Business needs: Organizations need to retain OLTP data for a sufficient period to support business operations, such as financial reporting, customer service, and fraud detection. In retail, the enterprise may retain online sales transaction data in hot storage to train machine learning models that compute demand forecasts or to generate comparative business intelligence reports.
Storage costs: Storing large volumes of OLTP data can be expensive. Implementing a tiered storage approach, expensive storage as it ages, can optimize costs. Consider hot and cold storage options to move the data in frequent use. Use data table compression techniques to minimize storage costs for less frequently used data.
Data security: OLTP data often contains sensitive spain rcs data information, such as customer details or financial transactions. Robust security measures, including encryption and access controls, are essential to protect this data from unauthorized access. This consideration is most important when there is secondary storage for infrequent data.
Create buckets: When you are dealing with large troves of transactional data, it’s advisable to divide transactional data into different buckets to group data by retention windows and ease maintenance. For example, creating buckets of reporting and non-reporting data can ensure data is present for future audits.
Purging and configuration management: Enterprises purge transactional data periodically using automated methods to maintain the ongoing active data window. Creating configurations or parameterizations for each functional domain or type of data can help develop individual or group retention swim lanes. For example, sales, inventory and order data can exist as one group, whereas pricing, cost, and financial data can be defined as another group.