Data Warehouse Architecture Properties

A data warehouse system must meet the following architecture features:

 We sometimes wish to keep analytical and transactional processing as far away as possible.

 The scalability of the solution should be demonstrated by the ability to process a huge volume of data and
stream it to different destinations, at high speed, in various formats. The data stream should be processed
and presented in the required format, at the right time and location, with the minimum impact to the existing
infrastructure. The data stream must be protected and managed with the highest level of confidentiality and
integrity. The size of the data stream and the rate at which the data is being generated must be determined
by the business requirements, and the available hardware and software resources must be utilized to the
fullest extent possible.

 The architecture should be extensible; new functionality can be implemented in an existing service by
extending the service’s APIs. For example, an insurance company could extend their customer service
platform to provide a new feature that allows customers to obtain a personalized quote based on their
preferences. Newer technologies, such as artificial intelligence, can be implemented in an existing service
by extending the service’s APIs. For example, an insurance company could extend their customer service
platform to provide a new feature that allows customers to obtain a personalized quote based on their
preferences. Newer technologies, such as artificial intelligence, should be implemented in the core services;
the core services can be extended for new business functions, such as customer relationship management.

 Data security is a critical aspect of the data governance strategy. Data security controls at the source
include establishing data access controls and data encryption. Data security controls at the perimeter
include data security policies and monitoring access to the data.

 It should be simple and straightforward, and users should be able to work with the data in an efficient and
effective manner. Data Warehouse management should be easy to understand and implement. Data
Warehouse management should not be complicated and difficult for beginners should not find their way into
data warehouse management. It should be simple to use and easy to understand.
Types of Data Warehouse Architectures
There are basically three different data warehouse architectures.

Single-Tier Architecture
Single-tier architectures are not implemented in real-time systems. They are used for batch and real-time processing.
The data is first transferred to a single-tier architecture where it is converted into a format that is suitable for real-time
processing. This architecture is known as “single-threaded”. After this, the data is transferred to a real-time system.
Single-tier architectures are currently the most preferred way to process operational data. It is important to note that
single-tier architectures are not implemented in real time systems.
The data storage and processing middleware should be able to determine the quality of the data before the data is
accepted by the analytical engine and transformed into relevant information. If these steps are not performed, then
the middleware can be penetrated by malicious or faulty code. As an example, consider a credit score calculation. If a
malicious hacker controls the middleware, then the hacker can modify the score and extract valuable data.

Two-Tier Architecture
In a two-tier data warehouse, an analytical process is separated from a business process. This allows for greater
levels of control and efficiency. A two-tier system also provides a better understanding of the data and allows for
more informed decisions.
Two-layer architecture describes a four-stage data flow in which physical sources are separated from data
warehouses by a two-layered architecture.

 The source of the data is critical to the data warehouse’s integrity. The integrity of the data stored in the data
warehouse must be guaranteed. Data integrity is the degree to which data values in a database record are
true or accurate. A data warehouse is a system that stores information in a database so that it can be
searched and analyzed.

 Data staging is a key process in the ETL process, and one that can significantly reduce the time it takes to
extract, transform, and load (ETL) a large data set. ETL tools can extract data from various storage sources,
transform the data with corporate-specific functions, and load the data into a data warehouse. Data
warehouse functions such as monitoring the system, provisioning new data, and making decisions on the
basis of the data are all done through data warehouse functions such as ETL. Data warehouse functions
such as ETL can be implemented through a data warehouse.

 Data warehouse metadata is a critical component of the data warehouse. It is the information that helps a
data warehouse administrator decide which data to delete, which data to retain, and which data to use in
future reports. It is also important to maintain data warehouse consistency. Data warehouse administrators
must determine which data should be updated or deleted when new data arrives, and which data should be
left untouched. When data warehouse consistency is not guaranteed, application developers and users must
be careful about which tables and reports they create.

 Data profiling is also very important for this level as it helps in validating data integrity and presentation
standards. It also comes with advanced analytics such as real-time and batch reporting, data profiling and
visualizations, and rating functions. It is important to keep in mind that this is not just a data warehouse but a
live data platform that receives and analyzes massive amounts of data. This is why it is important to keep
track of data changes, scalability, and performance of the system.

