Loading Using the Agent FAQS

1. What is the role of the run-time agent in the loading process?
•  The run-time agent is responsible for reading a result set from the source server and writing this data to the loading table in the staging area. It uses JDBC for both reading from the source and writing to the staging area.
2. How does the run-time agent fetch data from the source?
•  The run-time agent fetches data from the source by executing a SELECT command on the source server. This command retrieves the necessary data for processing.
3. How is data written to the staging area?
•  After reading the data, the agent writes the data to the staging area by executing an INSERT command on the loading table in the staging area, ensuring the data is loaded correctly.
4. What are the key features used in this method for data processing?
•  The key features used are:
• Array fetch: Reads data in batches but processes each row individually.
• Batch update: Writes data row-by-row in batches for more efficient insertion.
5. Is this method efficient for large volumes of data?
•  No, this method is not ideal for large volumes of data because it processes data row-by-row, which can lead to slower performance for large datasets. While array fetch and batch update improve efficiency, it may still be resource-intensive for large data transfers.
6. Can this method be used for both small and large datasets?
•  This method is better suited for smaller to medium-sized datasets. For large datasets, more efficient loading methods or techniques may be required to handle the volume and performance demands.
7. How does the array fetch feature work?
•  The array fetch feature allows data to be retrieved in batches from the source, but it processes each row individually. This method reduces the overhead of querying each row individually but still processes them one at a time.
8. How does the batch update feature improve data writing?
•  The batch update feature improves efficiency by allowing the agent to insert multiple rows at once. It processes and inserts data row-by-row, but batches multiple rows together to reduce the number of database operations needed.
9. Can the SELECT and INSERT commands be customized?
•  Yes, the SELECT and INSERT commands can be customized within the knowledge module (KM) to suit the specific data requirements for the mapping and integration process.
10. Are there any alternatives for loading large volumes of data?
•  For large volumes of data, other more efficient methods, such as parallel processing or using specialized bulk loading techniques, may be required to optimize performance.