From Unreliable Jobs to 99.95% System Availability
Airflow, Kubernetes, and AWS helped automate data orchestration, reduce manual intervention, and increase system availability from 90% to 99.95%.
At a Glance
The client needed to eliminate manual job scheduling, improve workflow reliability, and make complex data pipelines easier to monitor and manage.
Industry
Automotive
Function
DevOps
Solution
Data Orchestration
Engagement
DevOps
The Client
The client is a US-based used car selling company with a buyer network across the United States. Its platform provides sellers with instant offers using price prediction algorithms and real-time bids from licensed buyers.
The Challenges
Replacing Manual Data Orchestration
The client's custom application relied on manual scheduling and monitoring, which caused jobs to pause, fail, or run late.
Manual Job Scheduling
Scheduled jobs required frequent manual intervention for monitoring and execution, making routine maintenance tedious and increasing the risk of missed workflows.
Skipped Jobs
Paused jobs had to be manually checked and restarted, causing some scheduled processes to be skipped or completed later than required.
Application Failures
Frequent job failures affected application initialization, making the overall system unreliable and creating additional operational work for the engineering team.
Complex Data Pipelines
The complexity of the data pipelines made it difficult to trace problems, identify affected jobs, and resolve application issues quickly.
Limited Traceability
The existing setup provided limited visibility into job execution and failures, making it difficult for the team to identify problems efficiently.
Time-Consuming Maintenance
Engineers spent hours tracing bugs and restoring the application, taking time away from other development and operational activities.
Build a More Reliable Data Orchestration Setup
Automate workflows, improve system visibility, and reduce the manual effort required to maintain critical applications and complex data pipelines.
The Solution
Automated Data Orchestration With Airflow
Maruti Techlabs automated the client's data orchestration with Apache Airflow and built a scalable infrastructure to manage workflows and deployments.
Replaced manual orchestration with Apache Airflow to automate data workflows, reduce intervention, and improve the reliability of scheduled jobs.
Replaced manual orchestration with Apache Airflow to automate data workflows, reduce intervention, and improve the reliability of scheduled jobs.
The Journey
Manual to Automated Orchestration
Business Outcomes
Improved Reliability With Automated, Scalable Orchestration
99.95% System Availability
System availability increased from 90% to 99.95% after moving from a single instance to clusters.
Automated Job Management
Jobs could be stopped automatically using scripts, reducing manual intervention.
Faster Deployments
Easier DAG deployment and CI/CD enabled faster delivery of new features.
Parallel Processing
Massively parallel processing improved deployment efficiency.
Faster Issue Identification
Automated monitoring made it easier to identify roadblocks at an early stage.
Fewer Bugs
Continuous integration, deployment, and automatic software updates helped reduce the frequency of bugs.
Conclusion
The new orchestration setup improved system reliability, simplified monitoring, and reduced manual intervention.
Tech Stack
Improve System Reliability With Modern DevOps Practices
Automate data workflows, simplify infrastructure management, and build highly available applications with modern DevOps practices.
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Frequently Asked Questions
Apache Airflow was selected because it is Python-based and could be customized to meet the client's data orchestration requirements.
Airflow automated job orchestration and enabled parallel execution, rather than relying on sequential manual scheduling. This reduced manual intervention and helped prevent skipped or delayed jobs.
As the number of Airflow DAGs and clusters increased, managing and scheduling them became more challenging. Astronomer provided a centralized interface to manage multiple Airflow deployments and view metrics, statuses, and logs.
The CI/CD pipeline automated code deployment from development to production. This supported faster feature releases while maintaining application reliability.
The team moved the application from a single instance to clusters, and automated orchestration, parallel processing, monitoring, and deployment practices improved system reliability.