Added best practices around optimization, security, and error handling from recent discussions

Author: JPHaus

Guides/Data Pipeline Best Practices.md

@@ -33,12 +33,17 @@ A best practice guide for data pipelines compiled from data engineers in the com
 
 - Don't let file sizes become too large or too small. Large files (>1 GB) can require more resources and many small files can create a large overhead to process. ~250 MB is a good size that allows for better parallel processing.
 - Use the [[Claim Check Pattern|claim check pattern]] to pass large amounts of data between tasks in your pipeline.
+- Compress data when possible before storing or transmitting. gzip, snappy, and lz4 are common compression algorithms.
+- Partition/cluster data based on common query patterns for efficient querying.
 
 ## Security
 
 - Save credentials in a secrets manager and access them in your pipeline programmatically.
 	- Ideally, have secrets rotated automatically.
 - Avoid logging any sensitive information like credentials or PII data.
+- Encrypt data in transit and at rest.
+- Implement proper access control (IAM) policies.
+- Create audit trails/logs of data access and changes.
 
 ## Testing
 
@@ -47,6 +52,14 @@ A best practice guide for data pipelines compiled from data engineers in the com
 - Set up a local environment to test pipelines locally first. (see Docker above)
 - Re-define pipeline failures. If pipeline fails x times but data is still delivered on time then it was successful.
 
+## Error Handling and Monitoring
+
+- Implement exponential backoff for transient failures.
+- Streaming: Add dead letter queues (DQL) to store failed messages. This allows you to inspect and reprocess them later without losing data.
+- Set up notifications for pipeline failures.
+- Use comprehensive logging for debugging and auditing.
+- Track metrics such as ingestion rates, latency, CPU/memory usage, and error rates.
+
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 ## This note in GitHub