ManageEngine recognized in the 2023 Gartner® Magic Quadrant™ for Application Performance Monitoring and Observability. Read the report✕
Azure Synapse Analytics is a specialized tool made for developers and DBAs; it marries enterprise data warehousing, big data, and log and time series analytics into a unified software. Monitoring Azure Synapse is critical to ensure safe storage of data and proper functioning of intelligence operations. Applications Manager offers an Azure Synapse monitoring feature that helps track key metrics which define the performance of Azure Synapse Analytics.
Applications Manager's Azure Synapse monitoring capabilities enables you to get deep insight into the critical parameters that define its performance. By keeping an eye on KPIs in serverless pool, Synapse Link, Synapse integrations & pipelines, you can picture the underlying operations and predict probable errors with precision. These metrics are available on the Azure Synapse monitoring dashboard in both numerical and graphical format for easy understanding.
SQL pools in Azure Synapse Analytics are used for storing and querying large datasets. You can monitor both dedicated and serverless SQL pools with Applications Manager. The Synapse Analytics monitoring tool sheds light on crucial dedicated SQL pool metrics such as the pool's resource usage, DWU limit, DWU used, pool size, status and the provisioning state. It also monitors query stats such as the number of active queries, queued queries, adaptive cache hit and adaptive cache used, and pool connection stats.
Big Data pools, commonly referred to as Apache Spark pools in Azure Synapse, provides the agency for performing in-memory cluster computing in Azure Synapse Analytics. It uses RAM for storage and parallel processing to achieve the same. As such, the key metrics when it comes to big data pools are metrics related to memory, Spark applications, vCores and nodes.
Applications Manager's Synapse monitoring tool provides vast information on the resources such as vCores and memory allocated for each Spark pool, Active & Ended Apache Spark applications, no. of nodes, node size, minimum & maximum nodes for auto-scale operation, AutoPause delay, and the provisioning state. This enables DBAs to observe and rightly scale the Apache Spark pools.
With its powerful fault management system, Applications Manager's Azure monitoring tool procures data on the faults that occur in the system, as well as drilled-down data on the origins of those faults. This speeds up the fault analysis and troubleshooting process considerably. It's easy to configure thresholds for various performance attributes and raise alarms whenever those thresholds are breached. You can also recognize performance degradation by utilizing Anomaly profiles which compares real time performance stats with a predefined standard. These alerts can be received through email, SMS, messages in Slack channels or as tickets in ServiceNow or ServiceDesk Plus.
Applications Manager's Azure Synapse monitoring tool provides extensive reports on all important performance attributes for analysis of historical trends. Forecast reports offered by Applications Manager enables you to predict growth and utilization trends using machine learning techniques, which helps you during capacity planning.
It allows us to track crucial metrics such as response times, resource utilization, error rates, and transaction performance. The real-time monitoring alerts promptly notify us of any issues or anomalies, enabling us to take immediate action.
Reviewer Role: Research and Development