Monitor and Optimize Data Storage and Data Processing¶

Overview¶

This domain covers monitoring data storage and processing systems, and optimizing and troubleshooting performance issues. It represents 10-15% of the exam and focuses on operational excellence and performance tuning.

Key Topics¶

Monitor Data Storage and Processing¶

• Logging implementation with Azure Monitor
• Monitoring service configuration
• Data movement performance measurement
• Statistics updates and monitoring across system
• Data pipeline performance monitoring
• Query performance measurement
• Cluster performance monitoring
• Custom logging options understanding
• Pipeline test scheduling and monitoring
• Azure Monitor metrics and log interpretation
• Pipeline alert strategy implementation

Optimize and Troubleshoot¶

• Small file compaction
• Data skew handling
• Data spill handling
• Shuffle partition tuning
• Pipeline shuffling identification
• Resource management optimization
• Pipeline performance tuning
• Analytical vs transactional optimization
• Descriptive vs analytical workload optimization
• Failed Spark job troubleshooting
• Failed pipeline run troubleshooting

Services Reference¶

Core Services¶

• Azure Monitor
• Log Analytics
• Application Insights
• Azure Data Factory Monitoring
• Synapse Studio Monitoring
• Databricks Monitoring

Supporting Services¶

• Azure Advisor
• Azure Cost Management
• Spark UI
• Azure Metrics Explorer
• Kusto Query Language (KQL)

Best Practices¶

Monitoring Strategy¶

• Implement comprehensive logging at all pipeline stages
• Set up proactive alerts for failures and performance degradation
• Use custom metrics for business-specific KPIs
• Create monitoring dashboards for key metrics
• Retain logs according to compliance requirements
• Monitor costs alongside performance metrics

Performance Optimization¶

• Identify and eliminate bottlenecks systematically
• Use explain plans to understand query execution
• Implement caching for frequently accessed data
• Optimize file sizes (avoid small file problem)
• Use appropriate partitioning strategies
• Minimize data shuffling in Spark jobs

Resource Management¶

• Right-size clusters based on workload
• Use auto-scaling for variable workloads
• Schedule non-critical jobs during off-peak hours
• Implement cost allocation tags
• Monitor resource utilization and adjust accordingly
• Use spot instances for fault-tolerant workloads

Troubleshooting Approach¶

• Check pipeline activity run details and error messages
• Review metrics for anomalies
• Examine Spark UI for job details and stages
• Analyze query execution plans
• Check for data skew in partitions
• Review resource utilization (CPU, memory, I/O)

Common Scenarios¶

Performance Issues¶

• Slow-running queries in Synapse SQL pool
• Spark job failures due to out-of-memory errors
• Pipeline execution delays and timeouts
• Data skew causing uneven partition processing
• Small file problems affecting query performance

Monitoring and Alerting¶

• Pipeline failure notifications
• Long-running query alerts
• Resource utilization threshold alerts
• Data quality validation failures
• Cost anomaly detection

Optimization Tasks¶

• Query performance tuning with statistics and indexes
• Spark job optimization with repartitioning
• File compaction to reduce small files
• Distribution key selection for Synapse tables
• Materialized view creation for common queries

Troubleshooting Workflows¶

• Investigating pipeline failures with logs
• Analyzing Spark job failures with Spark UI
• Diagnosing data quality issues
• Identifying resource bottlenecks
• Root cause analysis for performance degradation

Study Tips¶

• Learn Kusto Query Language (KQL) for Log Analytics
• Practice reading Spark execution plans and UI
• Understand data skew detection and remediation
• Hands-on experience with pipeline monitoring in Data Factory
• Study Synapse dedicated SQL pool performance tuning
• Practice identifying shuffle operations in Spark
• Learn small file compaction techniques
• Understand different types of Spark shuffles
• Study partition pruning and its benefits
• Practice creating custom metrics and alerts