Amazon
Optimizing Amazon’s Data Engineering Workflow
Symufolk partnered with Amazon to help them overcome significant challenges related to their data engineering infrastructure. As Amazon’s data requirements grew exponentially, their existing system struggled with inefficiencies and scalability. Our team worked closely with Amazon’s data engineering unit to implement a high-performance solution that would streamline their data workflows, improve data quality, and deliver real-time insights for better decision-making.
Total Hours
0
+
Increased Workflow
0
%
Reduced Cost
0
%
Satisfaction Rate
0
%
Challenges
- Manual handling of a large number of data sources lead to delays and slowed down ingestion.
- Raw data in Amazon S3 proved to be dirty and voluminous, which made its cleaning and organization slow as well as inconsistent.
- Traditional workflows became inefficient for large volumes of data in terms of data quality affecting operations.
- Slow Decision-Making Business teams could not access processed data quickly, which slowed decision-making based on data.
- The increased complexity of accumulating higher volume data and subsequent manual processes made it harder to manage, resulting in inefficiencies and bottle-necking.
Solution
We implemented automated data ingestion using Airflow, where custom DAGs streamlined the process by automating scheduling, error handling, and providing real-time alerts. This reduced manual intervention and sped up data availability.
For data transformation, we utilized AWS Glue to build scalable ETL pipelines that efficiently cleaned, structured, and enriched raw data, significantly improving transformation speed and data quality. The transformed data was then integrated into Amazon’s BI tools, enabling business teams to access real-time insights and make faster, data-driven decisions.
For data transformation, we utilized AWS Glue to build scalable ETL pipelines that efficiently cleaned, structured, and enriched raw data, significantly improving transformation speed and data quality. The transformed data was then integrated into Amazon’s BI tools, enabling business teams to access real-time insights and make faster, data-driven decisions.
our input
Python
GraphQL
Node.js
NestJS
PHP
Microsoft Net
RESULTS
Electrolux’s central data platform has entirely changed its approach to manufacturing operations. And it has delivered results across different dimensions:
Electrolux’s central data platform has entirely changed its approach to manufacturing operations. And it has delivered results across different dimensions:
30%
30%
30% Operational Overhead Reduction
Automation cut manual work by 30%.
25%
25%
Scalability
The solution scaled seamlessly, maintaining performance with growing data.
40%
40%
40% Faster Decisions
Reduced data turnaround by 40%, enabling quicker decision-making.
how we did it
50% Efficiency Boost
Reduced manual handling, enhancing workflow scalability.
Real-Time Access
Cut time to insights by 35%.
Scalable
Modular solution supports 100% data growth without reengineering.
Faster Processing
Automated workflows speed up data availability.
Faster Processing
Enabled faster, data-driven decision-making.
Future-Ready
Easily adapts to growing data demands.
Technologies
WE USED
Creation Process
Discovery & Assessment
We worked closely with Amazon’s team to understand the challenges, current infrastructure, and business goals.
Solution Design
Based on our assessment, we designed the data orchestration and transformation strategy using Airflow and AWS Glue.
Implementation
Our experts implemented automated workflows and scalable ETL pipelines, ensuring smooth data ingestion, transformation, and reporting integration.
Testing & Optimization
We rigorously tested the solution, making adjustments to ensure optimal performance under varying data loads.
Ongoing Support
We established monitoring tools and alerts to ensure system health and provide ongoing support for the evolving infrastructure.