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Data Life Cycle Management

Today's database applications frequently require scalability and rapid roll-in and roll-out of data with minimal disruption to data access by applications. Roll-in of data refers to the addition of new data as it becomes available while roll-out of data refers to the moving out (usually archiving) of historic data. Many applications today are accessed 24 X 7, therefore eliminating the previously available batch window for data updates. Also, many applications require the continuous feed of data updates while applications concurrently access the data.

The DB2 database system provides a variety of facilities that enable scalability and facilitate the continuous feed or roll-in and roll-out of data, with minimal interruption of data access. This document recommends best practices to design and implement these DB2 facilities to achieve these goals.

Data Life Cycle Management

Table of contents

Best Practice - Data Life Cycle Management - 01. Introduction

Best Practice - Data Life Cycle Management - 02. Partitioning techniques

Best Practice - Data Life Cycle Management - 03. Multi-dimensional clustering

Best Practice - Data Life Cycle Management - 04. Using database partitioning, table partitioning and multi-dimensional clustering in the same database design

Best Practice - Data Life Cycle Management - 05. Additional techniques to support life cycle management

Best Practice - Data Life Cycle Management - 06. Designing and implementing your table partitioning strategy

Best Practice - Data Life Cycle Management - 07. Rolling in data (Which solution to use?)

Best Practice - Data Life Cycle Management - 08. Roll-in of compressed table partitions

Best Practice - Data Life Cycle Management - 09. Roll-in and roll-out with continuous updates

Best Practice - Data Life Cycle Management - 10. Post roll-out data growth and retention management

Best Practice - Data Life Cycle Management - 11. Conclusion

Best Practice - Data Life Cycle Management - 12. Best Practices Summary