Types of Data Mining Architecture: Explained in Plain English

Key Points

• Data architecture is about equipping businesses with the right data management strategy, including data collection, organization, storage, transformation, and access.
• Effective data architecture improves data quality, integrity, reduces redundancy, and enhances decision-making.
• The data mining process involves data sources, staging, storage, and presentation layers.
• There are four main types of data mining architecture: no coupling, loose coupling, semi-tight coupling, and tight coupling.
• The choice of data mining architecture depends on the size and complexity of data operations.

Before we get into data mining architecture and methods of data mining, we need to make one thing absolutely clear: ultimately, this is about equipping businesses with the right data management strategy. 

In today’s guide, we explain the main types of data architecture available to engineers, as well as the advantages and disadvantages of each. Understand, though, that data architecture is a highly complex and evolving task.

Picking the right method for your use case requires looking at a lot of moving pieces, and you will also very likely have to revise or update your approach to take into account changes in business needs. So, to help you gain a deeper understanding, let’s dive in!

Why Does Data Mining Architecture Matter?

People talk a lot about the importance of using data to make business decisions. While this is obviously true, what often gets forgotten is that the data itself would be useless if there wasn’t a good way of orchestrating its delivery from the raw source to the final consumer. 

In fact, there’s a huge amount of effort and planning involved in building these data pipelines, and that’s why organizations need to rely upon a well-articulated data management strategy. 

Such a strategy must lay out what kinds of data are to be collected, how they are to be collected, how data sets are to be organized and stored, how to transform and load the data, and then how data is to be accessed — and under what conditions. 

This is what constitutes data architecture. While it’s a complex process, effective data architecture yields many benefits.

Data architecture improves the quality of organizational data, ensures data integrity, reduces data redundancy, and — perhaps most important of all — improves people’s decision-making.

An Overview of the Data Mining Process 

Before we get into the fine-grained details of data architecture, it would be good to establish a framework for understanding your data’s basic life cycle — its journey from raw material to final deliverable. This is the actual pipeline that data flows through.

Source

In the first layer, we have the data source, or sources. Your sources might include a number of different types of data, like CSVs or other files, and databases.

Databases, of course, can come in many different forms. You could, for instance, have a transactional database, which records information like sales to customers or new employee hires. 

Staging

Once you’ve looked at your sources, it’s time to look at the “staging” layer. This is where raw data gets extracted, transformed, and cleaned before moving on to the next level. If you’ve ever heard the “ETL” acronym, this is indeed what we’re referring to here. 

• Extract: Gathering data from the sources
• Transform: Clean, format, and standardize the data for enhanced readability and ease of organization
• Load: Moving the data into a designated storage space

Once designed and deployed, ETL systems are generally automated through software solutions such as AWS Glue and others offered by third parties like Amazon Web Services, Microsoft, or IBM. 

Storage

Now that you’ve staged and loaded your data, you’re ready to move onto the storage layer. In this part of the pipeline, data gets stored and backed up so that it can be ready for use.

Typically, data moves first to a data warehouse, then into a smaller data mart — or just the opposite. The order in which data is stored depends on the nature of your data architecture. 

Presentation

Presentation is the next, final, and most critical layer of your data’s life cycle. Here, stored data can be queried and retrieved by different teams.

The presentation layer often involves the use of business intelligence tools, such as Databricks, Snowflake, Tableau, or others that allow users to easily visualize the results of queries. 

4 Main Types of Data Mining Architecture

So, now you understand the major components of the data mining process, but what are the different design principles that affect exactly how you construct a data pipeline?

That’s where data architecture comes in. With that in mind, let’s dive into the different types of data mining architecture available to you. 

For the purposes of this discussion, we’re going to introduce you to four major types of data mining architecture, while allowing for the definite possibility that there are surely other methods out there for designing data systems.

The four types that we’re going to discuss include: 

• No Coupling
• Loose Coupling
• Semi-Tight Coupling
• Tight Coupling

Basically, these four different types represent different degrees of “coupling” with an actual database or warehouse. The more bound your architecture is to a database system, the more complex and difficult to construct the architecture is going to be—and vice versa.

Or, to put it another way, your data mining system becomes increasingly centralized the more you move from no coupling to tight coupling. Let’s go over each of these in detail. 

No Coupling

No coupling is the simplest form of data architecture. With no coupling, you either pull data directly from a flat file system, or from a database or data warehouse — but you do not use any of the database’s features (such as indexing or sorting) or store any data in the database or warehouse. 

