Building an AIOps Pipeline with Open Source Tools Training Course

Designing an Open AIOps Architecture

• Introduction to essential elements in open AIOps pipelines
• Data pathways from ingestion through to alerting
• Comparative analysis of tools and integration strategies

Data Acquisition and Consolidation

• Collecting time-series data using Prometheus
• Capturing log data with Logstash and Beats
• Standardizing data for cross-source correlation

Creating Observability Dashboards

• Visualizing key metrics via Grafana
• Developing Kibana dashboards for log analysis
• Leveraging Elasticsearch queries to derive operational insights

Anomaly Detection and Incident Forecasting

• Integrating observability data into Python pipelines
• Training ML models for outlier identification and forecasting
• Deploying models for real-time inference within the observability workflow

Alerting and Automation via Open Tools

• Defining Prometheus alert rules and configuring Alertmanager routing
• Initiating scripts or API workflows for automated responses
• Utilizing open-source orchestration platforms (such as Ansible, Rundeck)

Integration and Scalability Factors

• Managing high-volume ingestion and extended data retention
• Implementing security and access controls within open-source stacks
• Scaling individual layers independently: ingestion, processing, and alerting

Real-World Use Cases and Extensions

• Case studies covering performance tuning, downtime avoidance, and cost efficiency
• Expanding pipelines with tracing utilities or service maps
• Best practices for operating and sustaining AIOps in production

Conclusion and Recommended Next Steps