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The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design, development and deployment" of a data warehouse or business intelligence system. [1]
Records life cycle management. A difficult challenge for many enterprises is tied to the tracking of records through their entire information life cycle so that it's clear, at all times, where a record exists or if it still exists at all. The tracking of records through their life cycles allows records management staff to understand when and ...
Agile methodologies don’t assume requirements to be permanent at any stage of the software life cycle. These methods are designed to support sporadic changes in contrast to waterfall design technique. An important part of this approach is iterative development, where the entire software life-cycle is run multiple times during the life of a ...
Formally, a "database" refers to a set of related data accessed through the use of a "database management system" (DBMS), which is an integrated set of computer software that allows users to interact with one or more databases and provides access to all of the data contained in the database (although restrictions may exist that limit access to particular data).
A systems development life cycle is composed of distinct work phases that are used by systems engineers and systems developers to deliver information systems.Like anything that is manufactured on an assembly line, an SDLC aims to produce high-quality systems that meet or exceed expectations, based on requirements, by delivering systems within scheduled time frames and cost estimates. [3]
In modern management usage, the term data is increasingly replaced by information or even knowledge in a non-technical context. Thus data management has become information management or knowledge management.
Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong assumptions about the data which may have a positive impact on performance.
A real-life ETL cycle may consist of additional execution steps, for example: Cycle initiation; Build reference data; Extract (from sources) Validate; Transform (clean, apply business rules, check for data integrity, create aggregates or disaggregates) Stage (load into staging tables, if used) Audit reports (for example, on compliance with ...