Site Reliability Engineering (SRE) team for log analytics ,Here are a few examples:

1. Elasticsearch : Elasticsearch is a widely used open-source search and analytics engine. It is often used in conjunction with other components of the Elastic Stack, such as Logstash for data ingestion and Kibana for visualization and exploration of logs.

2. Splunk : Splunk is a popular commercial log management and analysis platform. It offers features for collecting, indexing, searching, and analyzing log data from various sources. Splunk supports powerful search capabilities and provides visualizations, dashboards, and alerting mechanisms.

3. Graylog : Graylog is an open-source log management platform that helps collect, index, and analyze log data. It provides search functionality, dashboards, and alerting features. Graylog supports centralized log collection from various sources, including syslog, file inputs, and message queues. 4. ELK Stack : The ELK Stack combines Elasticsearch, Logstash, and Kibana to create a comprehensive log analytics solution. Logstash is used for log ingestion and transformation, Elasticsearch for indexing and search, and Kibana for visualizing and exploring log data.

5. Datadog : Datadog is a cloud monitoring and analytics platform that offers log management capabilities. It provides features for collecting, aggregating, searching, and analyzing log data. Datadog integrates with other monitoring tools and offers visualizations, alerts, and anomaly detection.

6. Prometheus and Grafana : While primarily known for time-series monitoring, Prometheus and Grafana can also be used for log analysis. Prometheus can scrape logs from targets using exporters, and Grafana provides powerful querying and visualization capabilities for log data.

These are just a few examples of tools commonly used for log analytics in an SRE context. The choice of tooling depends on various factors such as the organization's requirements, scale, budget, and existing infrastructure. It's important to evaluate different tools based on their features, scalability, ease of use, and compatibility with existing systems before selecting the most suitable one for your specific needs.