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In our recent projects, we encountered significant issues related to data synchronization across multiple business units. Different departments, each managing their own data structures, often introduced new columns or modified existing ones in their tables. These frequent changes led to numerous challenges:
- Inconsistent Data Integration: The varying schemas created difficulties in integrating data consistently across different systems. This inconsistency led to errors and confusion when trying to aggregate or analyze data from multiple sources.
- Manual Synchronization Challenges: The manual process of updating target tables and merging data was cumbersome and error-prone.
- Reporting Delays: Inaccurate or outdated data due to synchronization issues caused delays in generating timely and reliable reports.
To address these challenges and improve our data integration processes, we developed a set of dynamic procedures designed to handle schema changes and automate data synchronization efficiently. These procedures were built to adapt to any business needs and provide robust solutions for dynamic data management.
Objective: Develop a flexible and automated solution that can dynamically handle schema changes and synchronize data across various departments or business units.
We designed two core procedures to resolve these issues:
- Dynamic Table Creation/Modification Procedure (
PROCESS_AND_SYNC_DATA) - Dynamic Data Merge Procedure (
DYNAMIC_MERGE)
Technical Details:
Dynamic Table Creation/Modification Procedure
Procedure Name: PROCESS_AND_SYNC_DATA
Purpose: This procedure creates or updates the target table schema dynamically based on the source table’s structure and performs data synchronization.
Steps:
- Identify Target Table: Appends ‘_TGT’ to the input table name to determine the target table name.
- Fetch Schema Information: Retrieves column names and data types from the source table.
- Create or Update Target Table:
- Create Table: If the target table does not exist, it creates it with columns matching the source table.
- Update Table: If the target table exists, it alters the table to add any new columns from the source table.
- Call Data Merge: Invokes the
DYNAMIC_MERGEprocedure to synchronize data from the source table to the target table.
Dynamic Data Merge Procedure
Procedure Name: DYNAMIC_MERGE
Purpose: This procedure handles the data merge operation, ensuring that records from the source table are appropriately inserted into the target table based on a dynamic schema.
Steps:
- Retrieve Column Information: Dynamically fetch column names from the source table.
- Construct Merge Query: Build a dynamic SQL merge query to handle inserts and updates based on hash values of the columns.
- Execute Merge Query: Perform the merge operation to synchronize data between the source and target tables.
Through the implementation of these dynamic procedures, our project effectively addressed the challenges of data synchronization and schema management, providing a robust and scalable solution for diverse business requirements.
