Transactional Data Best Practices

Follow these best practices when working with transactional data that captures changes since the last update.

It is important to build scalable, efficient, and reliable data loading processes for transactional data to ensure historical accuracy and smooth integration.

Building efficient and scalable data loads

Process transactional data instead of full snapshots

As an Embedded Partner, you will handle large data volumes. To improve efficiency, only process transactional data that captures changes since the last update. This reduces the amount of data transferred and processed, making daily data uploads faster and more manageable.

Processing transactional data greatly improves performance, ensuring systems handle daily operations smoothly while using fewer resources. It also allows the tracking of historical changes like job shifts or performance updates without transferring redundant data.

Leverage the efficiency of daily deltas

Daily deltas capture changes since the last upload, making data processing more efficient. This method reduces processing and storage needs by only transferring and storing incremental changes.

Using deltas lowers data storage and processing costs while allowing detailed tracking of job, performance, and compensation changes. This is a key to managing large datasets while maintaining system responsiveness.

Consider the complexity of managing deltas

Implementing deltas requires robust systems to accurately capture and apply changes. The complexity of managing deltas can increase, as errors during processing can lead to data inconsistencies over time. A correction workflow should be in place to address potential issues. Ensuring data integrity is essential. Errors in delta processing can accumulate, so it’s important to have mechanisms for verifying and correcting inconsistencies.

Maintaining historical records

Implement historical data tracking

Whether you are using transactional data or snapshots, always ensure that historical records are maintained. Historical data provides a comprehensive view of changes over time and supports data correction workflows. Design your Extract, Transfer, and Load (ETL) processes and databases to capture and store historical data.

This may include:

• Techniques to detect and capture changes in data sources.
• Database tables designed to store historical data and track changes over time.
• Logs that record changes to data for audit and tracking purposes.

Capturing and storing historical data is critical for auditing, compliance, and understanding trends in key metrics like job performance or compensation. This ensures the ability to track changes accurately over time.

Design ETL processes for scalability

Ensure your ETL processes are built to handle large-scale data processing efficiently. This includes detecting changes in data sources and setting up database structures that can store historical data while scaling with the organization’s needs. As data volumes grow, scalable ETL processes are crucial to maintaining system performance and ensuring seamless data processing during daily operations.

For more information on how to design scalable data systems, see Building for Scale and Data Schema Best Practices.