History

Monitor pipeline runs, troubleshoot issues, and track your data processing performance over time.

History provides comprehensive tracking and monitoring of all your data integration pipeline executions within your Precog workspace. It serves as your operational dashboard, audit trail, and troubleshooting center, giving you complete visibility into how your data flows are performing and where issues might occur.

Understanding Pipeline History

Pipeline history in Precog captures every execution of your data integration workflows, from simple source-to-destination transfers to complex multi-step processing chains. Each execution record includes timing, performance metrics, data volumes, success/failure status, and detailed logs that help you understand exactly what happened during each run.

History Components

Execution Records

  • Run identification - Unique identifiers for each pipeline execution
  • Timestamp tracking - Start time, end time, and duration for each run
  • Status information - Success, failure, warning, or in-progress states
  • Data metrics - Records processed, data volumes, and transfer rates

Performance Metrics

  • Execution timing - Detailed breakdown of processing phases and bottlenecks
  • Resource utilization - CPU, memory, and network usage during execution
  • Throughput rates - Records per second, data transfer speeds, and efficiency metrics
  • Error rates - Frequency and types of errors encountered over time

Detailed Logs

  • System logs - Infrastructure and platform-level execution information
  • Application logs - Data processing logic and transformation details
  • Error logs - Detailed error messages, stack traces, and diagnostic information
  • Audit logs - Security and compliance tracking for data access and processing

Data Lineage

  • Source tracking - Which source systems provided data for each execution
  • Transformation history - What processing steps were applied to the data
  • Destination records - Where processed data was delivered and in what format
  • Dependency tracking - How different pipeline executions relate and depend on each other

Accessing and Navigating History

History Dashboard

Recent Executions Overview

  • Status summary - Quick view of recent successes, failures, and warnings
  • Performance trends - Charts showing execution times and success rates over time
  • Active runs - Current pipeline executions and their progress
  • Alert summary - Recent alerts and notifications requiring attention

Search and Filtering

  • Date range filters - View history for specific time periods
  • Status filters - Focus on successful, failed, or warning executions
  • Pipeline filters - View history for specific sources, destinations, or schedules
  • Custom queries - Advanced filtering based on multiple criteria

Bulk Operations

  • Batch reprocessing - Re-run multiple failed executions simultaneously
  • Bulk analysis - Analyze patterns across multiple execution records
  • Export functionality - Download execution data for external analysis
  • Archive management - Manage long-term storage of historical execution data

Detailed Execution Views

Execution Summary

# Example execution record
Execution ID: exec_20241213_142305_abc123
Pipeline: Daily Customer Sync
Schedule: Daily at 2:00 AM UTC
Start Time: 2024-12-13 14:23:05 UTC
End Time: 2024-12-13 14:28:42 UTC
Duration: 5 minutes 37 seconds
Status: Success
Records Processed: 15,247 customers
Data Volume: 2.3 GB

Step-by-Step Breakdown

# Execution phases
1. Source Connection (0:05)
   - Connected to Salesforce API
   - Retrieved 15,247 customer records
   - Applied incremental filter: last_modified > '2024-12-12 14:23:05'

2. Data Transformation (2:15)
   - Applied data cleaning rules
   - Standardized address formats
   - Calculated derived fields
   - Validated data quality rules

3. Destination Loading (3:10)
   - Connected to Snowflake warehouse
   - Created staging tables
   - Loaded data via COPY command
   - Updated production tables via MERGE

4. Completion Tasks (0:07)
   - Updated execution metadata
   - Sent success notifications
   - Cleaned up temporary resources

Resource Usage Metrics

  • CPU utilization - Peak and average CPU usage during execution
  • Memory consumption - Maximum memory usage and allocation patterns
  • Network I/O - Data transfer volumes and network performance
  • Storage I/O - Disk read/write operations and temporary data usage

Performance Monitoring and Analysis

Execution Performance Tracking

Timing Analysis

  • Execution duration trends - How processing time changes over time
  • Phase breakdown - Which steps in your pipeline take the most time
  • Comparative analysis - How current runs compare to historical averages
  • Performance baselines - Establish normal performance ranges for alerting

Throughput Monitoring

  • Records per second - Data processing speed and efficiency metrics
  • Data transfer rates - Network throughput for source and destination connections
  • Parallel processing efficiency - Performance gains from concurrent execution
  • Resource scaling impact - How additional resources affect processing speed

Capacity Planning

  • Growth trend analysis - How data volumes and processing times are increasing
  • Resource utilization patterns - Peak usage times and capacity requirements
  • Scaling recommendations - When and how to increase system resources
  • Cost optimization - Balancing performance requirements with resource costs

Success Rate and Reliability Metrics

Success Rate Tracking

  • Overall success percentage - Pipeline reliability over different time periods
  • Success rate by pipeline - Which workflows are most/least reliable
  • Success rate trends - Whether reliability is improving or degrading over time
  • Recovery rate - How quickly failed executions are successfully re-run

Error Pattern Analysis

  • Common error types - Most frequent causes of pipeline failures
  • Error frequency trends - Whether specific errors are becoming more common
  • Error source analysis - Whether errors originate from sources, processing, or destinations
  • Seasonal error patterns - Time-based patterns in pipeline failures

