When merging databases, especially of such large sizes (12 TB and 15 TB), it is crucial to ensure data integrity and consistency. The recommended approach is to:

Stop the Replication Task: This is important to prevent any changes from being replicated to the target while the IBM load process is ongoing.

Perform the IBM Load: Execute the IBM load to merge the database into the existing database.

Resume the Replication Task: Once the IBM load has been successfully completed, the replication task can be resumed.

This approach ensures that the data loaded via IBM load is not missed or duplicated in the target database. It also allows Qlik Replicate to continue capturing changes from the point where the task was stopped, thus maintaining the continuity of the replication process.

It's important to note that creating a new task after the IBM load (Option D) could lead to complexities in managing the data consistency and might require additional configuration. Continuing to run the task (Option C) could result in conflicts or data integrity issues during the load process. Therefore, Option B is the safest and most reliable approach to ensure a smooth merge of the databases.

For further details and best practices, you can refer to the official Qlik Replicate documentation and support articles which provide guidance on similar scenarios1234.

Qlik Enterprise Manager provides a centralized command center to configure, execute, and monitor data replication and transformation tasks across the enterprise. It is specifically designed to manage and control Qlik Replicate and Qlik Compose tasks. Additionally, it integrates with Qlik Catalog to automatically catalog data assets generated by Qlik Replicate directly in Qlik Catalog1. This integration allows for tracking end-to-end data lineage, which improves compliance, governance, and trust in the data assets managed within Qlik Catalog1.

The documentation clearly states that Qlik Enterprise Manager is used to design, execute, and monitor Qlik Replicate and Qlik Compose tasks, and it also mentions the integration with Qlik Catalog for data asset management2. However, there is no mention of Qlik Sense being controlled by Qlik Enterprise Manager. Qlik Sense is a separate product for data visualization and analytics, and its management is not within the scope of Qlik Enterprise Manager's functionalities as described in the available resources12.

Therefore, the correct answer is C. Qlik Replicate, Qlik Compose, Qlik Catalog, as these are the components that can be controlled with Qlik Enterprise Manager.

To identify the internal commands that Qlik Replicate is executing prior to an error, the Trace logging level should be used. This level provides detailed information about the operations being performed by Qlik Replicate, including the internal commands executed before an error occurs1.

Here's how the Trace logging level works in Qlik Replicate:

When logging is set to Trace, the log lines are identified with ]T:. This indicates that the log will include detailed trace information about the internal workings of Qlik Replicate, such as sending control records to components or waiting for termination of threads1.

The Trace level is more detailed than Warnings (]W:) and Errors (]E:), which only show warning and error messages without the detailed context of the operations leading up to them1.

The Trace level is also distinct from Verbose (]V:), which provides even more detailed logging information but may not be necessary for identifying the commands leading up to an error1.

Therefore, the correct answer is C. Trace, as it is the appropriate logging level to use when you need to analyze the actions performed by Qlik Replicate just before an error occurs1.

To run a Qlik Replicate Task at a high-performance level, the main hardware components that are recommended include:

Cores: A higher number of cores is beneficial for handling many tasks running in parallel and for prioritizing full-load performance1.

SSD (Solid State Drive): SSDs are recommended for optimal performance, especially when using a file-based target or dealing with long-running source transactions that may not fit into memory1.

Network bandwidth: Adequate network bandwidth is crucial to handle the data transfer requirements, with 1 Gbps for basic systems and 10 Gbps for larger systems being recommended1.

The other options do not encompass all the recommended hardware components for high-performance levels in Qlik Replicate tasks:

A . SSD, RAM: While these are important, they do not include the network bandwidth component.

B . Cores, RAM: This option omits the SSD, which is important for disk performance.

D . RAM, Network bandwidth: This option leaves out the cores, which are essential for processing power.

For detailed hardware recommendations for different scales of Qlik Replicate systems, you can refer to the official Qlik documentation on Recommended Hardware Configuration.

Qlik Replicate offers several methods for parallel load during a full load process to accelerate the replication of large tables by splitting the table into segments and loading these segments in parallel. The three primary ways Qlik Replicate allows parallel loading are:

Use Data Ranges:

This method involves defining segment boundaries based on data ranges within the columns. You can select segment columns and then specify the data ranges to define how the table should be segmented and loaded in parallel.

Use Partitions - Use all partitions - Use main/sub-partitions:

For tables that are already partitioned, you can choose to load all partitions or use main/sub-partitions to parallelize the data load process. This method ensures that the load is divided based on the existing partitions in the source database.

Use Partitions - Specify partitions/sub-partitions:

This method allows you to specify exactly which partitions or sub-partitions to use for the parallel load. This provides greater control over how the data is segmented and loaded, allowing for optimization based on the specific partitioning scheme of the source table.

These methods are designed to enhance the performance and efficiency of the full load process by leveraging the structure of the source data to enable parallel processing