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H&M, a global retail leader, struggled with an outdated data infrastructure that hindered scalability, performance, and automation.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

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Reduced Operations Cost

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Zufriedenheitsrate

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Herausforderungen

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.

Lösung

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.

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ERGEBNISSE

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%

Faster Query Speed

Optimized BigQuery improved processing times, delivering instant insights.

40%

Operational Efficiency

Automated workflows reduced manual interventions, enhancing reliability.

95%

Documentation Coverage

Improved troubleshooting and system management, ensuring long-term efficiency.

wie wir es gemacht haben

Bewertung

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.

Technologien

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Schöpfungsprozess

Erste Einschätzung

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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