reduction in downtime
cost saving compared to the previous application
The client, a global semiconductor manufacturing leader, is dedicated to reshaping manufacturing through innovative solutions, specializing in advanced process technology for rapidly expanding markets. They had initially established their data pipeline on AWS, with the goal of transitioning their data sources to Redshift and improving the efficiency of their existing ETL pipeline to handle extensive Terabytes of data processing.
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.