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How to Create Chunks Using HVR (High Volume Replicator) Tool

Data integration is a critical process in modern enterprises as it enables the seamless flow of data between different systems and databases. With increasing data volumes and the need for real-time or near real-time data replication, businesses are adopting data replication solutions like HVR (High Volume Replicator) to ensure efficient and reliable data movement.


HVR is a popular data replication tool known for its high-performance capabilities, flexibility, and ease of use. One of its essential features is the ability to create “chunks” for data replication. Chunks are subsets of data that HVR uses to replicate changes from the source to the target systems. By creating chunks strategically, you can optimize data replication and minimize the impact on source systems during the extraction process.

In this article, we’ll explore how to create chunks using HVR to improve data replication efficiency:

  1. Understanding Chunks:
    Chunks in HVR are logical divisions of data based on certain criteria, such as the primary key or a range of values. HVR replicates data in these smaller, manageable portions rather than replicating the entire dataset every time changes occur. This approach reduces the load on source systems, minimizes network utilization, and enhances the overall data replication performance.
  2. Identifying Key Columns:
    To create chunks effectively, you need to identify appropriate key columns in your source data. Key columns are used to uniquely identify records and define the boundaries of each chunk. Common examples of key columns are primary keys, timestamps, or any other columns that ensure uniqueness and consistent ordering.
  3. Configuring Chunk Tables:
    In HVR, you configure chunk tables that define the chunking strategy. These chunk tables specify how to divide the data based on the identified key columns. HVR offers various chunking modes, such as “Divide by Value,” “Divide by Rowcount,” and “Divide by Range.” The appropriate mode depends on your data characteristics and the defined key columns.
  4. Setting Chunk Sizes:
    Depending on the selected chunking mode, you can specify the chunk size. For example, in “Divide by Rowcount,” you can set a specific number of rows per chunk. In “Divide by Range,” you define a range of values for each chunk. Carefully choosing the chunk size is crucial as it impacts replication performance and resource utilization.
  5. Defining Chunk Filters:
    Chunk filters are additional criteria you can apply to further segment data into logical subsets. By applying filters, you can replicate only the relevant data, reduce the load on the target system, and minimize storage requirements.
  6. Handling Large Objects (LOBs):
    If your dataset contains large objects like images, videos, or documents, you must handle them appropriately while creating chunks. HVR provides options to either include the LOBs in every chunk or replicate them separately in special LOB chunks, depending on your replication needs.
  7. Testing and Optimization:
    Before deploying your chunking strategy in a production environment, thoroughly test it with representative datasets. Monitor performance and make necessary adjustments to optimize chunking and replication efficiency.
  8. Monitoring and Maintenance:
    Once your HVR replication environment is up and running with chunks, establish regular monitoring and maintenance practices. Keep an eye on chunk sizes, performance metrics, and data consistency to ensure a smooth and reliable replication process.

HVR offers an excellent solution for data replication, and its chunking capabilities play a significant role in optimizing the process. By understanding your data, identifying key columns, and configuring well-designed chunks, you can enhance data replication performance, reduce the impact on source systems, and achieve near real-time data synchronization between source and target databases. Efficient data replication is critical for data-driven decision-making, and with HVR and well-crafted chunks, businesses can gain a competitive edge in today’s data-driven world.

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Supplemental Logging Access & Importance in Data Management

Supplemental Logging Access and Its Importance in Data Management

In the realm of data management, ensuring accurate and efficient logging is crucial for maintaining data integrity and facilitating various operations. One aspect of logging that plays a significant role is supplemental logging access. In this article, we will delve into the concept of supplemental logging access and explore why it is needed in data management processes. By understanding its importance, businesses can optimize their data operations and streamline their overall workflow.

  1. Defining Supplemental Logging Access:

Supplemental logging access refers to the capability to capture additional information beyond the default logging parameters in a database management system. It allows for the collection of granular data, including changes made to specific columns, row identities, and other transactional details. Supplemental logging access provides a comprehensive audit trail, making it easier to track and analyze data modifications for various purposes.

  1. Enhanced Data Recovery and Replication:

One of the primary reasons why supplemental logging access is essential is its role in facilitating efficient data recovery and replication processes. By capturing detailed information about changes made to the database, organizations can accurately recreate and restore data in the event of system failures or data corruption. Additionally, this granular logging helps streamline data replication across multiple systems, ensuring consistency and integrity.

  1. Simplified Change Data Capture (CDC):

Change Data Capture (CDC) is a vital technique in data integration and synchronization, particularly in environments where real-time data replication is necessary. Supplemental logging access plays a crucial role in CDC by providing the necessary data points to track and capture changes in a database. This enables efficient and accurate synchronization between source and target systems, minimizing data discrepancies and improving overall data quality.

  1. Auditing and Compliance Requirements:

In today’s regulatory landscape, businesses must adhere to various compliance standards and regulations. Supplemental logging access helps meet these requirements by providing a comprehensive audit trail of data modifications. It allows organizations to demonstrate accountability, traceability, and compliance with regulatory guidelines. Whether it’s for financial audits, data privacy regulations, or internal governance, supplemental logging access ensures transparency and assists in fulfilling compliance obligations.

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  1. Efficient Data Analysis and Reporting:

Detailed logging provided by supplemental logging access can be leveraged for in-depth data analysis and reporting purposes. By capturing comprehensive information about data changes, businesses can gain valuable insights into patterns, trends, and anomalies. This enables better decision-making, identification of performance issues, and optimization of data management strategies. With accurate and comprehensive data, organizations can drive business growth, improve operational efficiency, and identify areas for improvement.


Supplemental logging access is a vital component in data management processes, offering enhanced logging capabilities beyond default parameters. Its importance lies in facilitating efficient data recovery, replication, change data capture, auditing, compliance, and data analysis. By leveraging supplemental logging access, organizations can ensure data integrity, streamline operations, and meet regulatory requirements. Incorporating this logging capability in your data management strategy will contribute to better decision-making, improved business performance, and a solid foundation for future growth.

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