Three-Tier Architecture
A three-tier structure is employed in the source layer, the reconciled layer, and the data warehouse layer. The
reconciled layer sits between the source data and data warehouse. The main disadvantage of the reconciled layer is
the fact that it is not possible to completely ignore the problems of the data before it is reconciled. Therefore, the main
focus of the reconciler should be on data integrity, accuracy, and consistency. For example, assume that the data
warehouse contains a collection of company data elements that are updated frequently, such as order book
information. In such a case, the best approach would be to use a web-based data warehouse refresh tool, which
extracts the latest data from the data warehouse and refreshes the data in the corporate application. This architecture
is appropriate for systems with a long-life cycle. Whenever a change occurs in the data, an extra layer of data review
and analysis is done to ensure that no erroneous data was entered. This architecture is also known as data-driven
architecture. This structure is mainly used for large-scale systems. It is important to note that the extra layers of data
review and analysis created by this structure does not consume any extra space in the storage device.

Advantages of Data
WarehouseArchitecture
 The data mart is a collection of data model definitions that captures the data model at a high level and
provides a common data access strategy for the data warehouse. The data mart provides a common data
 access strategy for the data warehouse, consistency, and governance from one location to manage the
diverse data sources. The data mart is an important building block for the data warehouse. It provides a way
to standardize data access, create a common strategy for data integration, and make the data model
available for data profiling and analytics. The data mart does not create data – it only provides the data
access strategy.

 The process of change starts with identifying the problems and pain points of your current system, and then
mapping out a plan to solve those problems using the new system. After that, the system is tested to make
sure that everything is working as expected. Once the system is deemed fit for purpose, the process of
change starts. The first thing is to make sure that the existing stakeholders are comfortable with the new
system. Then, the process of change has to be validated by conducting an assessment. This is how the
model is considered the most appropriate for business transformation.

 There is a good reason why over 90% of the data businesses collect is in the form of data warehouses. Data
warehouses are huge collections of data, usually stored in a database, that are used to help make business
decisions. Many data warehouses are designed to support ETL processes and deliver data to a CRM
system so that business users can start looking at actual data and make decisions.

 With a data warehouse, you can also take advantage of ETL (extract, transform, and load) and ETL
management processes to connect your data sources and process them together. In other words, a data
warehouse is a central repository of your data that can be accessed by any of your analytic platforms.

 Data warehouses have increased in speed and scale with the adoption of NoSQL databases, like MongoDB
or GARIA. When implemented in conjunction with a BI platform, data warehouse technology enables real-
time analytics, enabling the streamlining of decision making, the reduction of lead and invoice inquiries, and
increased profitability.

Disadvantages of Data Warehouse

Architecture
 The maintenance of a data warehouse is a crucial task which needs to be done well. For a data warehouse
to be maintained, there is a need to collect data, process it, and then analyze it. However, data collection,
processing, and analysis need to be done within a certain timeframe. The maintenance of a data warehouse
requires a great deal of effort, which may not be justified by the returns on investment. However, a data
warehouse can be a critical component of an enterprise data management system.

 To speed up the process and minimize the time required for extracting data, you can leverage some ETL
tools to automate the process. However, automated extraction does not guarantee that the data is properly
cleaned and validated. It is best to do both manually and manually-enforced tasks in sequence. When the
data has been validated and the cleanup process has been automated, the data is ready for ingestion into
the warehouse.

 Such an omission might result in incorrect assessment of the property value, over estimation of expenses, or
under estimation of sales. Data integration is essential for any organization that processes large amounts of
data. It must be ensured that all required data is integrated in the warehouse. This can be done by using
data trapping or data mining techniques.

 The majority of the data will be stored in a warehouse and analyzed using data profiling tools. The
warehouse infrastructure will be required to support the analysis of massive amounts of data and the
storage of the data in the most cost-effective manner. The warehouse will act as a repository for the data
and will be the central depository for all data analysis tools.
 However, with the right approach, an organization can achieve better results by working with data
warehouse tools in a disciplined and structured fashion. One important aspect of a data warehouse’s
architecture that must be carefully considered is the data source. If the data comes from multiple sources
such as external sources such as sensors or authorized partners, then data integration is even more
important. An organization must first decide which sources of data it wants to work with and then work to
integrate these data sources.