About the Client

A multinational corporation with a core business of insurance and asset management, wanted to leverage open-source technologies to optimize their data warehouse architecture, enhance agility, and facilitate data-driven decision-making.

Problem Statement

The client wanted to enhance their data warehouse automation and flexibility capabilities seeking dbt’s potential to cut costs and save time. With a desire to explore the benefits of dbt in a proof-of-concept and potentially transition from Wherescape, they faced the following problems:

  • Limited knowledge and expertise in dbt
  • No experience with Data Vault 2.0 implementation in dbt
  • Dependencies on existing data warehouse automation solution
  • Missing solution for the data warehouse orchestration

The Challenge

The client faced several challenges hindering the successful implementation of their data warehouse project:

  • Need to gain expertise in dbt and familiarity with airflow for automation and orchestration
  • Insufficient internal resources to execute the POC effectively
  • Potential resistance to change from existing automation tool
  • Complex legacy business logic that needed to be migrated to Data Vault 2.0 Business Vault

These challenges contributed to difficulties in solving the identified problems and achieving the desired outcomes.

The Solution

In response to the client’s need for a more efficient data warehouse system, we implemented a proof-of-concept offering the following solution:

  • Integration of dbt and usage of our datavault4dbt package to successfully implement a Data Vault 2.0 Implementation with dbt
  • Adoption of Airflow as an orchestrator for workflow management
  • Seamless integration with Azure Data Lake for to enable usage of the current data

This solution enabled the client to make data-driven decisions effectively.

Azure Data Lake and Snowflake Data Warehouse

Tangible Results for the Client

After implementing our solution, the client witnessed significant advancements, highlighted by a successful proof of concept (POC) and achieved the following results:

  • Successful POC
  • Improved data warehouse automation and flexibility
  • Efficient utilization of airflow as an orchestrator tool
  • Enhanced ability to make data-driven decisions

Technologies involved

  • dbt Core
  • datavault4dbt
  • Azure Data Lake
  • Apache Airflow
  • Snowflake

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