Future of Data Warehousing: One Thing at a Time Principle Know When to Begin Principle Know When to End Principle Large to Medium to Small Principle Stage Data Integrity Principle Name the Data Principle Own the Data Principle Build the Data Principle Type the Data Principle Request an e-inspection copy.
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Database Development and Management. Database and Applications Security: Current—and, in my view, short-sighted—market thinking is that a data lake filled with every conceivable sort of raw, loosely managed data will address these needs.
Data warehouse - Wikipedia
That approach may work for non-critical, externally sourced social media and Internet of Things data. In fact, it may be argued that these characteristics are even more important in the maintenance phase than in the earlier ones of data warehouse development. One explicit design point of the Data Vault data model is agility. A key differentiator between Hub, Link, and Satellite tables is that they have very different usage types and temporal characteristics.
In effect, the data warehouse is structured according to good engineering principles, while the data marts flow with user needs. This structuring enables continuous iteration of agile updates to the warehouse, continuing through to the marts, by reducing or eliminating rework of existing tables when addressing new needs.
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The engineered components and methodology of the Data Vault approach are particularly well-suited to the application of DWA tools, as we saw in the design and build phases. However, it is in the maintain phase that the advantages of DWA become even more apparent.
- Setting up and managing a data warehouse.
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Widespread automation is essential for agility in the maintenance phase, because it increases developer productivity, reduces cycle times, and eliminates many types of coding errors. This metadata plays an active role in the development and runtime processes of the data warehouse and marts and is thus guaranteed to be far more consistent and up-to-date than typical separate and manually maintained metadata stores such as spreadsheets or text documents.
The number differs based on the database software being used.
How to Maintain a Data Warehouse
Transform data by combining multiple fields into one field; breaking down data fields into separate fields based on year, month and day; mapping related data sets into a single representation; and applying surrogate keys to dimension table records. Keep users of the warehouse well-trained on how to create queries, access database structures and handle unexpected contingencies. Also, review all warehouse changes with staff members before applying them, because the changes might negatively affect a user's ability to acquire the information he needs.
Update statistic files after you load new data rebuild an index.
Also, run periodic updates either weekly or monthly. Keeping your statistic files updated will optimize query transactions, which in turn will enhance overall performance speed.