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 scenarios 1 2 3 4 .

Questions no: 17 Verified Answer : = A. IBM IMS & E. HP NonStop Step by Step Comprehensive and Detailed Explanation with all References : = ARC (Attunity Replicate Connect) CDC agents are used for capturing changes (CDC) and can be utilized with both relational and non-relational endpoints supported by ARC. The endpoints that have ARC CDC agents include:

IBM IMS (A) : This is a database and transaction management system, and it is listed as one of the endpoints supported by ARC CDC agents 1 .

HP NonStop (E) : This is a platform for high-availability servers and is also supported by ARC CDC agents 1 .

The other options provided do not align with the endpoints that have ARC CDC agents:

B. IBMDB2Z/OS : While DB2 for z/OS is a common database system, it is not mentioned in the context of ARC CDC agents.

C. Kafka Source : Kafka is a streaming platform, and while it can be an endpoint for data, it is not listed as having ARC CDC agents.

D. SAPHANA : SAP HANA is an in-memory database, and it is not specified as having ARC CDC agents.

Therefore, the verified answers are A. IBM IMS and E. HP NonStop , as they are the endpoints that utilize ARC CDC agents for capturing changes 1 .

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

Questions no: 21 Verified Answer : = C. As close as possible to the source system

Step by Step Comprehensive and Detailed Explanation with all References : = In an on-premises environment, Qlik Replicate should be set up as close as possible to the source system. This is because the source system is where the initial capture of data changes occurs, and having Qlik Replicate close to the source helps to minimize latency and maximize the efficiency of data capture.

C. As close as possible to the source system : Positioning Qlik Replicate near the source system reduces the time it takes to capture and process changes, which is critical for maintaining low latency in replication tasks 1 .

The other options are not recommended because:

A. As close as possible to the target system : While proximity to the target system can be beneficial for the apply phase, it is more crucial to have minimal latency during the capture phase, which is closer to the source.

B. In the “middle” between the source and target : This does not provide the optimal configuration for either the capture or apply phases and could introduce unnecessary complexity and potential latency.

D. In a cloud environment : This option is not relevant to the question as it specifies an on-premises setup. Additionally, whether to use a cloud environment depends on the specific architecture and requirements of the replication scenario.

For detailed guidance on setting up Qlik Replicate in an on-premises environment, including considerations for placement and configuration to optimize performance and reduce latency, you can refer to the official Qlik Replicate Setup and User Guide 1 .

 The goal is to convert a timestamp to Unix time (seconds since January 1, 1970).

 The strftime function is used to format date and time values.

 To get the Unix epoch time, you can use the command: strftime('%s',SAR_H_COMMIT_TIMESTAMP) - strftime('%s','1970-01-01 00:00:00') .

 This command extracts the Unix time from the timestamp and subtracts the Unix epoch start time to get the number of seconds since January 1, 1970. This is consistent with the Qlik Replicate documentation and SQL standard functions for handling date and time conversions.

To convert a timestamp to Unix time (also known as Unix epoch time), which is the number of seconds since January 1st, 1970, you can use the strftime function with the %s format specifier in Qlik Replicate. The correct syntax for this conversion is:

strftime('%s', SAR_H_COMMIT_TIMESTAMP) - strftime('%s','1970-01-01 00:00:00')

This function will return the number of seconds between the SAR_H_COMMIT_TIMESTAMP and the Unix epoch start date. Here’s a breakdown of the function:

strftime('%s', SAR_H_COMMIT_TIMESTAMP) converts the SAR_H_COMMIT_TIMESTAMP to Unix time.

strftime('%s','1970-01-01 00:00:00') gives the Unix time for the epoch start date, which is 0 .

Subtracting the second part from the first part is not necessary in this case because the Unix epoch time is defined as the time since 1970-01-01 00:00:00 . However, if the timestamp is in a different time zone or format, adjustments may be needed.

The other options provided do not correctly represent the conversion to Unix time:

Options A and B use datetime instead of strftime , which is not the correct function for this operation 1 .

Option C incorrectly includes < code > datetime.datetime < /code > , which is not a valid function in Qlik Replicate and seems to be a mix of Python code and SQL 1 .

Option E uses Time.now.strftime , which appears to be Ruby code and is not applicable in the context of Qlik Replicate 1 .

Therefore, the verified answer is D , as it correctly uses the strftime function to convert a timestamp to Unix time in Qlik Replicate 1 .

For Qlik Replicate’s Change Data Capture (CDC) process, the valid source endpoint types include:

A. Classic Relational RDBMS : These are traditional relational database management systems that support CDC. Qlik Replicate can capture changes from these systems using log-based CDC tools which are integrated to work with most ETL tools 1 .

C. SAP ECC and Extractors : SAP ECC (ERP Central Component) and its extractors are also supported as source endpoints for CDC in Qlik Replicate. This allows for the replication of data changes from SAP’s complex data structures 1 .

The other options provided are not typically associated with CDC in Qlik Replicate:

B. MS Dynamics direct access : While Qlik Replicate can connect to various data sources, MS Dynamics is not commonly listed as a direct source for CDC.

D. Generic REST APIs Data Lake file formats : REST APIs and Data Lake file formats are not standard sources for CDC as they do not maintain transaction logs, which are essential for CDC to track changes.

For detailed information on setting up source endpoints and enabling CDC, you can refer to the official Qlik documentation and community articles that discuss the prerequisites and configurations needed for various source endpoints 2 3 4 5 .

For replicating columns that contain large PDFs, the appropriate data type to use in Qlik Replicate is:

D. BLOB : This stands for Binary Large Object and is the data type used for storing large binary data such as PDF files. The BLOB data type is suitable for handling the size and binary nature of PDF files 1 .

The other options are not suitable for the following reasons:

A. DLOB : This is not a recognized data type in Qlik Replicate.

B. NCLOB : NCLOB, or National Character Large Object, is used for storing large text data in the national character set and is not optimal for binary data like PDFs.

C. NVARCHAR(1000) : NVARCHAR is a character data type with a specified length, which would not be suitable for large binary objects like PDFs due to size limitations and the nature of the data.

For more information on data types and their usage in Qlik Replicate, you can refer to the official Qlik Replicate documentation on Replicate data types .