This blog post demonstrates a hybrid end-to-end monitoring solution integrated with Microsoft Sentinel and Azure Monitor for ingesting streamed and batched logs from diverse sources, on-premises, or any cloud, within an enterprise ecosystem.
- Combine features provided by Microsoft Sentinel and Azure Monitor with Azure Data Explorer to build a flexible and cost-optimized end-to-end monitoring solution. Below are some examples:
- Use Microsoft Sentinel as a SIEM and SOAR component in the overall monitoring solution where you can ingest security logs from firewalls, Defender for Cloud, and so on. SIEM is short for security information and event management, whereas SOAR is short for security orchestration, automation and response.
- Use Azure Monitor’s native capabilities for IT asset monitoring, dashboarding, and alerting so you can ingest logs from VMs, services, and so on.
- Use Azure Data Explorer for full flexibility and control in all aspects for all types of logs in the following scenarios:
- No out of the box features provided by Microsoft Sentinel and Azure Monitor SaaS solutions such as application trace logs.
- Greater flexibility for building quick and easy near-real-time analytics dashboards, granular role-based access control, time series analysis, pattern recognition, anomaly detection and forecasting, and machine learning. Azure Data Explorer is also well integrated with ML services such as Databricks and Azure Machine Learning. This integration allows you to build models using other tools and services and export ML models to Azure Data Explorer for scoring data.
- Longer data retention is required in cost effective manner.
- Centralized repository is required for different types of logs. Azure Data Explorer, as a unified big data analytics platform, allows you to build advanced analytics scenarios.
- Query across different products without moving data using the Azure Data Explorer proxy feature to analyze data from Microsoft Sentinel, Azure Monitor, and Azure Data Explorer in a single query.
- To ingest logs with low latency and high throughput from on-premises or any other cloud, use native Azure Data Explorer connectors such as Logstash, Azure Event Hub, or Kafka.
- Alternatively, ingest data through Azure Storage (Blob or ADLS Gen2) using Apache Nifi, Fluentd, or Fluentbit connectors. Then use Azure Event Grid to trigger the ingestion pipeline to Azure Data Explorer.
- You can also continuously export data to Azure Storage in compressed, partitioned parquet format and seamlessly query that data as detailed in the Continuous data export overview.
- Azure Event Hub: Fully managed, real-time data ingestion service that’s simple, trusted, and scalable.
- Azure IoT Hub: Managed service to enable bi-directional communication between IoT devices and Azure.
- Kafka on HDInsight: Easy, cost-effective, enterprise-grade service for open source analytics with Apache Kafka.
- Azure Data Explorer: Fast, fully managed and highly scalable data analytics service for real-time analysis on large volumes of data streaming from applications, websites, IoT devices, and more.
- Azure Data Explorer Dashboards: Natively export Kusto queries that were explored in the Web UI to optimized dashboards.
- Microsoft Sentinel: Intelligent security analytics for your entire enterprise.
- Azure Monitor: Full observability into your applications, infrastructure, and network
The best part about all this is Microsoft Sentinel is built on Azure Monitor (Log Analytics) which in turn, is built on Azure Data Explorer.
That means that switching between these services is seamless. Which allows us to reuse Kusto query language queries and dashboards across these services.
Do you see now why I really love KQL and ADX??