H&M’s
Transforming H&M’s
Data Architecture Through Migration
H&M, a global retail leader, struggled with an outdated data infrastructure that hindered scalability, performance, and automation. Their Azure-based system faced inefficiencies in handling growing data volumes, leading to slow query speeds and operational bottlenecks. To enhance data-driven decision-making, SymuFolk partnered with H&M to migrate its entire data ecosystem to Google Cloud Platform (GCP)—a move that improved performance, automation, and scalability.
Total Hours
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Increased Efficiency
0
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Reduced Operations Cost
0
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Taxa de satisfação
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Desafios
H&M faced multiple challenges in its existing data architecture:
- Scalability Limitations: Legacy infrastructure couldn’t support increasing data volumes, slowing business operations.
- Slow Query Processing: Inefficient tools led to long query response times, delaying insights.
- Manual Workflows: Human intervention at every step increased errors, inefficiencies, and operational delays.
- Operational Bottlenecks: Limited automation created significant roadblocks to smooth data processing.
- Analytics Disruptions: Frequent manual tasks diverted the team’s focus away from actionable insights.
Solução
SymuFolk migrated H&M’s data ecosystem to Google Cloud Platform (GCP) and optimized BigQuery for efficiency, scalability, and speed. This migration eliminated data bottlenecks and enabled real-time analytics.
To enhance automation and reliability, we implemented DBT to automate data transformations and Cloud Composer for seamless workflow orchestration. Additionally, we empowered H&M’s teams with comprehensive training sessions on GCP, BigQuery, and DBT, ensuring independent operations and long-term scalability.
nossa contribuição
Pitão
GráficoQL
Node.js
NestJS
PHP
Microsoft Net
RESULTADOS
SymuFolk’s strategic transformation streamlined H&M’s data operations, improving query speed, scalability, operational efficiency, and decision-making while ensuring seamless system management.
SymuFolk’s strategic transformation streamlined H&M’s data operations, improving query speed, scalability, operational efficiency, and decision-making while ensuring seamless system management.
65%
65%
Faster Query Speed
Optimized BigQuery improved processing times, delivering instant insights.
40%
40%
Operational Efficiency
Automated workflows reduced manual interventions, enhancing reliability.
95%
95%
Documentation Coverage
Improved troubleshooting and system management, ensuring long-term efficiency.
como fizemos isso
Avaliação
Conducted a detailed analysis of H&M’s Azure infrastructure, identifying performance, security, and scalability gaps before planning migration.
Planning
Developed a risk-mitigated migration roadmap, prioritizing systems and defining success metrics to ensure a seamless transition.
Migration Execution
Migrated data, applications, and workflows in phases, ensuring data integrity and minimal downtime.
Optimization
Leveraged GCP-native tools like BigQuery and Pub/Sub to enhance performance, scalability, and cost-efficiency.
Automation & Training
Implemented DBT for automated data transformation, introduced Cloud Composer for workflow orchestration, and trained 100+ team members for operational independence.
Seamless Scalability
GCP’s elastic infrastructure now scales automatically to meet data demands.
Tecnologias
NÓS USAMOS
NÓS USAMOS
Avaliação Inicial
Conducted a detailed review of H&M’s Azure infrastructure, identifying scalability, security, and performance gaps.
Solution Design
Developed a risk-mitigated migration plan, aligning GCP architecture with H&M’s business objectives for seamless scalability.
Migration & Automation
Migrated data, applications, and workflows to GCP, optimized BigQuery, and automated data transformations with DBT.
Testing & Optimization
Enhanced performance using GCP-native tools, implemented Cloud Composer for workflow orchestration, and optimized cost efficiency.
Ongoing Monitoring & Training
Provided hands-on training for 100+ team members, ensuring independent operations and continuous improvement.
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