Best Practices

Query Optimization

• Partitioning & Primary Keys: Define partition keys (e.g., date_key, region, service_type) and primary keys for fast filtering.

• **Avoid SELECT ***: Always query only required columns to reduce I/O.

• Use LIMIT/OFFSET for testing and sampling.

• Query Profiling: Use EXPLAIN and SYSTEM QUERY LOG to analyze performance.

• Batch Aggregations: Pre-aggregate (daily, hourly) usage data in the ABDR Layer to avoid scanning raw CDRs every time.

Error Handling

• ETL Scripts: Wrap all extraction and load operations in try-except (Python) or equivalent error handling.

• Logging: Capture errors with context (source system, batch ID, time).

• Retry Logic: Implement exponential backoff for transient failures.

• Graceful Shutdown: Ensure DB connections to ClickHouse close cleanly on failure.

Security

• Credential Management: Never hardcode credentials—use environment variables.

• Role-Based Access: Assign least-privilege access to ClickHouse users (etl_user, reporting_user).

• Secure Transport: Enforce TLS/SSL for ClickHouse and source system connections.

• Network Security: Use VPN/private endpoints for inter-database transfers (e.g., CRM → ClickHouse).

• PII Handling: Mask or encrypt subscriber PII (MSISDN, IMSI, account IDs) in Bronze/Silver layers.

Performance

• Batch Inserts: Insert data into ClickHouse in batches (10k–50k rows per insert) for efficiency.

• Connection Pooling: Reuse ClickHouse connections for multiple inserts.

• Memory Efficiency: Use streaming/chunking when extracting from CRM/Billing to avoid OOM with large DataFrames.

• Parallel Processing: Split ingestion by date range, region, or subscriber segments.

• TTL & Partition Pruning: Define retention policies (TTL) in bronze tables to manage data lifecycle.

Monitoring

• ETL Logging: Maintain a log table in ClickHouse with job name, start time, end time, row counts, and status.

• Row Count Validation: Track record counts per batch vs. source to detect drops.

• Ingestion Lag Monitoring: Monitor real-time streams (Kafka → ClickHouse) for latency.

• Alerting: Set up alerts for pipeline failures, row mismatches, or delayed ingestion.