Solution

Quantiphi conducted a comprehensive pipeline re-architecture, leveraging big data technologies such as Spark on Amazon EMR to efficiently process extensive datasets. They successfully transitioned the client’s data warehouse to Amazon Redshift, optimizing data management. Additionally, Quantiphi engineered an event-driven framework for streamlined pipeline automation and established a secure AWS Lake House architecture to ensure seamless data ingestion and persistence.

Results

Improved ETL SLA: Enhancing the ETL pipeline to achieve a 30-minute SLA (Service Level Agreement) target.
Cost Reduction through Architecture: Leveraging architecture changes to reduce operational costs.
Efficient Handling of Large Data Volumes: Ensuring the seamless processing of high volumes of data in a single operation.
Federation Capabilities of Redshift: Leveraging Redshift’s support for data federation to consolidate and query data from multiple sources.
Native AWS Integration: Leveraging Redshift’s native integration with various AWS services, including IAM (Identity and Access Management) and S3 (Simple Storage Service), to streamline data management and access.