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Table of contents
Identifying and Integrating the Useful Data
Tools for Data Integration
Need for Data Integration Tools
Stages in Data Integration
Exploratory Stage
1️⃣Looking for Patterns in Big Data
2️⃣Using FlumeNG for Big Data Integration
3️⃣Exploratory Data Analysis Tools
Codifying Stage👩‍💻
Integration and Incorporation Stage
Frequently Asked Questions
Mention some of the challenges of data integration.
What do you understand by common data integration?
Name the steps performed in data integration.
What is the need for data integration in DBMS?
Last Updated: Mar 27, 2024

Integrating the Data Sources

Author Naman Kukreja
0 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


The most valuable thing in today’s modern world is data. Data is required everywhere and collected from everywhere. So how this happens that whenever you enter your data somewhere, either in some application or on a website, that corresponding application or website behaves according to your entered information and provides services according to your preferences?

Big data

All this comes under integrating the data sources and analyzing them to work according to the user. We will learn more about data integration while moving further in the blog, so let’s get on with our topic without wasting time.

Identifying and Integrating the Useful Data

The technique of collaborating with processes, people, suppliers, and technology to gather, reconcile, and better use data for decision support from various sources is known as big data integration. Velocity, Volume, authenticity, variety, value, and visibility are all features of big data.

Tools for Data Integration

There are many tools available for data integration. We will learn some of them in this blog section.

On-Premise tool These technologies aid in integrating data from a variety of on-premise sources. Native connectors for batch loading from many popular data sources are included in the tools deployed in the private cloud or local network. They're perfect for databases with a lot of data.
Open-Source tool The open-source data integration technologies enable you to have complete control over your data inside your organization. They are the ideal and cost-effective solutions for your internal data consolidation requirements and data security and compliance.
Cloud-based tool These tools are integration platforms that combine data from various sources into a cloud-based data warehouse that allows users to see data in real-time. These technologies make it easier to utilize data more effectively.

Need for Data Integration Tools

There are many requirements for data integration tools. You can complete the data integration process more effectively and rapidly using the finest data integration solutions. They will enable data scientists to concentrate on other critical business processes by automating data mapping, transformation, and cleaning chores. Here are a few advantages that the correct tools may give you:

📗Simplifies the data: A data integration tool's main job is to make complex data easier to understand. To increase efficiency and convenience of use, unstructured or semi-structured data from numerous sources must be consolidated.

📙Add value to your data: Data gathered from diverse source systems may be shown in various ways, including graphs, tables, and other visual representations. You'll need all of your data in one format to undertake qualitative and quantitative analysis, and data integration technologies may help you with that.

📗Save Time and Effort: Manual data integration takes time and consumes most of your team's working hours. Why do the task by hand when there are existing technologies to simplify the data integration process? You can obtain critical business information at the right moment if you have the proper tools. Even your Data Analyst team's work may be reduced to concentrate more on the company's profitability.

📙Lower the risk of errors: When doing certain operations manually, the possibility of mistakes cannot be eliminated, and the same is valid for data integration. Even if you have your software for particular purposes, data accuracy cannot be guaranteed. This is when data integration technologies come in handy. You can put your systems in place and access critical corporate data in real time with the correct tools.

You may need to combine many of these vast data sources for your investigation. To finish your research, you'll need to transport vast data. You may need to link your big data with your operational data once completed your analysis. 

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Stages in Data Integration

You probably have no idea what you'll uncover when you start your big data analysis. Your investigation will go in phases. You may start with petabytes of data and restrict your findings as you seek patterns. The three steps that follow are discussed in further detail:

Exploratory Stage

You'll want to look for patterns in the data in the early phases of your study. New and unexpected linkages and correlations between items can only be discovered by reviewing massive amounts of data (terabytes and petabytes). These patterns, for example, may reveal client preferences for a new product. You'll need a platform like Hadoop to organize your massive data to find these patterns.

EDA is generally used to examine what data might disclose outside of formal modelling or hypothesis testing tasks and better understand data set variables and their interactions. It might also assist you in determining if the statistical approaches you're contemplating for data analysis are suitable. EDA methods were first established in the 1970s by American mathematician John Tukey and are still commonly employed in the data discovery process today.

1️⃣Looking for Patterns in Big Data

Many firms are starting to experience competitive benefits due to big data analytics. Social media data streams are becoming an increasingly important part of their digital marketing strategy for many businesses. Wal-Mart, for example, analyses consumer location-based data, tweets, and other social media feeds to provide more focused product suggestions and customize in-store product selection to customer demand. Wal-Mart bought Kosmix, a social media firm, in 2011 for its technological platform for discovering and analyzing real-time data streams. This technology may be used in the exploratory stage to quickly search through large volumes of streaming data and extract trending patterns that pertain to specific items or consumers. The information may be utilized to improve inventory depending on the likes and dislikes of consumers in a particular area.

The massive data volumes are whittled down when firms hunt for patterns in big data as if they were fed through a funnel. You may start with petabytes of data and then delete data that does not match up as you hunt for data with comparable features or data that creates a specific pattern.

2️⃣Using FlumeNG for Big Data Integration

Flume is often used to gather massive log data from several servers. A Flume system, it maintains track of all the physical and logical nodes. Agent nodes are placed on the servers and oversee the transport and processing of a single stream of data from its origin to its destination. Collectors are also used to combine data streams into more significant streams that may be written to a Hadoop file system or another big data storage container. Flume is built for scalability, meaning it can add additional resources to a procedure to efficiently handle massive volumes of data. Flume's output may be connected with Hadoop and Hive for data analysis. Flume also features data transformation components and can change the y-axis.

