Background: Addressing the Limits of a Legacy System
A prominent company in the Middle East was facing increasing challenges with its existing data platform. Built on Cloudera, the system had served the business for several years, but it was no longer meeting evolving demands. Maintenance was costly, scaling was difficult, and business teams found it hard to access timely insights.
Leaders in both IT and the business saw that the current environment was slowing them down. To stay competitive, they needed a more flexible, efficient platform—one that could support analytics, automation, and future use cases like machine learning and Generative AI.
They chose to modernize by migrating to Google Cloud Platform (GCP). To help them do it right, they partnered with Evalueserve.
Approach: A Structured Migration, Grounded in Business Priorities
Evalueserve began with a clear focus: understand what the company needed, how the current system was operating, and what success would look like in a new environment.
Initial Assessment
The first step was a full assessment of the existing data estate. Evalueserve’s team worked closely with the client’s IT and business teams to map out:
- Key use cases and workloads
- Tools and technologies in use (Hive, Spark, MapReduce, etc.)
- System performance and resource utilization
- Data volumes, formats, and integration points
The result was a detailed picture of what was working, what wasn’t, and what could be improved through a move to Google Cloud
Designing the Target Environment
With this understanding in place, Evalueserve designed a target architecture using Google Cloud services such as:
- BigQuery for analytics and reporting
- Dataproc for processing workloads
- Dataflow and Pub/Sub for streaming and change data capture
- Dataplex for data governance
This future-state design included recommendations on performance tuning, cost optimization, and scalability.
Execution: Migrating with Confidence and Control
Evalueserve structured the project around clear milestones, each tied to measurable outcomes. Migration was prioritized based on business impact, ensuring the most critical use cases were addressed first.
Steps in the Migration
- Legacy logic (e.g., Hive and Spark) was refactored and optimized for Google Cloud
- Data pipelines were rebuilt using native cloud tools
- Historical data was migrated, and new data ingestion pipelines were created
- Rigorous testing was conducted to ensure data quality and system performance
Security, access control, and monitoring were all built into the new environment from the beginning.
Results: A Modern Platform, Ready for What’s Next
After the migration, the company saw immediate improvements:
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Reduced operational overhead, with automated scaling and centralized monitoring
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Faster access to data, enabling more timely business decisions
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Lower infrastructure costs, with optimized use of cloud resources
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Stronger foundation for future use cases, including predictive analytics and AI
The move also improved collaboration between IT and business teams, who could now work with a shared platform built for growth.
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Overview & Impact
A prominent company in the Middle East modernized its data platform by migrating to Google Cloud Platform with the support of Evalueserve. This transformation led to improved data access and scalability.
