logging and monitoring mechanism

Question

I need to come up with a sound strategy and develop a robust logging and monitoring plan that captures and gives critical information on all the key metrics such as availability, performance, bottlenecks, etc. around all the Azure services such as ADF and Azure Databricks, ADLS Gen2, VMs, and Blob. One way is to enable diagnostic settings and capture all the information in log analytics, and for ADF we can have stored procedures to capture pipeline runtimes, successes, and failures.  But my concern is with log analytics when logging data exceeds in GBs it has cost escalations.

Please advise me on what shall be a detailed and effective logging and monitoring strategy that is also cost-effective.

Answer 1

I am breaking your use case down step by step:

What are the core objectives of your logging and monitoring strategy?

The primary goals should include capturing critical information on availability, performance, bottlenecks, and other essential metrics across all your Azure services. This includes services like ADF, Azure Databricks, ADLS Gen2, VMs, and Blob storage. The strategy should also ensure quick detection and resolution of issues to maintain high availability and performance.

How do you balance detailed logging with cost management?

One of the significant concerns with using Log Analytics is the potential for high costs, especially as the volume of logging data increases. To address this, consider the following strategies:

1. Implement retention policies that align with your business needs. You can configure the retention period for logs in Log Analytics to automatically delete logs older than a certain period, thus reducing storage costs.
2. Instead of logging every event or metric, use sampling techniques to log only a subset of events or focus on capturing logs that are critical to your operations. Additionally, filter out less critical logs that do not contribute significant value.
3. For services like ADF, where you might use stored procedures to capture specific metrics (pipeline runtimes, successes, failures), ensure that only essential data is ingested into Log Analytics. For other metrics, consider using Azure Storage or Application Insights, where costs might be lower.

What Azure services can help in building a cost-effective logging and monitoring solution?

1. Azure Monitor and Log Analytics: These are core tools for capturing logs and metrics across Azure services. Azure Monitor can be configured to collect data from various sources, and you can use Log Analytics to query and analyze this data. To optimize costs:
   • Use workbooks and alerts to visualize and respond to data in real time, reducing the need to store large volumes of historical data.
   • Set up Log Analytics workspaces to separate data from different environments (e.g., development, staging, production) and apply different retention policies.
2. Azure Storage Accounts: For logs that do not require frequent access or detailed analysis, consider storing them in Azure Blob Storage. This can be a more cost-effective solution for long-term storage.
3. Application Insights: For monitoring application performance, failures, and dependencies, Application Insights can provide detailed telemetry without necessarily pushing all logs into Log Analytics. It allows you to monitor application behavior with sampling and user insights, which can be more cost-efficient for specific use cases.

How do you ensure the strategy is scalable and adaptable?

As your use of Azure services grows, so will the volume of data generated. To ensure scalability:

1. Use Azure Automation or Logic Apps to automate the scaling of logging and monitoring solutions based on workload. For instance, dynamically adjust log retention policies or scale Log Analytics workspaces depending on data volumes.
2. Create custom dashboards in Azure Monitor or Power BI to visualize key metrics across services. Use alerts to notify stakeholders of critical issues in real-time, allowing for quicker resolution without relying on large data volumes.

How do you handle the specific needs of different Azure services?

• Beyond using stored procedures for capturing pipeline runtimes and statuses, consider integrating ADF with Azure Monitor to collect detailed metrics and logs. This integration allows you to track activity runs, trigger statuses, and more.
• Use the built-in monitoring capabilities of Databricks, and integrate with Log Analytics for detailed insights. Focus on logging errors, performance metrics, and job execution details, and store non-critical logs in a more cost-effective location.
• Monitor access logs, performance metrics, and storage capacity. Use lifecycle management policies to automatically move or delete old logs, minimizing storage costs.

How do you continuously optimize and review the logging strategy?

Finally, it’s essential to continuously review and optimize your logging and monitoring strategy. Regularly evaluate the cost-benefit ratio of the data you are logging, and adjust your strategy as your workload and business needs evolve. Implementing a governance policy for logging and monitoring can ensure that only essential data is collected and that the strategy remains aligned with budgetary constraints.