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Data Warehousing Lifecycle

Can you describe data warehousing lifecycle?
Tell us what you know about data warehousing lifecycle?
What are the processes and phases in data warehousing lifecycle?

These are fundamental questions to test your knowledge of data warehousing. To answer the questions, just briefly explain each phase of the lifecycle:

1. Data acquisition processes—extract, transform, load (ETL)

The processes include sourcing, cleansing, transforming, and aggregating data using parallel technology tools to build industrial-strength ETL processes that accommodate high data volumes from disparate sources. We identify the best sources for data elements, reconstructing data when required and deploying the most appropriate tools to retrieve the data from its primary sources. Through the cleansing process, we enhance data quality by ensuring data accuracy, type, and consistency, as well as eliminating duplicate records.

2. Data repositories

In the phase you need to build a variety of data repositories, including operational data stores, data marts, data warehouses, web warehouses, and data hubs. You start by implementing and properly documenting a physical data model, ensuring data from all functional areas is sufficiently integrated to support cross-functional analyses. Then you perform database tuning, model denormalization, and aggregation as necessary to support information delivery requirements. When scalability requirements call for it, you partition and distribute data into a parallel architecture.

3. Information delivery applications

You implement information delivery applications that allow corporate users to access the data in the warehouse. These include decision support tools, data mining and analytic tools, and applications that optimize supply chain, campaign management, billing, and industry-specific processes. In this phase you also ensure near- and long-term reporting and access requirements are met as well. These may include fixed-frequency static reports; ad-hoc reports; dynamic, multidimensional queries; Internet/intranet application interfaces; and data mining.

4. Data warehouse administration

As the data warehouse grows, administration (or management) of the repository is a crucial step in optimizing results and return on investment. You provide data warehousing administration services such as performance analysis, user analysis, benchmarking, auditing, and tuning to help clients measure the ongoing success of their data strategies.

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