![]() Tip: While Tableau Desktop has the capability to create joins and do some basic data shaping, Tableau Prep Builder is designed for data preparation. To join data and be able to clean up duplicate fields, use Tableau Prep Builder instead of Desktop Fields used in the join clause cannot be removed without breaking the join.If you change the data type after you join the tables, the join will break. When joining tables, the fields that you join on must be the same data type.To combine published data sources, you must edit the original data sources to natively contain the join or use a data blend. Published Tableau data sources cannot be used in joins.To view, edit, or create joins, you must open a logical table in the relationship canvas-the area you see when you first open or create a data source-and access the join canvas.As such, Improve Performance for Cross-Database Joins may be relevant. For example, a relationship across data sources will produce a cross-database join when the viz uses fields from tables in different data sources. Note: Relationships eventually leverage joins (just behind the scenes). However, there may be times when you want to directly establish a join, either for control or for desired aspects of a join compared to a relationship, such as deliberate filtering or duplication. For more information, see How Relationships Differ from Joins. Relationships are the recommended method of combining data in most instances. Relationships also allow for context-based joins to be performed on a sheet-by-sheet basis, making each data source more flexible. Relationships preserve the original tables’ level of detail when combining information. The default method in Tableau Desktop is to use relationships. Depending on the structure of the data and the needs of the analysis, there are several ways to combine the tables. This can help you proactively address data quality issues.It is often necessary to combine data from multiple places-different tables or even data sources-to perform a desired analysis. Monitor for duplicates: Set up monitoring and alerts to notify you when the number of duplicate rows in your tables exceeds a certain threshold. Schedule regular deduplication: If your use case and data ingestion process are prone to creating duplicate rows, schedule periodic deduplication jobs to maintain data quality and query performance. Create a new table with deduplicated data CREATE TABLE your_table_deduplicated AS SELECT * FROM ( SELECT *, ROW_NUMBER() OVER (PARTITION BY primary_key_column1, primary_key_column2 ORDER BY id) AS row_num FROM your_table ) t WHERE t.row_num = 1 - Replace the original table with the deduplicated table BEGIN DROP TABLE your_table ALTER TABLE your_table_deduplicated RENAME TO your_table COMMIT Use CTAS (Create Table As Select) for large tables: If you need to deduplicate a large table, it might be more efficient to create a new table with deduplicated data and then replace the original table. This query will remove all duplicate rows, keeping only one unique record based on the specified primary key columns. Replace your_table with the table name and primary_key_column1, primary_key_column2 with the columns that together represent the primary key or unique identifier for the rows. WITH duplicates AS ( SELECT id, ROW_NUMBER() OVER (PARTITION BY primary_key_column1, primary_key_column2 ORDER BY id) AS row_num FROM your_table ) DELETE FROM your_table WHERE id IN (SELECT id FROM duplicates WHERE row_num > 1) Use window functions for deduplication: To remove duplicate rows from a table in Redshift, you can use window functions like ROW_NUMBER() in combination with a DELETE statement. You can use these primary keys in your deduplication queries. Use primary keys: Although Redshift doesn't enforce primary key constraints, defining primary keys in your table schema can help you identify duplicate rows easily. This can be done using ETL (Extract, Transform, Load) tools or custom scripts to identify and remove duplicates before importing the data. To deal with duplicate rows in Redshift, follow these best practices:Äeduplicate data before ingestion: If possible, clean and deduplicate your data before loading it into Redshift. Duplicate rows in Amazon Redshift can occur due to various reasons, such as data ingestion issues, application errors, or a lack of primary key constraints.
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