to the user already logged into the SaaS application. The security context of the user must be adequate and correct according to the user’s roles and permissions (authorization), as well as the initial session duration of the Looker session must be of equal duration.


Data Access Restriction in Multi-Tenant Environment

Looker allows specification of additional required user attributes per user session. The sample LookML snippet below shows a left outer join of table sales__items with table sales (where clause is  “where  sales__items._sds_source_key__id = sales._id” a parent child relationship introduced by StitchData automatically when unpacking the nested array items in document sales).
Secondly, the access_filter attribute enforces an additional filter on sales.client_id to match user.client_id. This provides the security context for a user in the multi-tenant SQL table.

explore: sales__items {

  join: sales {

    type: left_outer

    relationship: many_to_one

    sql_on: ${sales__items._sdc_source_key__id} = ${sales._id} ;;


  access_filter: {

    field: sales.client_id





Building the URL for the iFrame

Step-by-step instructions for the embedded URL are at https://docs.looker.com/reference/embedding/sso-embed.
A few settings need to be changed in the Admin console. Code needs to be written to produce a URL like such

external_group_id=”Allegra K”&

Example of a Looker dashboard that can be embedded as an iFrame into a SaaS application.



Looker was used to deliver an embedded Analytics and Reporting solution for a large enterprise multi-tenant SaaS platform. A so-called cloud analytics data pipeline was set up from MongoDB to AWS Redshift using StitchData without a line of coding. Reports and dashboards were created in Looker on top of the AWS Redshift data warehouse. The URL of the top-level analytics dashboard that was assigned to an iFrame of the SaaS application, was created by a backend API to fit the security context of the client company and the authorization level of the user.

Powered by Looker delivers a white labeled solution with all the conveniences of a hosted service like automatic upgrades, along with the powerful features of Looker itself such as report scheduling, data distribution, API integration, access restriction, among others.

Lastly, it is very nice from an operational point of view, that new reports can be added to the reporting layer independently of the release schedule of the rest of the SaaS platform.



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