H&M’s
Transforming H&M’s
Data Architecture Through Migration
H&M, a global retail leader, faced scalability, query speed, and manual workflow challenges with its Azure-based data infrastructure. To enhance efficiency and enable data-driven decisions, H&M migrated to the Google Cloud Platform (GCP) for its high-performance analytics, streamlined operations, and scalable cost-effectiveness.
Total Hours
0
+
Increased Efficiency
0
+
Reduced Operations Cost
0
%
Satisfaction Rate
0
%
Challenges
- Struggled with the rapid growth of data volumes-the tools that were used initially could not handle the volumes, meaning that query response times slowed down.
- Inefficient workflows-manual interfaces introduced inefficiencies, brought in the possibility of errors due to manual intervention in each step of the data lifecycle.
- Scalability issues infrastructure was not scalable in regard to H&M’s new business requirements.
- Operational bottlenecks-the workflows presented were complex, and the level of automation was low, creating huge roadblocks to overall operational efficiencies.
- Diverted Focus from Analytics: The requirement of human intervention in workflows resulted in the diversion of focus away from analytics and actionable insights.
Solution
We moved to Google Cloud Platform and further optimized BigQuery to be efficient and scalable. We leveraged DBT to make data transformation automated, minimize errors, and standardize processes.
We built effective end-to-end data pipelines that included proactive monitoring using Cloud Composer orchestration. Additionally, we equipped the teams of H&M through training and workshops in GCP, BigQuery, and DBT, making the operation independent and scalable.
We built effective end-to-end data pipelines that included proactive monitoring using Cloud Composer orchestration. Additionally, we equipped the teams of H&M through training and workshops in GCP, BigQuery, and DBT, making the operation independent and scalable.
our input
Python
GraphQL
Node.js
NestJS
PHP
Microsoft Net
RESULTS
We’re proud of reaching new heights with our customers,
helping them achieve advanced levels of scalability and stability.
We’re proud of reaching new heights with our customers,
helping them achieve advanced levels of scalability and stability.
65%
65%
Query Speed
Reduced query processing times by 65%, enabling faster access to critical data.
70%
70%
Scalability
Elastic GCP infrastructure seamlessly scaled to meet H&M’s growing data demands.
40%
40%
Operational Efficiency
Automated workflows cut manual effort by 40%, improving reliability and standardization.
how we did it
Tailored Strategy
90% reduction in migration disruptions through a tailored approach.
Best-in-Class Tools
65% increase in efficiency through BigQuery and DBT for data management.
End-to-End Expertise
Automated 100% of critical workflows, ensuring smooth operations.
Empowered Teams
Trained more than 100 team members to improve skills and knowledge.
System Documentation
Ensured 95% system documentation coverage for easier understanding and support.
Continuous Improvement
Focus on optimizing tools and processes to maintain high operational standards.
Technologies
WE USED
Creation Process
Assessment
Analyzed the Azure infrastructure to identify gaps in performance, security, and scalability, defining objectives for a seamless migration to GCP.
Planning
Created a risk-mitigated migration roadmap with prioritized systems, clear timelines, and success metrics to ensure minimal disruption and smooth transition to GCP.
Migration Execution
Migrated data, applications, and workflows to GCP in phases, ensuring data integrity, minimizing downtime, and integrating with existing systems for seamless operation.
Optimization
Optimized GCP resources using native tools like BigQuery and Pub/Sub, improving performance, scalability, and cost-efficiency while supporting business growth.
Automation
Implemented DBT for data transformation and Cloud Composer for workflow orchestration, automating tasks, streamlining processes, and improving consistency across teams.