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.
No comments:
Post a Comment