Data Mining Architecture: How Data Systems are Structured for Insights

Key takeaways 

Data mining architecture refers to the systems and structures used to analyze large data sets and uncover insights that support business decisions. Here are some important facts to know: 

• Data careers are in high demand due to the increasing number of organizations that mine data and the need for suitable infrastructure, with data science projected to grow at a 34 percent rate during the decade leading up to 2034 [1].

• Data mining architecture enables businesses and organizations to approach data mining with a range of systems and processes, making data collection, organization, analysis, and interpretation efficient and well-managed. 

• You’ll find four main types of architecture data mining systems: data warehouse architecture, data lake architecture, data mesh architecture, and data fabric architecture. 

Learn more about data mining architecture and how it can help you enter a number of data-related careers. When you’re ready to learn more, consider enrolling in the IBM Data Architecture Professional Certificate to strengthen your skills in data mining architecture. By the end of the course, you’ll learn practical skills, such as database design, database engineering, and data management.

A brief history of data mining architecture

Data collection has historically played a major role in how businesses understand customers, markets, competitors, and processes. However, when data was on a smaller scale and technology was less advanced, data collection programs were simple and independent of each other. Integrating data wasn’t a focus, and professionals hadn’t yet developed data mining architecture. That process began in the 1970s.

Today, data integration tools enable the extraction and collation of raw data from a range of sources. Data architecture encompasses the rules, models, and processes that define how to manage data. 

The architecture of a data mining system

The architecture for data mining is the overarching framework (i.e., the systems, policies, and models) an organization uses to collect, integrate, analyze, and store data. Data mining involves several steps, and a robust architecture is important at every stage. 

The steps include:

• Defining the problem or business objectives

• Selecting and collecting relevant data 

• Preparing the data using data cleaning methods to ensure accuracy

• Building models to classify and compare data points

• Analyzing the data, creating visualizations, and implementing insights

At each stage, businesses and organizations have increasingly begun using autonomous architecture to improve data governance, enhance scalability, and make informed decisions. This process might include using AI tools for data classification, clustering, and transformation. The end result is architecture that empowers the data mining process for improved collection, organization, analysis, and interpretation. 

Types of data mining system architectures

You can categorize data mining system architectures into different types based on their structure and purpose. You might also define them by how they support an organization’s operating model. 

Data warehouse architecture

Data warehouse architecture is the design of systems for collecting and storing large amounts of data for analysis. The architecture needs to be simple and capable of storing large volumes of data. Before storage, architects must clean the data, and once it is in the warehouse, they can categorize and store it in private, secure areas.

Data lake architecture

Data lakes are large repositories for storing data before cleaning, categorizing, and distributing it to data warehouses. Each part of a data lake’s architecture has a different function or zone: 

• Raw: Data from a variety of places, either structured or unstructured. 

• Cleansed: Data that has been cleaned.

• Curated: Data is ready for use in a data warehouse. 

Data fabric architecture

Data fabric architecture enables you to manage data across a range of platforms, including data lakes and warehouses, by using a system that provides uniform data services and accessibility. The technology is still relatively new, but tech companies like Microsoft are developing ways to incorporate it into data mining architecture.

Data mesh architecture 

A data mesh architecture integrates various data sources across an organization and links them for analysis. This type of architecture supports security initiatives by allowing teams to manage their own data more efficiently. 

What is the difference between a data warehouse and a data lake?

Data warehouses and data lakes often work together within the same data mining architecture, which can lead to confusion between them. However, you can use some information to tell them apart. Data lakes hold raw, unprocessed data. It’s a storage system that holds all data as it arrives and then puts it through a filter system. 

On the other hand, a data warehouse stores data that an organization wants to use to inform business decisions. Often, this data is extracted from a data lake. 

Careers that use data mining architecture

Data mining is essential to businesses and organizations, creating opportunities for professionals with technical skills in building data-mining architectures to enter a range of careers. These careers encompass collecting, cleaning, storing, processing, and reporting on data, as well as the associated infrastructure. 

According to the US Bureau of Labor Statistics, working with data can offer a high salary and an above-average job outlook, with the broad category of data science anticipated to grow at a rate of 34 percent from 2024 through 2034 [1]. Some possible career opportunities include:

Data architect

Job outlook (growth projected 2024-2034): 4 percent [2]

Average annual base salary: $123,100 [2]

A data architect designs the structures and systems for data mining activities within an organization. These often include creating automation processes and systems to streamline functions and increase the efficiency and accuracy of data collection, storage, and processing. 

Machine learning engineer

Job Outlook (growth projected 2024-2034): 20 percent [3]

Average annual base salary: $140,910 [3]

As a machine learning engineer, you build, maintain, and manage the machine learning systems and algorithms that form the data mining architecture for an organization, allowing them to work with large data sets.

Big data engineer

Job Outlook (growth projected 2024-2034): 34 percent [1]

Average annual base salary: $112,590 [1]

Big data engineers design systems and architectures to handle the collection and analysis of large data sets, such as data mining warehouses. The job’s responsibilities often include designing algorithms that allow companies to extract raw data and turn it into something manageable, readable, and organized.

Database developer 

Job Outlook (growth projected 2024-2034): 4 percent [2]

Average annual base salary: $123,100 [2]

As a database developer, you work with databases to improve them or build and maintain new ones. Your role is to make them more efficient, streamlined, and to remove any out-of-date code. You are also responsible for any necessary troubleshooting. 

Data modeler 

Job Outlook (growth projected 2024-2034): 9 percent [4]

Average annual base salary: $103,790 [4]

As a data modeler, your skills in modeling conceptual, logical, and physical data will allow you to produce insights from large data sets. These insights will drive more informed decisions. Your models will act as a map of an organization’s assets, architecture, and systems.

Read more: What is a data modeler?

Build data mining architecture skills with our free resources 

Stay up to date with job roles that allow you to use your data mining architect skills by subscribing to our LinkedIn newsletter, Career Chat. You can also continue expanding your data science knowledge and skills by exploring other resources, including: 

• Take the quiz: Which Data Science Course Should I Take?

• Watch on YouTube: Data Engineer Career in 70 Seconds

• Bookmark for later: Why Do Data Analysts Need to Understand AI?

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