Massive Data Volumes
Processing and managing high-frequency IoT data streams from diverse sources without performance bottlenecks.
Real-Time Insights
Enabling immediate analytics and alerting to support timely decision-making.
Scalable Storage
Archiving large datasets efficiently while ensuring quick retrieval when needed.
Distributed Cron Jobs
Coordinating and monitoring numerous scheduled tasks across multiple microservices.
Complex Dependencies
Managing interlinked workflows and task execution sequences without errors.
Operational Reliability
Minimizing downtime through automated retries and robust failure recovery mechanisms.
Implemented Kafka for high-throughput, real-time streaming, and reliable data ingestion from IoT devices.
Used Druid for fast analytics, ad-hoc queries, and scalable long-term data storage.
Centralized cron management with Apache Airflow to schedule, monitor, and automate workflows across services.
Deployed Kafka, Druid, and Airflow in High Availability mode for maximum uptime and resilience.
Migrated 10 TB of legacy data to Druid for improved query performance.
Automated infrastructure provisioning and scaling for consistent, repeatable deployments.
Seamlessly migrated 10 TB of historical data to Druid without impacting ongoing operations.
Enabled ingestion and analysis of 1 Gbps IoT streaming data in real time.
Achieved resilient Kafka, Druid, and Airflow deployments, ensuring minimal downtime and uninterrupted operations.
Consolidated 100+ disparate cron jobs into Airflow for better control and monitoring.
Reduced complex query execution time from hours to just seconds or minutes.
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