As the client’s business continued to experience substantial growth, the volume of data generated from diverse channels and source systems grew exponentially. Managing, tracking, and effectively utilizing this data for various reporting and marketing purposes became a significant challenge. Their legacy Oracle PLSQL jobs and Informatica pipelines can only provide data to stakeholders once a day, frequently causing significant delays.
This limitation posed a hurdle in making data-driven decisions, as critical information was either missing or possessing incomplete subsets. Adding to the complexity of data delivery, these legacy platforms lacked the flexibility to integrate with third-party data sources, such as Salesforce. As a result, data from these sources had to be acquired through cumbersome methods such as utilizing an SFTP server to deliver flat files once a day.
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We embarked on a digital transformation journey to modernize our client’s data architecture. We aimed to deliver data more regularly in the required format that is accessible to all teams within the organization. So, here’s what we implemented:
Data Ingestion and Transformation
To achieve prompt data delivery and availability, we devised a strategy to handle events generated by a custom POS (Point of Sale) application. These events were routed through Amazon SQS and SNS Pub/Sub layers into an AWS S3 data lake. Further, we implemented a delta lake architecture using Medallion framework to ensure data quality and consistency.
Raw events were taken in and processed through complex business logic transformations in Delta Live Tables. Subsequently, the transformed data was loaded back into the AWS S3 data lake.
Real-time Data Streaming
We implemented a streaming approach, using the Spark Structured Framework along with the TriggerOnce mechanism. This facilitated data delivery to stakeholders in near real-time, with updates processed every 15 minutes.
Orchestration and Workload Management
Orchestration of the entire workflow was managed using Databricks Jobs. The workflows were thoughtfully categorized into relevant business subject areas, reducing dependency between workloads and ensuring timely data delivery.
Recognizing the potential surge in data volume during critical business hours, such as Black Friday, we ensured that compute resources are highly scalable. We made this environment parameterized, enabling it to scale smoothly to handle higher data processing requirements when necessary.
Data Quality Assurance
As an integral part of the solution, a data quality pipeline was implemented. This pipeline rigorously validated business logics, ensuring data integrity and accuracy.
Benefits: Driving operational excellence with enhanced data accessibility
Our implementation resulted in a transformative outcome & yielded a multitude of benefits for the client.
- Timely data delivery allowed teams to focus on critical tasks, improving productivity and effectiveness.
- 64+/month
- Work hours saved
- Our parameterized compute infrastructure ensured seamless scalability during traffic spikes, enabling efficient data processing.
- Improved Operational efficiency, and the reduction in interdependencies between workflows established a strong foundation.
- 42+hours/month
- Saved on operational overhead
Ready to transform your data architecture?
With our proven expertise in implementing cutting-edge solutions like Delta Lake, Spark Streaming, and AWS technologies, we strategize the data architecture tailored to your business goals and desired ROI.
Contact us today and discover how our team reshapes your data architecture, enabling you to envision a data-driven business model.
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sales@zeb.co