Migrations of Analytics Workload from AWS to GCP
Client
The client is a fast-growing EdTech company delivering personalized learning experiences at scale. Their platform processes large volumes of learner data and required a modern, cost-efficient analytics stack to support real-time insights and machine learning use cases.
Project Context
The client wanted to migrate to a near-real-time, large-scale data platform to consolidate and enrich business data for analytics and ML use cases.
Challenges
Required real-time data pipelines
Needed support for ML and advanced analytics
Wanted lower TCO than AWS stack
Solution
Built real-time pipelines with Kafka and enriched data using Spark
Performed detailed TCO analysis
Managed a centralized data lake with GCP serverless stack
Project Objectives
Build a data lake with time-series and summary analytics, enable ML use cases, and reduce TCO.
Solution Delivery
SquareShift migrated the analytics workload from AWS to GCP using a fully serverless setup, enabling real-time analytics and supporting ML use cases while reducing cost.
Testimonial
Migrating to GCP with SquareShift’s help has transformed our data operations - faster, smarter, and more cost-effective
