Optimizing Event Processing: Achieving 50% Improvement in pipeline throughput
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Optimizing Event Processing: Achieving 50% Improvement in pipeline throughput

In this case study, we showcase how our team successfully optimized the event processing pipeline and enabled a seamless transition to a new event pipeline, for a San Francisco-based startup in the finance industry. By implementing load testing, code analysis, and optimization techniques, we achieved remarkable results. Not only did we surpass the target throughput by 2.5 times, but we also completed the project in just half of the estimated time.


An overview:

Project duration:3 Months
Technology:Data Engineering
Project goal:
  • 20% improvement in load time
  • Allow the switch to a new event pipeline by load testing, code analyzing and optimizing existing code.
Challenges:Ensure real-time data processing in the data pipeline, even during peak times.
  • Completed project in half of planned time.
  • Improved load time by 50%.
Technologies and tools used on the project:
  • Kafka ecosystem
  • Kubernetes
  • Airflow
  • Redshift
  • Python
  • Datadog
  • Locust


About the Client

The company is a San Francisco-based organization and specializes in providing a comprehensive payroll platform. The platform is specifically designed for small and medium-sized businesses, providing features such as benefits administration, compliance management, and automated payroll processing.


About the Project

The company faced a challenge where their event processor was unable to handle peak loads, resulting in application overload. This issue was particularly critical during salary payment periods, which occurred at least twice a month.

The client had a team consisting of 4 data engineers with full ownership of the components. They were occupied with daily application maintenance, leaving limited resources available. Since the identified problem was significant it required an urgent solution to ensure the proper functioning of the application.


Project Goals

We were brought in as dedicated engineers to optimize the event processor to operate without delays during those peak times. Our team consisted of 2 senior data engineers working part-time and 1 mid-level engineer working full-time. The planned time frame to complete the project was 6 months.

Code profiling & Load testing

First, we performed an in-depth code analysis that allowed us to identify problematic areas.

We leveraged the power of DataDog to efficiently track, analyze, and gain valuable insights from metrics, while effectively troubleshooting any issues that arose. Additionally, we harnessed the capabilities of Locust to perform robust load testing, ensuring the optimal performance and scalability of our systems.

The integration of these tools enabled us to gather essential information, enhancing our pipeline’s performance and efficiency. By understanding how the code is executed and where resources are being consumed, we were able to make informed decisions to optimize the performance and efficiency of their platform.

Code Optimization

After setting up proper code profiling and conducting load testing, we identified the bottlenecks in the system. We proceeded with an iterative process of optimizing the code and re-running the load tests until we were satisfied with the results. Our efforts in adding instrumentation to the project established a solid foundation for implementing new features and assessing their performance impact. This approach ensures that we prevent potential bottlenecks and maintain a clear understanding of our system’s throughput.




We maintained effective communication with the client’s team throughout the project, ensuring a seamless collaboration. Daily standup meetings were scheduled at 6 PM, accommodating the client’s time zone (9 AM on the West Coast and 12 PM on the East Coast), allowing us to discuss progress, address any questions or concerns, and align our efforts.

Additionally, we conducted planning sessions every two weeks, following the scrum framework, to establish priorities and tasks for the upcoming sprint. Ad-hoc meetings were organized whenever necessary, enabling us to promptly resolve any issues and ensure uninterrupted workflow.

Furthermore, asynchronous comments on code were utilized, providing us with valuable feedback that awaited us in the morning, allowing for efficient iterations and continuous improvement.



The project successfully facilitated a smooth transition to a new event pipeline through rigorous load testing, comprehensive code analysis, and effective code optimization. The optimized codebase now ensures efficient event processing, enhancing the overall performance and reliability of the system.

We achieved a 50% improvement in processing time, surpassing the initial target of 20%. By significantly improving event processing, users of this application can enjoy faster and more responsive experiences, ensuring seamless interactions and enhanced overall usability.


“We achieved a remarkable 50% improvement in processing time, exceeding the initial target of 20%.”


We had the necessary deep understanding and knowledge of data engineering, Kafka, and event processing, which perfectly aligned with the expertise of the client’s team. We established a strong and efficient communication channel with their team, who readily provided us with the necessary information and insights, enabling smooth collaboration throughout the project.

Despite the initially planned 6-month project timeline, our team achieved superb efficiency and completed the entire project within a condensed time frame of just 3 months.

This accelerated delivery not only showcased our effective execution, but also stands as a testament to the exceptional collaboration and expertise of the client’s team. Their comprehensive understanding and proactive communication greatly contributed to the successful outcome, enabling us to seamlessly provide valuable assistance in various data engineering and DevOps tasks, fostering an ongoing partnership.

Discover how our team tackled another project. Click here to read more.


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