In a market dominated by big data and analytics, data marts are one key to efficiently transforming information into insights. Data warehouses typically deal with large data sets, but data analysis requires easy-to-find and readily available data. Should a business person have to perform complex queries just to access the data they need for their reports? No—and that’s why companies smart companies use data marts.
A data mart is a subject-oriented database that is often a partitioned segment of an enterprise data warehouse. The subset of data held in a data mart typically aligns with a particular business unit like sales, finance, or marketing. Data marts accelerate business processes by allowing access to relevant information in a data warehouse or operational data store within days, as opposed to months or longer. Because a data mart only contains the data applicable to a certain business area, it is a cost-effective way to gain actionable insights quickly.
WHAT IS DATA MART?
Who uses data marts?
Many types of businesses can use data marts for more accessible data. Here are some examples of industries that can benefit from data marts:
What is a data mart?
A data mart is a collection of data that focuses on a single subject or part of a business. Data marts might focus on things like specificity, accessibility, marketing, sales, finance or employee performance to generate usable sets of data. Businesses use this data to improve operations and learn more about a companys internal processes.
Data mart types
Here are the three types of data marts:
1. Dependent data marts
Dependent data marts typically originate from a companys existing data warehouse. The data mart depends on data feeds from the warehouse and works as a functional part of the whole warehouse. Dependent data marts only extract information sets from the warehouse when the team needs specific data. You can form dependent data marts by putting specific data sets into a cluster and then extracting a specific portion of the data for analysis when necessary.
2. Hybrid data marts
A hybrid data mart is an intermediary between independent and dependent data marts, which uses information from both an internal data warehouse and external sources. The hybrid model combines the flexibility of independent data marts and the dependability of the companys own data warehouse. You can create a hybrid data mart by creating a data set as a dependent of the data warehouse and connecting it to external sources. This allows the team to collect data from both internal and external sources.
3. Independent data marts
Independent data marts operate separately from a data warehouse. These are typical of smaller businesses or businesses that dont want to invest resources into creating a full data warehouse. Building independent systems gives the business the data it needs without the need to grow potentially outside of budget requirements. Independent data marts can also be more flexible and accessible when they dont depend on a data warehouse.
Benefits of data marts
Data marts can offer benefits to every industry. Here are a few benefits to consider:
Data mart structures
Data marts typically have specific structures depending on their use. Here are a few examples of data mart structures:
The star structure is a multidimensional database that resembles the shape of a five-pointed star. The center of the star represents the specific business process that constitutes the purpose of the data mart, and the outer ends of the star contain associated data. While the outer arms all relate and depend on the central database, the arms dont necessarily depend on each other for functionality.
The vault-style data mart is a more layered approach to the data mart system that helps establish the foundations of a full company data warehouse. The vault style requires less maintenance than the star model and helps layer data sets for more agility and accessibility when team members are accessing the data. A vault system also increases the security of data sets.
The snowflake model is an extension of the star model. It uses the existing blueprint of the star data mart and provides extra dimensions of data for the central data set. With additional tables, the snowflake model provides extensive data sets and requires less disk space to store and maintain.
How to use a data mart
Using a data mart requires certain steps to ensure youre setting up the right tools and using them correctly. Here are some steps for setting up and using a data mart:
1. Design your companys data mart strategy
When designing your companys data mart strategy, consider whether youre building for a future data warehouse, using a preexisting warehouse or building independent data marts. Decide what kind of data you want to collect and store and how the company might use that data. In addition, determine if any modifications to the existing data warehouse system are necessary to support the addition of specific data marts and what the costs could be for the company. You can also consider how to scale the data mart if necessary.
2. Construct the data mart architecture
Construct your data mart architecture in alignment with the parameters you set in the first step. Determine what database to use for your data mart and add any necessary modifications to your existing data warehouse structures. You can also ensure accessibility to make data more accessible and easier for team members to use. Ensure users of the data mart have access to the data they need with the right permissions on the system and the right connectivity.
3. Populate the data mart architecture
Populating the data mart architecture means executing the data flow between your data warehouse or external sources. This populates the data mart with the data your company needs and allows you to troubleshoot any problems. You can determine what terms allow data to be accessible, how to clean and normalize data and what indices to use for greater accessibility of the data marts information.
4. Access your data marts
Access your populated data marts by setting up queries for specific data sets and ensuring the data mart retrieves them. This is a good opportunity for troubleshooting specific issues and ensuring the functionality of your data mart. You can examine how the data mart works with your existing data warehouse, how users feel about the interface and functionality and what improvements you might make in the future. Documenting the deployment phase can be a good way to track errors, successes and user feedback for future improvements.
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