This project implements a Containerized Data Processing Application with a high-availability architecture. The application processes data continuously using containerized services. The architecture distributes application workloads across multiple compute instances so that if one container or server fails, the workload can continue through another available instance. The solution focuses on service availability, automatic workload recovery, load distribution, monitoring, and scalable container management.
To implement a high-availability containerized data processing architecture that maintains continuous application operation during container, server, or service failures.
Receive data from multiple application or external data sources.
Python services collect incoming data and publish data streams to Kafka.
Process and transform incoming data continuously.
Spark consumes incoming data streams and performs filtering, transformation, and aggregation.
Store processed data for further analysis and application use.
PostgreSQL stores application and processing metadata, while Parquet stores processed analytical datasets.
Run processing services across multiple containers and nodes.
Docker packages the processing services, while Kubernetes distributes and manages containers across available nodes.
Detect failed containers or nodes and automatically recover the affected workloads.
Kubernetes restarts failed containers and reschedules workloads when required, while Prometheus monitors service and infrastructure health.
Monitor processing performance, workload health, and resource utilization.
Prometheus collects metrics and Grafana provides real-time processing and infrastructure dashboards.
Analyze processed data and generate operational reports.
Trino queries processed datasets and Superset provides analytical dashboards and reports.
Provides multiple compute instances for highly available container workloads.
Provides the secure network environment for application and processing services.
Provides persistent storage for application and processing workloads.
Stores processed datasets and application data requiring durable storage.
Manages permissions for cloud resources and application services.
Controls network traffic and protects application infrastructure.
Streams incoming data between ingestion and processing services.
Performs distributed data processing, transformation, and aggregation.
Stores application metadata, configurations, and processing information.
Stores processed data efficiently for analytical workloads.
Packages application and data-processing services into portable containers.
Deploys, distributes, monitors, restarts, and scales containerized workloads.
Collects application, container, and infrastructure health metrics.
Provides dashboards for application availability, processing performance, and infrastructure health.
Automates provisioning of cloud infrastructure.
Automates server and application configuration across the environment.
The proposed solution uses a high-availability containerized data processing architecture in which application services are deployed across multiple compute resources using Docker and Kubernetes. Kafka handles continuous data ingestion, while Spark performs distributed data processing. PostgreSQL and Parquet provide data storage, and S3 provides durable cloud storage. Kubernetes maintains multiple application instances and automatically recovers failed containers or workloads. Prometheus and Grafana provide continuous monitoring of application and infrastructure health. This architecture removes major single points of failure and allows the data processing application to continue operating even when individual containers or compute nodes fail.
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