Welcome to “Data Marts Overview.” After watching this video, you will be able to: Define what a data mart is. Give examples of data marts. Compare data marts to transactional
databases and enterprise data warehouses. Describe data pipelines for loading data marts. A data mart is an isolated part of the larger
enterprise data warehouse that is specifically built to serve a particular business
function, purpose, or community of users. For example, the sales and finance departments
in a company may have access to dedicated data marts that supply the data required for their
quarterly sales reports and projections. The marketing team may use data marts to analyze
customer behavior data, and the shipping, manufacturing and warranty departments
may have their own data marts. So, what are data marts used
for? Data marts are designed to provide specific support for
making tactical decisions. As such, data marts are focused only on the most
relevant data, which saves end users the time and effort that would otherwise be spent
searching the data warehouse for insights. The typical structure of a
data mart is as follows: It is a relational database with a star, or more
often a snowflake schema, which means it contains a central fact table consisting of the business
metrics relevant to a business process, which is surrounded by a related hierarchy of dimension
tables that provide context for the facts. Let’s look at some typical differences between
three types of data repositories: data marts, transactional databases, and data warehouses,
starting with data marts and databases. Both data marts and data warehouses
are online analytical processing (or OLAP) systems that are optimized for
read-intensive queries and operations, whereas transactional databases are
online transaction processing (or OLTP) systems that are optimized for
write-intensive queries and applications. Data marts use transactional databases or data
warehouses as data sources, while in transactional databases, operational applications,
such as point-of-sales systems, serve as the sources of data. A data mart stores validated, transformed, and cleaned data, while a database will have
raw data that has not yet been cleaned. Data marts accumulate historical data
that can be used for trend analysis, while transactional databases may
not always store older data. A data mart is much like a data warehouse,
except it has a smaller, tactical scope. Data warehouses broadly support the
strategic requirements of the enterprise. Data marts are lean and fast
compared to data warehouses, which can be very large, and hence, can be slower. There are three basic types of data
marts—dependent, independent, and hybrid. The difference between these three kinds of
data marts depends on their relationship with the data warehouse and the sources used
for supplying each of them with data. Dependent data marts draw data from the enterprise
data warehouse, while independent data marts bypass the data warehouse and are created
directly from sources, which may include internal operational systems or external data from
vendors or other sources outside the enterprise. Hybrid data marts only depend partially
on the enterprise data warehouse. They combine inputs from data warehouses
with data from operational systems and other systems external to the warehouse. Dependent data marts offer analytical capabilities
within a restricted area of the enterprise data warehouse. Thus, they inherit the security
that comes with the enterprise data warehouse. And since dependent data marts pull
data directly from the data warehouse, where data has already been cleaned
and transformed, they tend to have simpler data pipelines than
independent data marts. Independent data marts differ from dependent
data marts because they require custom extract, transform and load data pipelines
to carry out the transformation and integration processes on the source data since
it is coming directly from operational systems and external sources, and independent data marts
may also require separate security measures. Whatever the type of data mart
you may have, its purpose is to: Provide end-users with relevant
data when they need it. Accelerate business processes by
providing efficient query response times. Provide a cost-efficient method for
informing data-driven decisions, and Ensure secure access and control over your data. In this video, you learned that a data mart: Is an isolated part of the larger enterprise
data warehouse that is specifically built to serve a particular business function,
purpose, or community of users. Is designed to provide specific, timely, and
rapid support for making tactical decisions. And typically has a star or snowflake schema. You also learned that unlike an OLTP database, an OLAP data mart stores clean and validated
data and accumulates historical data. And, you can categorize data marts in terms of
their dependence on the enterprise data warehouse. A data mart can be completely
dependent on the data warehouse, a completely independent, standalone, mini
data warehouse, or a hybrid of the two.