Data Integration in Data Mining Simplified 101

on Data Engineering, Data Integration, Data Mining, ETL • May 27th, 2022 • Write for Hevo

Data Integration Data Mining - FI | Hevo Data

In today’s world, businesses focus on gathering more data to extract valuable insights from and optimize business processes. In short, incorporating Data Integration in Data Mining help businesses collect data efficiently, extract insights, and understand their customers by analyzing commodity purchase data, and new behavior nudges.

Today, we augment business capabilities by leveraging standard SaaS applications. With the help of Data Integration in Data Mining, companies can integrate all their business data stored in several Databases. 

In this article, you will learn about Data Integration and the role of Data Integration in Data Mining. You will also read about different approaches and techniques used for Data Integration in Data Mining. An organization needs to stay ahead of its competitors and make smarter business decisions.

Table of Contents

What is Data Integration?

Data Integration in Data Mining: Data Integration Logo | Hevo Data
Image Source

Data Integration is a process of combining data from multiple sources in one place to provide a holistic view of the data. It is an essential process in both domains, the commercial domain where there is a need to merge two similar Databases, and the scientific domain for combining data from multiple bioinformatics repositories. Data Integration aims to make data freely available and easier to consume and the process by users and systems. 

Correct implementation of the Data Integration process can help companies bring down the IT costs, improve data quality, and free up the resources. enhance changes and innovation without affecting existing applications or data structures. Data Integration comes into the picture when there is an increase in the frequency of data volume and the need to share the existing data becomes essential for businesses.

Advantages of Data Integration

Some of the key benefits of implementing Data Integration in business are listed below:

  • Data Integration reduces the need to manually transform nad combine data sets which significantly increases the operational efficiency.
  • Data Integration improves data quality by automating the data transformations that apply business rules to data.
  • Data Integration provides a unified view of data that allows companies to analyze data easily and generate valuable insights from it.

What is Data Integration in Data Mining?

Data Integration in Data Mining: Dataflow Chart | Hevo Data
Image Source

Data Integration is a process of merging data from multiple data sources. Data Integration in Data Mining is a record preprocessing method that involves merging data from heterogeneous data sources into coherent data to provide a unified view of the data. These data include several Databases, record cubes, or flat documents. The statistical strategy in Data Integration in Data Mining is formally stated as a triplet of the (G, S, M) approach, where G is a global schema, S is a heterogenous source and M represents a mapping between source and global schema queries. 

You have to deal with several issues such as Data Redundancy, Inconsistency, Duplicity, and many more. One of the most well-known implementations of Data Integration in Data Mining is building an enterprise’s Data Warehouse. Data Warehouses allow organizations to perform analysis on the data.

Replicate Data in Minutes Using Hevo’s No-Code Data Pipeline

Hevo Data, a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 100+ Data Sources straight into your Data Warehouse or any Databases. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code!

GET STARTED WITH HEVO FOR FREE

Hevo is the fastest, easiest, and most reliable data replication platform that will save your engineering bandwidth and time multifold. Try our 14-day full access free trial today to experience an entirely automated hassle-free Data Replication!

The Importance of Data Integration in Data Mining

Many companies incorporate Big Data and Data Analytics to stay ahead of their competitors. One of the most common applications of Data Integration in Data Mining is market and consumer data collection from various data sources such as Ad platforms, Sales platforms, Social Media platforms, etc. Data Integration helps companies in monitoring their activities and performance in real-time and perform Data Analytics on data for future predictions and improve strategies. 

Data Integration in Data Mining is also essential in the healthcare industry as it enables organizations to collect patient records from various sources and integrate them for identifying medical disorders and diseases and extract useful insights. Enterprise Data Integration feeds integrated data into data centers to enable enterprise reporting, predictive analytics, and business intelligence. Also, data collection and data integration in Data Mining helps in improving the accuracy of medical insurance claims processing. It ensures that the patient’s names and other personal information are saved accurately and consistently. 

Different Approaches to Data Integration in Data Mining

Data Integration in Data Mining is mainly categorized into two types of approaches. The different approaches to Data Integration in Data Mining are listed below:

Tight Coupling

Tight Coupling is a process of combining data from various data sources using ETL (Extraction, Transformation, and Loading) into a single storage system such as Data Warehouses. Here, Data Warehouses are treated as a data retrieval component. 

Loose Coupling

In the Loose Coupling, the data is kept actual data source Databases. Using this approach users get an interface to send a query, which transforms into a format suitable for the data source, and the query is received by the source and it sends the data back to the user as per the query.

What Makes Hevo’s ETL Process Best-In-Class

Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.

Check out what makes Hevo amazing:

  • Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
  • Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
  • Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making. 
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-day free trial!

Data Integration in Data Mining Techniques 

The various techniques used in Data Integration in Data Mining are listed below:

Manual Integration

Manual Integration is widely used by data analysts for collecting, cleaning, and integrating data to extract valuable information. This method avoids using automation during Data Integration. Manual Integration is best suited for an organization with a small or limited dataset. It is a time-consuming task and dealing with huge datasets will be a tedious task. 

Middleware Integration

In Middleware Integration, middleware software is used to collect data from multiple data sources, normalize it, and store it in the destination data set. It is used whenever an organization wants to transfer or migrate data from legacy systems to modern Databases. Middleware Data Integration in Data Mining act as a medium or interpreter between legacy and modern systems. 

Application-Based Integration

Application-based Integration uses software to extract, transform and load data from data sources. It saves time and effort but building such software application requires technical understanding. Although, this technique saves time and effort but complicated to implement.

Uniform Access Integration

Unifrom Access Integration technique integrates data from various data sources but it doesn’t change the location of the data, it stays in the original location. With this Data Integration in Data Mining technique, users can integrate data to create a holistic view without the need for separate storage space. 

Data Warehousing

Data Warehousing is a Data Integration in Data Mining technique that is similar to Uniform Access Integration but the only difference is it stores data in certain storage, Data Warehouse enables Data Analysts, Data Scientists, and other users to handle more complex queries with ease. It delivers high query speed and a safe place to store business data. 

Conclusion

In this article, you learned about Data Integration, What is Data Integration in Data Mining, and its importance. Also, you read about different approaches to Data Integration in Data Mining and various techniques. Users need to explore and choose the right Data Integration technique for Data Mining as per their needs and business requirements. 

Visit our Website to Explore Hevo

Companies need to analyze their business data stored in multiple data sources. Data needs to be loaded to the Data Warehouse to get a holistic view of the data. Hevo Data is a No-code Data Pipeline solution that helps to transfer data from 100+ data sources to desired Data Warehouse. It fully automates the process of transforming and transferring data to a destination without writing a single line of code.

Want to take Hevo for a spin? Sign Up here for a 14-day free trial and experience the feature-rich Hevo.

Share your experience of learning about Data Integration in Data Mining in the comments section below!

No-code Data Pipeline For your Data Warehouse