Deduplicating data is a crucial step in maintaining the integrity and efficiency of your data warehouse. This document provides an overview of why deduplication is a common focus, discusses areas where duplicates typically occur and covers best practices for handling duplicates during data ingestion and transformation.

Why Deduplication is Common and Important

Data deduplication is essential because it ensures the accuracy and reliability of your data. Duplicate records can lead to incorrect analysis, inflated storage costs, and inefficient processing. Common reasons for duplicates include joining data sources not being handled well, due to misunderstanding of how to join the data, and duplicates being introduced at source often through low quality data or incremental load challenges.

Ingest

Handling Incremental Data Loads

Connectors with Incremental Support:

Kleene connectors often handle deduplication when sources support incremental data loads. This means they can efficiently update the warehouse with only the new or changed records, reducing the risk of duplicates.

Bias Towards Safety in Ingests:

When sources do not support incremental updates well, ingestion processes tend to bias towards ensuring inclusion of all records by loading overlapping periods of time with existing datasets. This approach can introduce duplicates which should be handled by transforms.

Partial Incremental Support:

Some sources support incremental aspects for new records but not for updates on historical records. Users might want to track these state changes over time, necessitating a Slowly Changing Dimension (SCD) using a snapshot approach to capture the history accurately.

Event Streams and CDC Functionality

Event streams, including tools like Kleene's Change Data Capture (CDC), provide granular changes in data. Sometimes, you need to create a projection to resolve the current state from underlying event data. This is similar to deduplication but focuses on reconstructing the data as it appeared at a specific time.

Transforms

Snapshots and State Changes

Using Snapshots for Sources which don't support incremental loads:
When sources do not support updates, full data loads can be used to create snapshots. By comparing these snapshots, you can extract state changes and implement Slowly Changing Dimensions (SCDs) to maintain historical accuracy.

Preventative Deduplication

Avoiding Lazy Deduplication:

Analysts sometimes use "DISTINCT" or "GROUP BY" on all columns to prevent duplicates. While this ensures that duplicates are handled, it is a lazy practice that can introduce inefficiencies in large datasets. It adds unnecessary compute steps and makes root cause analysis difficult.

Better Practice for Deduplication:

Instead of preventative deduplication, implement tests to catch issues as they arise. This approach is more efficient and allows for easier root cause analysis. If resilience is critical, add tests before applying any deduplication steps.

Strategies to Reduce Data Volumes Processed During Deduplication

Incremental Processing:

Process only the new or changed data rather than reprocessing the entire dataset. This can significantly reduce the volume of data processed.

Partitioning:

Use partitioning strategies to isolate data that is likely to contain duplicates. This helps in reducing the scope of the deduplication process.

Indexing:

Apply indexing on key columns that are used for deduplication. This speeds up the identification of duplicates.

Data Sampling:

For large datasets, consider sampling the data to identify patterns of duplication before applying deduplication to the entire dataset.