During the experimental stage, however, one sort of integration is crucial. It is often essential to gather, combine, and transport incredibly vast volumes of streaming data to look for hidden patterns in big data. Traditional integration techniques, such as ETL would not be able to transport massive streams of data quickly enough to produce findings for real-time fraud detection analysis. FlumeNG (a more sophisticated version of the original Flume) streams your data into Hadoop in real-time.

3️⃣Exploratory Data Analysis Tools

You may use EDA tools to execute the following statistical functions and techniques:

📕Techniques for clustering and dimension reduction aid in creating graphical representations of high-dimensional data with numerous variables.

📗Summary statistics and a univariate depiction of each field in the raw dataset.

📕Using bivariate visualizations and summary statistics, you may analyze the link between each variable in the dataset and the target variable you're looking at.

📗Multivariate visualizations are used to map and analyze connections between multiple fields in the data.

📕K-means clustering is an unsupervised learning clustering approach in which data points are divided into K groups, i.e., the number of clusters, depending on their distance from the centroid of each group. The data points closest to a certain centroid will be grouped in the same category. K-means Market segmentation, pattern identification, and picture compression all involve clustering.

📗Predictive models such as linear regression employ statistics and data to anticipate outcomes.

Codifying Stage👩‍💻

You'll need to follow some procedures, from seeing a pattern to adopting it into your company process. For example, how would it utilize this information if a prominent shop watches social media and notices a lot of buzz about a future college football game near one of its locations? 

Codifying Stage

With hundreds of outlets and tens of thousands of consumers, you'll need a repeatable procedure to go from pattern recognition to new product selection and focused marketing. The merchant can move swiftly and fill the local shop with team-branded clothes and accessories with a procedure in place. When you discover anything fascinating in your big data research, you must codify it and include it in your business process. Link your big data analytics and your inventory and product management systems.

You must integrate the data to define the link between your big data analytics and your operational data.

Integration and Incorporation Stage

🟡Data is left in source systems, and a collection of views is defined to deliver and access a single perspective to customers throughout the company. For example, when a user requests customer information, the system transparently obtains the client's specific data. 

🟢The key advantages of virtual integration are near-zero latency in data updates propagation from the source system to the consolidated view and the elimination of the necessity for separate storage for aggregated data. However, limitations include the inability to manage data history and versions, the ability to apply the method only to similar data sources, and the fact that access to user data places an additional load on source systems that may not have been designed to handle it.

🟡Typically, this entails developing a new system that retains a duplicate of the data from the source systems to store and administer it separately from the original design. 

Data Warehouse is the most well-known example of this strategy (DW). The advantages include data version control and combining data from various sources. On the other hand, physical integration necessitates using a separate system to manage massive amounts of data.

🟢Many facets of data management, including data integration, are being impacted by big data. Data integration has traditionally focused on data transfer via middleware, including message forwarding standards and application programming interfaces (APIs) requirements.

These data integration principles are more suited to handling data at rest rather than in motion. The transition to a new world of unstructured data and streaming data has altered the traditional concept of data integration. 

🟡If you want to include streaming data analysis into your business process, you'll need innovative technology that allows you to make real-time choices. One of the essential goals of big data analytics is to find patterns that relate to your organization and to filter down the data set depending on the context. 

Big data analysis is simply one phase in the implementation process. After completing your big data analysis, you'll need a strategy for integrating or incorporating the findings.

🟢To take action, a firm that utilizes big data to forecast client interest in new items must combine the big data with operational data about customers and products. Suppose the corporation wants to use this data to acquire new items, modify the price, or manage inventories. In that case, it must combine operational data with its big data research findings. 

Companies in the retail business are starting to employ big data analytics to improve their consumer relationships and generate more tailored and targeted offerings. Integrating big data and operational data is critical to these endeavours' success. 

🟡Today's client gets e-mails about deals and coupon incentives for in-store or online purchases. Retailers aim to employ location-based services from the customer's mobile device in the future to determine where the consumer is in the shop and send a text message with a discount for use immediately in that department. In other words, a shopper may stroll into the store's entertainment area and get a text message offering a discount on a Blu-ray disc player purchase. 

🟢The retailer will need to combine big data feeds with real-time operational data on consumer history and in-store inventories. The analysis must be completed as soon as possible, and contact with the client must co-occur. Even a ten-minute wait may be too lengthy, and the consumer engagement time would be wasted.

You can also check out Data Analyst vs Data Scientist here.

Frequently Asked Questions

Mention some of the challenges of data integration.

Users have to back up their data. They Should not have outdated data and need to update the data regularly.

What do you understand by common data integration?

It is referred to as data warehousing, where we access and analyze the data for different objectives.

Name the steps performed in data integration.

Data preparation, metadata management, data franchising, and data management are various steps.

What is the need for data integration in DBMS?

It is required in DBMS as it makes the data accessible to various clients and stakeholders without duplicating the data.


In this article, we have discussed data integration, its need, stages of data integration, and tools for data integration, with a proper explanation of all the stages followed by some real-world examples.

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If you are interested in learning more about Big data, you must refer to this blog. And if you want to learn more about how virtualization is connected with big data, you must refer to this blog here. You can check out our blogs on Top 100 SQL ProblemsInterview ExperiencesProgramming Problems, and  Guided Paths. If you want to learn more, check out our articles on Code Studio

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