Data Mart: the essential connection point between BI and data warehouse
As the digitization of society as a whole accelerates, the importance of data collection, storage, and analysis continues to grow in business.
The use of “data marts” is gaining attention as a way to effectively use data in business settings. This article explores what a data mart is, how it works, and why it is important.
What is a data mart?
A data mart is a data store that operates primarily as a selection of data drawn from a data warehouse.
Data warehouses are used to store business data centrally and on a large scale, but when the data is actually used, it must be extracted for its intended purpose.
The place to store that extracted data is the data mart.
A similar term is “data lake,” which refers to a place to store unorganized data as a preliminary step to a data warehouse.
So, if you were to assemble a data management flow using all of these, it would operate as follows.
- Store the raw data coming from each department and each system in a data lake.
- Structure the data in the data lake and store it in the data warehouse
- Extract information necessary for each department from the data warehouse to the data mart
- Execute queries and analysis on the data in the data mart and provide feedback to the business
Why do we need a data mart?
To understand the importance of a data mart, let’s imagine that they don’t exist. In this case, your business data would be centrally managed in a data warehouse.
Suppose you wanted to use that data to perform customer or business analysis. Then you would be faced with the following problem:
⚠️ You would need to ask a highly skilled engineer to extract the data
The internal structure of the data stored in a data warehouse is almost always very complex, and to get the right information, you need to ask an engineer to extract the data for you.
That engineer may not immediately understand what you really want. They are systems experts, not experts in your business. You might have to go back and forth with them a few times to get what you want.
⚠️ You have to access a database that also stores data that is not relevant to you
Data warehouses are usually built for the purpose of centralizing all the data for that business. That means it contains data that has nothing to do with the what you want to access. It’s also a little scary to imagine everyone handling raw data that they might not know the purpose of!
⚠️ The sheer volume of the entire data warehouse can cause performance problems
The data stored in a data warehouse is enormous. Therefore, it inevitably takes time to extract the data. You might only want to access a small portion of it, but it takes a long time to search through because of the large amount of other data.
Data Mart Features
Data marts were created to solve these problems.
- Subject-oriented Data: Data marts are operated with a focus on the use of data in business settings. It only stores the specific data sets needed by each department, such as sales, finance, and marketing.
- Subset of Data Warehouse: A data mart is built as a subset of a data warehouse. This simplifies data utilization while maintaining the advantages of centralized management using a data warehouse.
- Accelerated access: Data marts are smaller data sets that are carved out from the data warehouse, making access to data faster.
- Granular access privileges: Because data marts are intended to be used in a business context, they provide fine-grained access rights management for users. This allows you to strengthen data governance and enforce information access policies.
How to build a data mart
So far, we have identified the following usefulness of data marts
- Easier access to data can improve your business
- Faster data access can improve organizational productivity
- Centralized data management and rapid data utilization can prevent information silos
- Data governance can be improved by managing access privileges
Despite these advantages data marts remain an unfortunately-nascent concept, and dedicated tools are scarce. Currently, the majority of users are using relational databases such as Postgres SQL or MySQL on their servers and operating them independently.
Morph will be your data mart! (with extras)
One of the most typical use cases for Morph is as a data mart.
As a data mart, Morph offers the following features.
One-click Postgres SQL setup
Morph allows you to set up Postgres SQL with auto-scaling with a single click. And because Morph’s Postgres is built on serverless architecture, it will never go down due to spikes in data or access volume.
Data import from various services
In addition to databases such as Postgres and MySQL, data can be imported from data warehouses such as BigQuery, and from SaaS such as Hubspot and Notion.
Tailored Access Management
On Morph, access permissions can be set for each database on a per-table — and even per-field — basis.
AI functionality for SQL-free data manipulation
Morph integrates Chat GPT to assist you in querying data sources and analyzing data like a professional without having to know SQL or Python. All you need is an understanding of your business. The days of programming skills as a barrier are gone.
Morph, a new No-Code tool for data management, is now in the public beta phase. We need your input and feedback from a wide range of industries and backgrounds. We invite you to join us on our journey by following the links below.
Website: https://www.morphdb.io/
