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What is a data mart and how does it help your business? A summary of the previous Episodes

Overview of data warehouse architecture

Data is the fuel of the digital economy, but not all data is equally useful or accessible. To make data-driven decisions, you need to store, organize and analyze your data in a way that suits your business needs and goals.

One way to do that is to use a data mart. A data mart is a subset of a data warehouse that focuses on a specific business area, department or topic. It provides targeted data to defined users, enabling fast access to critical insights.

In this post, we’ll explain what a data mart is, how it differs from a data warehouse and a data lake, and the benefits and challenges of using a data mart.

What Is a Data Warehouse?

A data warehouse is a centralized repository that stores historical and current data from across an organization. It supports business intelligence (BI) and analytics applications, enabling complex queries, reporting, and advanced analytics.

It follows the ETL (extract-transform-load) process and stores structured data from sources like transactional systems and application logs.

Per approfondire: Data Warehouses and Business Intelligence

What Is a Data Lake?

A data lake is a scalable storage platform for structured and unstructured data. It ingests data in its original form and supports real-time analytics, data science, and machine learning.

It follows the ELT (extract-load-transform) process and does not require predefined schema.

Per approfondire: What Is a Data Lake and Why Do You Need One?

What Is a Data Mart?

A data mart is a focused subset of a data warehouse, tailored to a specific domain like sales, finance, or marketing. It enables fast, relevant access to data for specific user groups.

Data marts can be built top-down (from a warehouse) or bottom-up (from other sources). They support fast query processing and are organized by subject or function.

Per approfondire: What Is a Data Mart and How Does It Help?

Benefits of Using a Data Mart

  • Relevance: Delivers targeted data to users with shared goals.
  • Performance: Improves query speed and reduces load on central systems.
  • Agility: Enables faster BI development and self-service analytics.
  • Security: Restricts access and supports governance and compliance.

Challenges of Using a Data Mart

  • Data quality: Depends on accuracy of source data.
  • Data integration: Requires ETL/ELT processes across formats.
  • Data maintenance: Needs regular updates and schema adjustments.
  • Data consistency: Risk of misalignment with central warehouse.

How to Get Started with a Data Mart

IBM offers cloud services to build and manage data marts:

  • IBM Db2 Warehouse: Managed cloud data warehouse for OLAP workloads.
  • IBM Cloud Pak for Data: Integrated platform for OLTP + OLAP, includes Watson Studio and Netezza.
  • IBM Cloud Object Storage: Scalable object storage for data lakes.
  • IBM DataStage: Managed ETL service for data integration and transformation.
  • IBM Cognos Analytics: BI platform for dashboards and visualizations.

I hope this post has given you a clear overview of what a data mart is and how it can help your business.

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