Availability and Uptime

  • Pipeline availability - Percentage of time pipelines are functioning correctly
  • Scheduled execution reliability - How often scheduled runs execute on time
  • Recovery time metrics - How long it takes to resolve and recover from failures
  • Maintenance impact - How planned maintenance affects pipeline execution

Troubleshooting and Diagnostics

Error Investigation

Error Classification

  • Transient errors - Temporary issues that often resolve with retry
  • Configuration errors - Problems with pipeline setup or authentication
  • Data quality errors - Issues with source data format or content
  • System errors - Infrastructure or platform-level problems

Diagnostic Information

# Example error record
Error Type: Source Connection Timeout
Error Code: CONN_TIMEOUT_001
Error Message: 'Connection to salesforce.com timed out after 30 seconds'
Timestamp: 2024-12-13 14:23:35 UTC
Pipeline Step: Source Data Retrieval
Source System: Salesforce CRM
Retry Attempts: 3 of 3
Resolution: Manual intervention required

Stack Trace:
  - ConnectionManager.connect() line 245
  - SalesforceConnector.authenticate() line 67
  - DataSource.initialize() line 123

Recommendations:
  - Check Salesforce service status
  - Verify network connectivity
  - Review authentication credentials
  - Consider increasing timeout settings

Root Cause Analysis

  • Error correlation - Identify patterns and relationships between different errors
  • Timeline analysis - Understand the sequence of events leading to failures
  • Resource correlation - Connect errors to system resource usage or availability
  • External factor analysis - Consider source system maintenance, network issues, or other external factors

Performance Diagnostics

Bottleneck Identification

  • Processing step analysis - Which phases of your pipeline are slowest
  • Resource constraint analysis - Whether CPU, memory, or I/O is limiting performance
  • Dependency analysis - How waiting for external systems affects overall performance
  • Data volume impact - How increasing data sizes affect processing efficiency

Optimization Recommendations

  • Configuration tuning - Suggested parameter changes for better performance
  • Resource allocation - Recommendations for CPU, memory, or connection adjustments
  • Architecture improvements - Suggestions for pipeline design improvements
  • Timing optimization - Better scheduling to avoid resource conflicts

Comparison Analysis

  • Historical comparison - How current performance compares to past executions
  • Similar pipeline comparison - Performance relative to pipelines with similar characteristics
  • Best practice comparison - How your pipelines compare to optimization guidelines
  • Industry benchmark comparison - Performance relative to industry standards

Data Quality and Validation Tracking

Data Quality Metrics

Completeness Tracking

  • Record count validation - Expected vs. actual record counts from sources
  • Field completeness - Percentage of records with required fields populated
  • Missing data patterns - Trends in data completeness over time
  • Data coverage analysis - Which parts of your data are consistently missing

Accuracy Validation

  • Format validation results - How often data matches expected formats
  • Range validation - Whether numeric and date values fall within expected ranges
  • Referential integrity - Consistency of relationships between different data sources
  • Business rule compliance - How well data meets business validation requirements

Consistency Monitoring

  • Cross-source consistency - Whether the same entities have consistent data across sources
  • Temporal consistency - Whether data changes follow logical patterns over time
  • Duplicate detection - Frequency and types of duplicate records identified
  • Schema consistency - Whether data structure remains stable over time

Validation Results Tracking

Quality Score Trending

  • Overall quality scores - Composite measures of data quality over time
  • Quality by source - Which source systems provide the highest quality data
  • Quality by data type - Which types of data (customer, financial, etc.) are most reliable
  • Quality improvement tracking - Whether data quality initiatives are showing results

Issue Resolution Tracking

  • Quality issue identification - When and where data quality problems are detected
  • Resolution time tracking - How quickly data quality issues are addressed
  • Fix effectiveness - Whether corrections actually resolve the underlying problems
  • Preventive measure impact - How proactive quality measures reduce future issues

Historical Data Management

Retention and Archival

History Retention Policies

  • Execution record retention - How long detailed execution logs are kept online
  • Performance data archival - Long-term storage of aggregated performance metrics
  • Log data management - Retention periods for different types of log information
  • Compliance retention - Maintaining records to meet regulatory requirements

Storage Optimization

  • Data compression - Reducing storage space for long-term historical data
  • Tiered storage - Moving older data to less expensive storage tiers
  • Selective retention - Keeping detailed records for critical executions, summaries for others
  • Automated cleanup - Policies for automatically removing old historical data

Reporting and Analytics

Historical Reporting

  • Trend reports - Long-term trends in pipeline performance and reliability
  • Capacity reports - Historical resource usage and capacity planning information
  • Compliance reports - Audit trails and data lineage for regulatory requirements
  • Business impact reports - How pipeline performance affects business operations

Custom Analytics

  • Performance analytics - Deep analysis of execution patterns and optimization opportunities
  • Cost analytics - Understanding the cost implications of different pipeline configurations
  • Quality analytics - Long-term analysis of data quality trends and improvement opportunities
  • Operational analytics - Understanding how pipeline operations affect business processes

Next Steps

After understanding your pipeline history:

  1. Configure Sources - Optimize sources based on performance data
  2. Set Up Destinations - Configure destinations for optimal performance
  3. Manage Schedules - Adjust scheduling based on historical performance
  4. Review Settings - Configure workspace monitoring and alerting preferences

For advanced monitoring and troubleshooting scenarios, consult your Precog workspace documentation or contact support for assistance with complex performance issues or monitoring setup.