The most efficient method is to exclude unneeded columns at the source using QREP’s column filtering. This reduces the volume of data captured from the transaction log, minimizes network transfer, avoids consuming unnecessary storage and compute resources in Snowflake, and simplifies the target table structure from the start.

Increasing commit frequency forces the task to commit more often to the target, which can create significant I/O overhead and actually slow down overall throughput. A better approach is often to decrease commit frequency (allowing more changes to be grouped per transaction) and increase the ‘Transaction Size’ setting, which lets QREP process a larger batch of changes from the log before applying, improving efficiency.

For initial/full loads, the ‘Fetch Buffer Size’ determines how much data is read from the source into memory at one time, and the ‘Apply Buffer Size’ determines how much data is held in memory before being written to the target. Tuning these buffers is crucial for managing memory footprint and optimizing load performance.

Qlik Replicate provides the system variable `$QREP_TIMESTAMP`, which represents the timestamp when the change was processed by the Replicate server. A User-Defined Transformation (UDT) is the correct method to add a new column to the target data flow and populate it with this value, effectively capturing the replication apply time.

Avro is a binary row-based format that is highly efficient (smaller payload than JSON), includes a rich schema that travels with the data, and supports robust schema evolution. These characteristics make it an industry-standard choice for streaming data pipelines with Kafka, balancing performance, flexibility, and compatibility.

For QREP to leverage native database partitioning for parallel initial load, it must first be able to automatically detect the partition key and its values. This is typically supported for range partitions on numeric or date columns. QREP can then spawn multiple threads to load different partitions concurrently, significantly improving performance.

Duplicate key errors during CDC often occur after a task pause, stop, or server restart if bookmark management isn’t resilient. Enabling ‘Bookmark Resilience’ ensures QREP stores its read position more robustly, preventing it from re-reading already-processed log entries and attempting to re-apply inserts, which manifest as duplicate key errors.

A disparity where ‘Changes Read’ increases but ‘Changes Applied’ remains at zero while the task is running is a classic sign of a target endpoint issue. It means QREP is successfully capturing changes but is blocked from applying them—common causes include target network connectivity problems, authentication failures, target table schema mismatches, or permission issues.

A Lookup transformation within the QREP task is the ideal solution. It allows you to perform a real-time join between the change data stream and the reference (metadata) table based on a key like the source table name. This enriches the data *during* replication, ensuring the classification is embedded in every record as it lands in the target, meeting the compliance requirement at the point of ingestion.

The most efficient method is to exclude unneeded columns at the source using QREP’s column filtering. This reduces the volume of data captured from the transaction log, minimizes network transfer, avoids consuming unnecessary storage and compute resources in Snowflake, and simplifies the target table structure from the start.0