Below is in detailed Difference between Joiner and Lookup Transformation. Definition, Purpose, Characteristics, Examples
| Joiner Transformation | Lookup Transformation |
| Definition: The Joiner transformation is an active transformation that joins two heterogeneous sources based on a join condition. | Definition: The Lookup transformation is a passive transformation that looks up data in a relational table, view, or synonym and retrieves relevant data based on a lookup condition. |
| Purpose: To join data from different databases or flat files. | Purpose: To fetch additional information from a lookup table and add it to the data flow. |
| Characteristics: 1. Can join data from different types of sources (e.g., relational tables, flat files). 2. Supports various join types: Normal, Master Outer, Detail Outer, and Full Outer joins. 3. Can perform complex joins involving multiple columns and conditions. 4. Requires two input pipelines (master and detail sources). | Characteristics: 1. Can be connected or unconnected (connected means it receives data directly from the pipeline, unconnected means it is called as a function within another transformation). 2. Typically used for lookups to enrich or validate data. 3. Can perform both dynamic and static lookups. 4. Supports caching to improve performance by storing the lookup table data in memory. |
| Examples: – Joining a customer table from an Oracle database with an order table from a flat file. – Performing a full outer join to get all rows from both sources regardless of match. | Examples: – Looking up employee names based on employee IDs from a different table. – Validating a product code by looking up a product catalog table. |
Joiner and Lookup Transformation | Key Differences
| Joiner Transformation | Lookup Transformation |
| Joiner: Used to join two data sources into a single result set. | Lookup: Used to retrieve related data from a lookup table and improve the current data flow. |
| Joiner Transformation | Lookup Transformation |
| Joiner: Performs joins similar to SQL joins, combining rows from two different sources. | Lookup: Acts like a dictionary, where each row from the input is used to perform a lookup and fetch related data. |
| Joiner Transformation | Lookup Transformation |
| Joiner: Can join data from different types of sources (heterogeneous sources). | Lookup: Typically uses a relational table, view, or synonym for lookups. |
| Joiner Transformation | Lookup Transformation |
| Joiner: May require careful performance tuning, especially with large datasets, due to the need to handle and match all rows from both sources. | Lookup: Can leverage caching to improve performance by storing the lookup table data in memory, reducing database hits. |
| Joiner Transformation | Lookup Transformation |
| Joiner: Supports multiple join types (Normal, Master Outer, Detail Outer, Full Outer). | Lookup: Primarily uses equality conditions for lookups but can also use non-equality conditions. |
| Joiner Transformation | Lookup Transformation |
| Joiner: Requires two input sources and combines them into one output stream. | Lookup: Uses one input stream and adds additional columns to it based on the lookup results. |
| Joiner Transformation | Lookup Transformation |
| Joiner Transformation: When you need to combine data from two different sources into a single result set, especially when the sources are heterogeneous or when different types of joins are needed. | Lookup Transformation: When you need to fetch additional data to enrich your dataset or validate it against a reference table, typically within the same relational database. |
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