Logstash Pipeline Development

Data Ingestion Pipelines: Design and implement Logstash pipelines to ingest data from various sources, including logs, metrics, and other structured/unstructured data.

Data Transformation: Develop custom filters and grok patterns in Logstash to parse, transform, and enrich data before sending it to Elasticsearch.

Real-Time Data Processing: Implement real-time data processing pipelines using Logstash, ensuring minimal latency and high throughput.

Performance Tuning and Optimization

Pipeline Performance Optimization: Optimize Logstash pipeline performance by tuning configurations, managing resource allocation, and reducing processing bottlenecks.

Scaling Logstash Nodes: Configure and manage Logstash clusters for high availability and scalability, including load balancing across multiple nodes.

Log Management and Analysis

Centralized Log Management: Set up centralized log management solutions using Logstash to collect, filter, and forward logs to Elasticsearch for storage and analysis.

Error Handling and Monitoring: Implement error handling, dead letter queues, and monitoring for Logstash pipelines to ensure reliability and prevent data loss.

Integration and Custom Solutions

Third-Party Integrations: Integrate Logstash with various third-party tools and services (e.g., Kafka, Beats, AWS S3) to create comprehensive data processing solutions.

Custom Plugins and Filters: Develop custom Logstash plugins and filters to meet specific business requirements, enhancing data processing capabilities.

Support and Troubleshooting

Logstash Troubleshooting: Provide expert-level support for Logstash, diagnosing and resolving issues related to pipeline failures, performance degradation, and data inconsistencies.

Maintenance and Upgrades: Manage Logstash upgrades and maintenance, ensuring compatibility with other ELK stack components and minimizing downtime.