How do you integrate data marts with other data sources and systems?

1 Data mart types

The first step to integrate data marts is to understand the different types of data marts and how they relate to the data warehouse. There are three main types of data marts: dependent, independent, and hybrid. Dependent data marts are derived from the data warehouse and follow the same data model and structure. Independent data marts are created separately from the data warehouse and may have different data sources, models, and formats. Hybrid data marts are a combination of dependent and independent data marts, where some data is sourced from the data warehouse and some from other sources.

2 Data mart integration methods

The next step to integrate data marts is to choose the appropriate method of integration, depending on the type and purpose of the data mart. There are four main methods of data mart integration: top-down, bottom-up, federated, and hub-and-spoke. Top-down integration is where the data warehouse is built first and then the data marts are populated from it. Bottom-up integration is where the data marts are built first and then the data warehouse is created by consolidating them. Federated integration is where the data marts are independent and query the data warehouse or other sources as needed. Hub-and-spoke integration is where the data warehouse acts as a central hub that distributes data to the data marts and receives feedback from them.

4 Data mart integration tools

The fourth step to integrate data marts is to use the appropriate tools and technologies that facilitate and automate the integration process. Some of the common tools are: ETL (extract, transform, load), ELT (extract, load, transform), EAI (enterprise application integration), EII (enterprise information integration), and BI (business intelligence). ETL and ELT are tools that move and transform data from various sources to the data warehouse or data marts. EAI and EII are tools that connect and integrate different applications and systems that interact with the data warehouse or data marts. BI is a tool that provides analysis and reporting capabilities on the data in the data warehouse or data marts.

5 Data mart integration best practices

The fifth step to integrate data marts is to follow some best practices and guidelines that ensure a successful and efficient integration process. Some of the best practices are: define clear business objectives and requirements for the data marts, design a logical and physical data model that supports the data marts, implement a robust data quality and consistency management system, establish a comprehensive data security and governance framework, leverage existing tools and standards for data integration, and monitor and optimize the performance and usability of the data marts.

6 Data mart integration benefits

The final step to integrate data marts is to reap the benefits of having a well-integrated and coherent data warehouse environment. Some of the benefits are: improved data availability and accessibility for business users, enhanced data analysis and reporting capabilities for decision making, reduced data redundancy and complexity for maintenance, increased data reliability and trustworthiness for compliance, and greater data agility and adaptability for innovation.