Data Pipeline Issues

What This Means

Data pipelines are systems that extract, transform, and load (ETL) data from various sources to destinations for analysis and reporting. Pipeline issues can cause:

• Missing or incomplete data in analytics
• Data staleness and delayed reporting
• Duplicate or corrupted records
• Failed data transformations
• Performance degradation in data processing
• Data loss during transfers
• Inconsistent data across systems

Common pipeline components include data ingestion, transformation, validation, enrichment, and loading into data warehouses or analytics platforms.

How to Diagnose

Monitor Pipeline Metrics

// Track pipeline execution metrics
const pipelineMetrics = {
  startTime: Date.now(),
  recordsProcessed: 0,
  recordsFailed: 0,
  errors: []
};

async function runPipeline(data) {
  try {
    for (const record of data) {
      try {
        await processRecord(record);
        pipelineMetrics.recordsProcessed++;
      } catch (error) {
        pipelineMetrics.recordsFailed++;
        pipelineMetrics.errors.push({
          record,
          error: error.message,
          timestamp: new Date()
        });
      }
    }
  } finally {
    pipelineMetrics.duration = Date.now() - pipelineMetrics.startTime;
    logMetrics(pipelineMetrics);
  }
}

Check Data Quality

• Compare record counts between source and destination
• Verify data freshness (timestamp of last update)
• Check for null values in required fields
• Validate data types and formats
• Monitor for duplicates

Pipeline Health Checks

async function healthCheck() {
  const checks = {
    sourceConnection: await testSourceConnection(),
    destinationConnection: await testDestinationConnection(),
    lastRunTime: await getLastSuccessfulRun(),
    recordCount: await validateRecordCount(),
    dataFreshness: await checkDataFreshness()
  };

  const isHealthy = Object.values(checks).every(check => check.status === 'ok');

  if (!isHealthy) {
    alertOnPipelineFailure(checks);
  }

  return checks;
}

Common Indicators

• Data gaps or missing time periods
• Sudden drops in record volume
• Increasing error rates in logs
• Long-running or stuck jobs
• Memory or disk space issues

General Fixes

1. Implement idempotent operations - Make pipeline re-runnable safely

   async function upsertRecord(record) {
     // Use upsert instead of insert to handle duplicates
     await db.query(`
       INSERT INTO analytics (id, data, updated_at)
       VALUES ($1, $2, NOW())
       ON CONFLICT (id)
       DO UPDATE SET data = EXCLUDED.data, updated_at = NOW()
     `, [record.id, record.data]);
   }
   

2. Add retry logic with backoff - Handle transient failures

   async function retryOperation(operation, maxRetries = 3) {
     for (let attempt = 1; attempt <= maxRetries; attempt++) {
       try {
         return await operation();
       } catch (error) {
         if (attempt === maxRetries) throw error;
   
         const delay = Math.pow(2, attempt) * 1000;
         console.warn(`Attempt ${attempt} failed, retrying in ${delay}ms`);
         await new Promise(resolve => setTimeout(resolve, delay));
       }
     }
   }
   

3. Use batch processing - Process records in batches for efficiency

   async function processBatch(records, batchSize = 100) {
     for (let i = 0; i < records.length; i += batchSize) {
       const batch = records.slice(i, i + batchSize);
       await Promise.all(batch.map(record => processRecord(record)));
   
       // Yield to prevent blocking
       await new Promise(resolve => setImmediate(resolve));
     }
   }
   

4. Implement data validation - Catch issues early

   function validateRecord(record) {
     const errors = [];
   
     if (!record.id) errors.push('Missing required field: id');
     if (!record.timestamp) errors.push('Missing required field: timestamp');
     if (typeof record.value !== 'number') errors.push('Invalid type for value');
   
     if (errors.length > 0) {
       throw new ValidationError(errors.join(', '));
     }
   
     return true;
   }
   

5. Add monitoring and alerting - Detect issues quickly

   async function monitorPipeline() {
     const metrics = await getPipelineMetrics();
   
     // Alert on high error rate
     if (metrics.errorRate > 0.05) {
       sendAlert('High error rate in pipeline', metrics);
     }
   
     // Alert on data staleness
     const hoursSinceLastRun = (Date.now() - metrics.lastRunTime) / 3600000;
     if (hoursSinceLastRun > 2) {
       sendAlert('Pipeline has not run in 2 hours', metrics);
     }
   
     // Alert on low throughput
     if (metrics.recordsPerMinute < expectedThroughput * 0.5) {
       sendAlert('Pipeline throughput below threshold', metrics);
     }
   }
   

6. Use dead letter queues - Capture failed records for later processing

   async function processWithDLQ(record) {
     try {
       await processRecord(record);
     } catch (error) {
       // Send to dead letter queue for manual review
       await deadLetterQueue.send({
         record,
         error: error.message,
         timestamp: new Date(),
         retryCount: record.retryCount || 0
       });
     }
   }
   

7. Implement incremental loading - Process only new/changed data

   async function incrementalLoad(lastRunTimestamp) {
     // Only fetch records updated since last run
     const newRecords = await source.query(`
       SELECT * FROM events
       WHERE updated_at > $1
       ORDER BY updated_at ASC
     `, [lastRunTimestamp]);
   
     await processRecords(newRecords);
   
     // Update watermark
     await saveLastRunTimestamp(Date.now());
   }
   

8. Use checkpointing - Resume from last successful point

   async function processWithCheckpoints(records) {
     let checkpoint = await loadCheckpoint();
   
     for (let i = checkpoint; i < records.length; i++) {
       await processRecord(records[i]);
   
       // Save checkpoint every 100 records
       if (i % 100 === 0) {
         await saveCheckpoint(i);
       }
     }
   
     await clearCheckpoint();
   }
   

9. Partition large datasets - Process in parallel

   async function processPartitions(data, partitions = 4) {
     const partitionSize = Math.ceil(data.length / partitions);
   
     const promises = Array.from({ length: partitions }, (_, i) => {
       const start = i * partitionSize;
       const end = start + partitionSize;
       const partition = data.slice(start, end);
   
       return processPartition(partition, i);
     });
   
     await Promise.all(promises);
   }
   

Platform-Specific Guides

Further Reading

• Data Pipeline Best Practices
• ETL vs ELT
• Data Quality Monitoring
• Building Reliable Data Pipelines
• Data Pipeline Testing