Case Study

50% Reduction in Human Effort Through Optimized ETL Process for Data Management

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Industry 

Electronics Manufacturing

Expertise 

Azure Blob Storage, Azure IoT Hub, Kafka Connect, Azure Kubernetes Storage

Offerings/solutions 

Scalable architecture with automated ETL, real-time analysis, and powerful visualization for actionable insights.

About the Client

The client is a global tech company with a strong U.S. and Latin American presence, specializing in innovative home and business products, with manufacturing in Memphis and operations in Montvale to deliver high-quality, adaptable solutions.

Highlights

300%

Device Handling Capacity

50%

Reduction in Human Effort

20%

Increase in Profitability

The Challenge

The customer wants to re-architect their existing Azure Infrastructure solution, which would decrease the cost of the services used in the solution. The solution should also be optimized to handle more IoT devices than before. With more devices connecting to the cloud, the risk of security breaches and vulnerabilities also increases. The client must ensure their cloud infrastructure remains secure while scaling without compromising performance or increasing costs.

Solutions

• Proposed 3 alternative Batch solutions and 2 alternative Real-Time solutions with their costs.
• Built the RealTime solutions with Azure Kubernetes Service (AKS Custom Scripts) and AKS (Logstash).
• Built the Batch Data solutions with both Databricks and Azure Synapse Analytics.
• Implement scalable and cost-effective IoT data ingestion using Azure IoT Hub and HDInsight Kafka Cluster. Leverage Kafka’s ability to handle large volumes of data in real-time, ensuring that the growing number of IoT devices can be supported without performance degradation.
• Leverage a combination of Databricks, AKS, and Spark HDInsight for ETL processes.
• Implement Azure Function Apps to automate data processing workflows and trigger actions based on real-time events, reducing the need for manual intervention and lowering operational overhead.
• Used Azure API Management to expose processed data securely to external applications.
• Integrate a hosted visualization tool to create real-time dashboards and reports.

The Results

Designed a scalable architecture for 3 million devices, improved data management by 50%, boosted profitability by 20%, and enabled real-time IoT data processing.

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