Amazon
Optimizing Amazon’s Data Engineering Workflow
Amazon’s data requirements were expanding rapidly, placing immense strain on its existing data engineering infrastructure. The manual handling of large datasets, slow ingestion processes, and inefficient workflows hindered Amazon’s ability to make real-time, data-driven decisions. SymuFolk partnered with Amazon to automate data workflows, enhance scalability, and enable real-time insights, ensuring their infrastructure could handle massive data volumes efficiently.
إجمالي الساعات
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Increased Workflow
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Reduced Cost
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معدل الرضا
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التحديات
Amazon faced several critical data infrastructure challenges:
- Manual Data Handling: Delays in ingestion and processing due to excessive manual intervention.
- Dirty & Voluminous Raw Data: Unstructured data in Amazon S3 required time-consuming cleaning and organization.
- Inefficient Workflows: Legacy processes could not scale effectively, reducing data quality and operational efficiency.
- Slow Decision-Making: Business teams lacked access to real-time data, slowing critical decision-making.
- Bottlenecks & Inefficiencies: The growing complexity of data accumulation and manual processes made management increasingly difficult.
حل
SymuFolk implemented automated data ingestion using Airflow, designing custom DAGs to automate scheduling, error handling, and real-time alerting. This significantly reduced manual intervention and ensured faster data availability.
For data transformation, we leveraged AWS Glue to create scalable ETL pipelines that efficiently cleaned, structured, and enriched raw data. The optimized data pipeline seamlessly integrates with Amazon’s BI tools, enabling real-time insights for business teams and faster, data-driven decision-making.
مدخلاتنا
بايثون
GraphQL
Node.js
NestJS
PHP
Microsoft Net
نتائج
SymuFolk’s transformation of Amazon’s data workflows enhanced efficiency, scalability, and decision-making speed, while reducing manual overhead and bottlenecks.
SymuFolk’s transformation of Amazon’s data workflows enhanced efficiency, scalability, and decision-making speed, while reducing manual overhead and bottlenecks.
30%
30%
Operational Overhead Reduction
Automation cut manual work by 30%.
25%
25%
قابلية التوسع
The solution scaled seamlessly, maintaining performance with growing data.
40%
40%
40% Faster Decisions
Reduced data turnaround by 40%, enabling quicker decision-making.
كيف فعلنا ذلك
Discovery & Assessment
Conducted a comprehensive analysis of Amazon’s data infrastructure, identifying inefficiencies.
Solution Design
Developed an automated data ingestion and transformation strategy using Airflow and AWS Glue.
تطبيق
Implemented scalable ETL pipelines and integrated real-time processing workflows for seamless ingestion and transformation.
Testing & Optimization
Rigorously tested workflows to optimize performance, error handling, and system reliability.
Ongoing Support & Monitoring
Established automated alerts and system monitoring to ensure continuous optimization and future scalability.
Scalable Infrastructure
The solution effortlessly scales to meet Amazon’s evolving data needs.
التقنيات
استخدمنا
عملية الخلق
التقييم الأولي
Conducted a comprehensive analysis of Amazon’s data infrastructure, identifying inefficiencies in manual workflows, data ingestion, and scalability.
Solution Design
Developed an automated data pipeline strategy using Apache Airflow for orchestration and AWS Glue for scalable ETL processing.
Implementation & Automation
Integrated real-time data ingestion, automated error handling and workflow scheduling, and optimized data transformation pipelines.
Testing & Optimization
Rigorously tested workflow efficiency, system reliability, and error handling mechanisms, ensuring seamless automation and scalability.
Ongoing Monitoring & Support
Established automated alerts and continuous monitoring, ensuring long-term system performance, adaptability, and real-time data availability.
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