Large scale customers based out of the west coast leverages DDS IRIS to accelerate the build of business rules layer on AWS and GCP

Built consumption layer for a large scale customer in less than 4 weeks by curating data from raw layer and enabled model ready features for the data science team to consume

development time to build consumable layer


Reduction in reported bugs


error in production - grade workflows

“The visual intelligence & exploratory data analysis aspects of IRIS help us digitize the Data Pack creation process under a low/no code model.

We have seen 30% productivity gain for our data engineers already.”


The customer was embarking on a digital transformation journey and in the process faced multiple barriers in building the data lake. Some of the challenges were:

  • Long time to implement. It took 6-7 months to build the consumable data layer for the analysts and data science teams
  • Business users close to the problem couldn’t code or prototype
  • Heavy costs due to talent hiring, prolonged project timelines, infra costs
  • No coding standards were followed and performance optimization techniques were not consistent


D Cube Analytics deployed state-of-the-art data preparation platform DDS IRIS to address the immediate need of building the consumption layer. We also provided a COE to assess the client’s data lake platform built on AWS and GCP.

During the course of engagement, we worked with customers to identify customizations, enhancements required for the tool to better align with the customer requirements.


Designed the consumption layer to align with business outcomes. Layers of data was built to enable stages of processing on raw data.

The final functional layer had pre-calculated KPIs which could directly power dashboards. This functional layer was also enriched with features for the Data science teams to consume.

The whole process took less than 4 weeks compared to the traditional development cycle time of 6-7 months.

The tool DDS IRIS generated Cloud agnostic workflows which were integarted with the client’s CI/CD platform to deploy across the different cloud platforms.


  • As the analysts themselves could create the pipelines and prototypes, the chances of bugs reduced, and the overall development cycle was drastically cut short
  • The ability to build workflows that could be ported between AWS and the GCP cloud platform was a game-changer for the client. It reduced their development cost by 7x to 8x times which typically would be spent in rework and migration-related IT costs
  • The average bugs count was down by 80% when developed through DDS IRIS


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