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Modern Data + AI Integration:Strategies and Architectures

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Bring Velocity and Operational Scale to your Snowflake Data Clean Room Projects with Nexla – Part 3

In part two, we discussed the challenges organizations have with scaling clean rooms. We looked into the challenges and optimal solutions for both the data providers and data consumers, as well as onboarding more people to the clean room. We also showed how an all-in-one converged platform with automation and a no-code/low-code interface can help overcome those challenges to create a better solution.

In this final blog of the 3-part series, we will discuss a use case of a large media & publishing and advertising company and showcase how Nexla provided velocity and operational scale to their clean room project.

Customer use case: Large Media & Publishing and Advertising Company

Let’s look into a use case of a large media & publishing and advertising company. There are two parts to this use case: the provider and the consumer. 

On the provider side (large media & publishing company):


The media & publishing company is bringing data in from different sources. They have viewers for their TV shows or their publications. They want to be able to bring that data in and share it confidentially. 


Their first challenge was that they rely on diverse data sets to enrich their data. Having diverse data results in richer experiences for their advertising partners (consumers); layers of data — of all sizes and kinds — provide insights into customers’ profiles and preferences as well as how best to serve them. Data is sourced from a plethora of sources; reports coming via email, users’ click information streaming directly to them, and data from several other sources. In addition, some of their data already exists in the Snowflake environment. 

The second challenge was that they had hundreds of advertising partners and had a difficult time keeping up with the demand while ensuring the right data was being shared with the right advertising partner (consumer). Each partner would be looking for a different slice of the data which was relevant to their use cases, resulting in a need for the media company to continuously create new data pipelines to generate these data set variations.

Finally, when they created a clean room, they wanted to do it at scale. They had hundreds of partners and not enough data engineers to code everything manually. They had make different slices of viewer data, maybe even that same data, and create multiple clean rooms, quickly

They needed a way to automate the process and also something that could be managed by less technical people.


With Nexla, they were able to look at an entity in Nexla’s User Interface (UI), provide join attributes, dimensions they wanted to open, created query templates and generated that entity. This enabled them to create right segments (slice) of data and share with the right consumer (advertising company). They were then easily able to choose a Snowflake clean room on Nexla’s UI as a destination, all without any code or scripting.

On the consumer side (advertising company):


These are the advertising parties, who wanted to quickly get access to the shared data in the cleanroom. They would then check the overlap of common users and run a campaign on them. 

They wanted to verify:

      • Were they matching audiences? 
      • Which show was their audience matching best?
      • Where did they want to run their campaign?


This was not an easy task, as most of them were business users unfamiliar with coding. 

Their first challenge was to connect to that clean room and query the data in a secure and supported way.

Their second challenge was to take that output into their application to use that information for audience analysis. They needed to set up those query templates and automate that process. In short, they wanted to activate that data right away. 

They needed an no-code/ low-code interface that provided them with an easy-to-use experience without having to worry about coding and updates.


They found the answer to their problems in Nexla. With Nexla, they logged in with Nexla’s simple user interface and immediately saw a clean room as an available source. Once selected, they could also choose the query templates to run and verify the audience.  

Nexla then presented them with Nexsets, logical data products that are created as an output of that query. The advertising company then easily connected the Nexset to Facebook ad’s API or to a file output for Snapchat, then activated it. The activation flow happens directly for that specific consumer (advertising company) of that clean room. With no coding required, all business users have access to the data they need.

Nexla made it a pretty straightforward process on both the consumer and the provider side. With Nexla, both the big media & publishing company and the advertising company realized velocity in easily connecting to various sources, bringing it to the clean room, creating the right segments (slices) of data, and sharing it with the right consumer. Meanwhile, the consumer was easily able to search and activate data to their choice of platform.


Snowflake’s Global Data Clean Room framework provides new capabilities that enable businesses to securely share data and collaborate with external parties. However, managing and maintaining clean rooms at scale can be a daunting task for organizations. They need a platform that is easy to use and provides them with automation.

 Nexla simplifies and streamlines clean room solutions for these organizations through automation while helping them maintain the highest standards of security and compliance. Nexla’s universal connectivity and ready-to-use data product capability adds velocity and brings operational scale to Snowflake data clean room projects. Nexla helps organizations realize the full potential of business by making it easier for providers to set up and use a clean room with their partners and helps consumers easily connect to and activate data on any platform of their choice.


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