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Designing a Data Stream Processing Pipeline for a Leading Moving & Storage Company

PODS system maintenance notice with a request a quote form, allowing users to submit service inquiries.

introduction

This study details a project related to designing a data stream processing pipeline for one of our Clients in moving and storage industry. The objective was to extract, transform and load data in real-time into data warehouses, thereby replacing erstwhile ine>icient and batched processing data jobs that heavily relied on outdated legacy systems. The legacy systems had been in place for 25 years and were deemed ine>icient due to their limitations in terms of speed, scalability, and dependency. By implementing a modern data stream processing pipeline, the aim was to leverage the benefits of robustness, fault tolerance, and distributed processing.

problem statement

  • Inefficient system due to their limitations in terms of speed, scalability, and dependency
  • No leverage in terms of benefits, robustness, fault tolerance, and distributed processing
  • Batched processing data jobs that heavily relied on outdated legacy systems.
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analysis

The existing legacy systems were thoroughly analysed for their limitations , to support the high demands of moving and storage company. The outdated system was found to be inefficient due to the batched processing data jobs which heavily relied on legacy systems.

implementation

  • To support the high demands of the moving and storage industry, the application service was deployed on a cloud platform. This enabled distributed processing and auto-scaling capabilities and ensured efficient handling of workload fluctuations during busy seasons
  • By harnessing cutting-edge distributed data storage and distribution platforms, as well as distributed stream processing frameworks, a robust and e>icient data delivery application solution was developed.
  • Furthermore, a monitoring dashboard was offered that provided insights into the performance of multiple data processing worker applications, along with load and capacity information. This monitoring system facilitated data-driven business decisions and enabled efficient scaling of the platform based on demand.

technologies used

React Native for Mobile Development
Node.js and Express for Backend Services
MongoDB for Data Storage
Google Maps API for Real-Time Tracking and Route Optimization
Java Spring boot Framework
Tibco EMS
Tibco Active spaces
Traffic cluster for authentication and Load balancing
Kibana, elastic search and Logstash for monitoring and alerting dashboard
Plugins for Legacy protocols
PODS system maintenance notice with a request a quote form, allowing users to submit service inquiries.
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outcomes & benefits

  • The advancements allowed us to o>er a cloud streaming application service that significantly accelerated the enterprise data transformation and processing tasks.
  • The solution delivered was fully compliant with the standard regulations of the moving industry and aligned with the requirements and procedures of the U.S. military regarding moving and storage solutions for military equipment.

conclusion

By thoroughly understanding the business requirements and expectations, it was convenient to conceptualize and execute the planned services effectively. Overall, the implementations resulted in a cutting-edge data stream processing pipeline that transformed the way the data was handled, providing real-time data delivery, improved efficiency, scalability, and compliance with industry standards.
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