If you're working with data in Google BigQuery, you may have encountered duplicated data. This is a problem because it can lead to inaccurate results of analyses.
There are several causes of data duplication in BigQuery, and ETL (extract, transform, load) processes are a common one.
BigQuery was originally designed as an append-only database on the assumption that all data should be "dumped" in the database, then transformed and processed later (to remove duplicates, among other things). However, BigQuery's very favorable price and easy, serverless setup led users to deploy it in cases more suitable for traditional relational databases. Therefore, many issues previously related to technologies such as MySQL, Postgres, and SQL Server started to occur, including data duplication.
Here are 6 tips for preventing data duplicates in your BigQuery tables:
1. Use a unique ID for each row of data. This will ensure that no two rows of data are identical.
2. De-duplicate data before it is loaded into BigQuery. This can be done using a tool like Dataddo, which helps you compose datasets with the granularity you need to prevent duplicities.
3. Use a partitioned table. When you create a partitioned table in BigQuery, each partition is assigned a unique ID. This means that ETL processes can't accidentally load duplicate data into the same partition, which would cause duplicates.
4. Use a clustered table. Clustered tables are similar to partitioned tables, but the data is clustered in a certain column (or columns) instead of being partitioned by a unique ID. This means that ETL processes can't accidentally load duplicate data into the same cluster.
5. Use the "Upsert" mode offered by Dataddo. This mode automatically detects and removes duplicates before they are loaded into BigQuery. In the Dataddo application, you can simply identify which column (or combination of columns) should be unique.
6. Use Dataddo's "Truncate Insert" mode. This mode erases all the data in an existing table prior to an insertion. Use this mode only when you do not need to preserve incremental changes in the data and you are only interested in the current "snapshot."