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Data Duplication: Understanding and Resolving Common Issues
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Data duplication can be a significant issue in data management, leading to inaccuracies and inefficiencies. Here's how you can identify and resolve common causes of data duplication.
For data storages:
By carefully reviewing these aspects, you can significantly reduce or eliminate data duplication, ensuring the accuracy and reliability of your data management process.
Check the Records in Your Source
Duplicate records often stem from source data anomalies. For instance, if a record includes multiple values for typically singular fields (like two email addresses or phone numbers), it can result in duplication. The second record, even though it contains alternate values, might appear as a duplicate in your table.
Check Your Snapshotting Policy
Example: With a date range set to the last 7 days and a daily sync frequency, the system will insert the last 7 days' data every day, potentially resulting in data being replicated sevenfold.
To optimize snapshotting:
- For storages, databases, warehouses: Opt for Replace as the snapshotting policy.
- For dashboarding apps: Append might be more suitable.
Check Your Date Range
The date range in source creation isn't for loading historical data. Typically, it's best to set it to Yesterday to pull the previous day’s data daily.
For loading data beyond this range, like the last 30 days, create a source with the range set to yesterday. Then use manual data load for the additional days. Note that selecting Append and keeping all snapshots can create duplications.
Data Storages: Write Modes and Unique Columns
Check Your Write Mode
The insert write mode in your destination can lead to duplicates. An alternative is the upsert write mode, which updates existing records and inserts new ones.
Check Your Unique Columns for Upsert
When using upsert write mode, it's crucial to choose the right unique columns. Incorrect selection can lead to repeated insertion of records.
Imagine you have a database of customer interactions.
1. Scenario with 'Customer ID' as Unique Column
In this case,
Customer ID is a stable and unique identifier for each customer. When new data comes in, the upsert process checks if a record with the same
Customer ID already exists. If it does, the system updates that existing record with the new information. If not, it inserts a new record. This approach effectively avoids duplication because
Customer ID uniquely identifies each customer.
2. Scenario with 'Login Time' as Unique Column
Login Time is used as the unique identifier. However,
Login Time varies with each login, meaning it's unlikely to be the same across multiple records. As a result, the upsert process may not find a matching
Login Time in the existing records. This leads to the system treating most incoming data as new information and creating new records each time, thus causing data duplication. It fails to recognize and update existing customer records because
Login Time does not serve as a consistent and unique identifier like
Therefore, choosing a unique column like
Customer ID, which consistently and uniquely identifies each record, is crucial in the upsert process to ensure data is accurately updated without duplication.