When collecting data in this way, the focus is on simplicity and ease of use. No coupling is ideal if all you need to do is quickly pull information from a small dataset — such as a text file, Excel file, or small transactional database.

And once data is retrieved, you would store it in local memory rather than in a database. Obviously, this kind of data strategy is non-sensical when dealing with larger, complex datasets with multiple sources of data.  

Loose Coupling

At this level, your own data mining system becomes somewhat more integrated with a database’s features. Because you’re now making use of some database functionality, your system becomes more efficient than a “no coupling” model.

If we can imagine no coupling as basically a manual method of data retrieval, then we can consider “loose coupling” as containing at least a sprinkle of automation. 

Microservices architecture stands out as a common iteration of loose coupling. Here, you build a data mining system out of small, independent data services that all interact with one another through APIs.

An emerging or mid-level e-commerce brand might use such an arrangement to keep inter-related data centers like product catalogs, user management, customer orders, transaction records, and shipping information all loosely connected to each other. 

In small- to medium-sized data environments, loose coupling is especially popular, as the semi-decentralized model allows for flexibility and greater resiliency in the event of failures to one or more systems.

Since microservices can be easily used across different platforms, loose coupling also invites a very healthy degree of portability and a certain amount of scalability. 

As with no coupling, though, loose coupling is not an appropriate option for bigger operations dealing with a larger-scale data environment. 

Semi-Tight Coupling

Here, your data mining system starts to make use of a database’s built-in features. At the same time, you still get some of the advantages that come with using a loosely coupled system.

Like loose coupling, you’re still not totally bound to a centralized application and you can reuse any existing data mining code, but because you’re directly interacting with a data warehouse, you can actually use all the functionality from it (such as filtering, indexing, and sorting). 

You’ll often see such systems in proprietary applications that use a third-party API or cloud-based data service to provide certain features or store and manage data. These applications can range from web-based, to mobile, to software applications. 

If you’re working with data on a larger scale, semi-tight coupling becomes far more critical, as it allows for greater scalability and more efficient handling of complex data systems.

The downsides include a greater amount of upfront effort needed to get the data mining system rolled out, and then greater dependency on data warehouses to ensure operational efficiency. 

Tight Coupling

As the name suggests, tight coupling involves the strictest level of integration between your data mining system and the database, databases, or data warehouse it interacts with. Such a system is also tightly coupled with a specific programming language and particular data-related algorithms. 

You might be able to surmise that this is the most appropriate model for mid- to enterprise-level organizations. At this scale, a data management strategy needs to be far more uniform and far more capable of interacting with large and complex data sets. 

In addition to the above advantages, the robust nature of the system suggests that it’s also going to be more reliable than the other architectures listed here. Particularly, you’ll have the ability to set up an extensive system of backup protocols to keep your data more secure.

Now, let’s consider the drawbacks of tight coupling. Probably, the most obvious disadvantage is the cost. Due to the complexity and size of this type of data architecture, you’ll need to pay a larger sum for advanced data mining capabilities and higher rates for data retrieval and storage.  

Plus, this kind of data mining architecture is incredibly difficult to set up, and ongoing maintenance and management of the system is more complex. And when it comes time to make significant changes to your set-up, or to scale up or down, you’re going to find that more difficult to implement as well. 

Picking the Right Data Mining Architecture for Your Environment

So, decentralized or centralized? That’s the question. What it boils down to is the size and complexity of your data operations.

If you’re working with a small set of data and want to keep your retrieval methods simple and nimble, you’ll want to stick with a more decentralized approach—either no coupling or loose coupling. 

The larger and more complex your data is, the more centralized you’ll need your mining architecture to be. In other words, you’ll need to be more “coupled” to databases or to a data warehouse.

Remember though: for increased centralization to make sense, you’ll need to have a bigger budget to work with, and you’ll need to have data engineers who know how to set up and manage a more complex system. 

Recall, too, that when you’re thinking about how to design your data mining system, the overall goal is to move toward an intelligent, well-articulated data strategy. And having a good data management strategy is important even if your data operations are simpler. 

Resources for Learning More

Data mining architecture is a complex topic, but it is a vital subject to understand if you are pursuing a career in data science.

We’ve given you some ideas to digest here, but you might need further support on your journey toward more sophisticated data management. To that end, you might want to consider pursuing either third-party help or community support. 

Online developer communities, such as GitHub, offer a place for data engineers to share knowledge. And not just technical documentation, but even entire repositories of code that you can apply to a variety of use cases. 

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