Case Studies

Optimizing Big Data Workflows With DevOps

DevOps and Big Data case study cover image
Big Data flow transformation with DevOps - Akvelon's case study

Our Client: A Pioneer in Real-Time Big Data Solutions

Our client, a leading Big Data solutions provider, specializes in transforming raw data into actionable insights through advanced technologies like Kafka, Spark, and Hadoop.

Their platform provides real-time data analysis, filtering, and processing capabilities and allows for high levels of customization with an internal processing language. Our client's cloud-agnostic solution operates across Azure, AWS, Google Cloud Platform, and private in-house clouds.

When they turned to Akvelon, our client aimed to streamline their product delivery process, improve usability for data engineers, and automate key workflows.

Core Project Challenges & Akvelon’s Solution

After our initial project assessment, we outlined the following challenges to address to streamline the platform performance and product delivery:

    • Inefficient development and deployment workflows arise from an inadequate DevOps infrastructure.
    • Lack of efficient load testing makes the messaging subsystem unreliable under high loads.
    • Fragmented infrastructure creates operational complexities.
    • Scalability issues make it harder for the system to handle more data and meet growing user demands.
    • Manual workflows increase effort and raise the likelihood of errors.

To address these issues, we implemented a scalable CI/CD pipeline using Jenkins, Terraform, and Ansible across multiple clouds to automate deployments and improve reliability. The real breakthrough came in enhancing the data processing system, as it allowed the system to handle over 12,000 messages per second. With these enhancements, we resolved stability issues and eliminated critical bugs.

Our team also transformed the data engineering workflows by replacing manual CLI operations with an intuitive UX/UI, making Spark job management across clusters much more efficient. For better monitoring, we centralized logs through Kafka into Kusto, with Grafana visualizations providing clear, real-time insights into system performance. These changes streamlined operations, increased the system’s scalability, and eased system management.

Results & Impact

      • 50% Faster Deployments:
        Automated processes cut deployment time in half.
      • Effortless Workflow Management:
        Engineers now manage data processing workflows seamlessly across multiple clusters.
      • Scalable & Cloud-Agnostic Framework: Our solution ensures flexibility, future-proof operations, and compatibility with diverse cloud environments.
      • Enhanced System Insights:
        Centralized logging and monitoring improved troubleshooting and operational efficiency.

Conclusion

Our solution streamlined operations by reducing deployment times, simplifying workflow management, and providing a scalable, cloud-agnostic framework. Our client's platform can grow and perform seamlessly across different environments with improved system insights and a future-ready setup.

Whether you’re tackling Big Data challenges or optimizing your infrastructure, Akvelon’s DevOps expertise provides reliable, scalable solutions.

Technologies Used:
Cloud & DevOps: Azure AD, Azure DevOps
Data Processing: Apache Kafka, Apache Spark
Infrastructure: Kubernetes, Terraform