What Is Data Integration? Meaning, Main Types and Examples

On the one hand, the modern world is all about the data that comes from countless sources. It lets you analyze the business in-depth and plan its successful future. On the other hand, handling all these sources and using all their data correctly is a big challenge, but it is necessary.

Retrieving the data and ensuring their consistency and accuracy, managing different data types, distributing the data to the business applications – all these factors define why data integration is important. There are lots of related specific issues. That’s why we have prepared this article. We start with the data integration definition and also explore other essential aspects. You’ll learn about the types of data integration, challenges, and benefits of using the technology.

Table of contents

  1. Data Integration Definition
  2. How Does Data Integration Work?
  3. Data Integration Patterns
  4. CDC (Change Data Capture) Data Integration
  5. Data Integration Techniques
  6. Benefits of Data Integration
  7. Challenges of Data Integration
  8. Data Integration Software

Data Integration Definition

Before we start diving deep, let’s consider the definition of data integration. What is it, and how and where does it get integrated?

The data integration definition is quite simple. It is the process of retrieving and combining different types of data from various sources. There, diverse data types are unified. Further, this data is stored in a dedicated repository that feeds the applications.

This way, the data integration meaning includes the most demanded task in data handling. It creates clean and consistent consolidated data. This way, data becomes the foundation for analysts, executives, and business managers to make the right business decisions.

How Does Data Integration Work?

The data integration workflow relies on establishing a connection between the data source and the “destination” system. Then it routes the data flow from the source to the target. Professional software solutions perform these tasks automatically, ensuring the absence of errors due to human factors.

The data integration architecture suggests having a kind of mediated schema that will cover other schemas of the data sources and merge them all into one whole. The data mapping is vital here. It reconciles the data of different types and matches their elements to the mediated schema. Some systems do it in the target data storage. Others transform the data before storing it. Software solutions may use different methods.

Have a look at the data integration architecture diagram below:

data integration architecture diagram

Often, organizations deal with several types of data storage, adjusting the data integration processes to each data type.

Data Warehouse Integration

Data Warehouse is a central repository storing data from many sources. Data warehouse integration means combining and unifying all that data. It also gets structured for easy processing by any department of an organization. An integrated data warehouse is often a part of the global environment involving phone systems, BI apps, and other related software. This way, all responsible employees can immediately access the necessary data in one place.

Integrated Data in Object Storage

Object Storage is a specific technology that stores the data in one repository and can distribute it across many devices. The distinguishing feature is a flat-file system for huge unstructured data volumes. An integrated data storage usually deals with archived files, backups, log files, images, videos, music files, etc. The integration of data storage solutions of such type is the same – the system has to transform the data in such a way to make it suitable for handling with other applications.

Data Lake Integration

An integrated data lake is a storage dedicated for any analytical tasks. The data stored in such “lakes” does not require indexing or specific preparations. Speaking of the data lake integration patterns, they are standard. First, the data migration from external sources happens to get into the internal data lake. Then, the system manages and registers that data. After that, the data gets to the dedicated interfaces for advanced analytics purposes.

Data Lakehouse

The data lakehouse combines the data warehouse and data lake. It provides both the data structuring and management of unstructured data. The data lakehouse integration helps to reduce the storage costs. It also saves time as it does not require moving the data between different systems.

The most essential component is the “pipeline” established between the “warehouse” and the “lake” (refer to the material about what is data pipeline for more information on this essential aspect). This way, analysts can get all the necessary data (both structured and unstructured) in one tier, optimized for various queries.

Integrated data storages are necessary for any organization, big or small.

Data Integration Patterns

Companies rely on data and access it through different applications. It is necessary to deliver the data to those applications and sync them all. Ensuring the freshness of the information is one more vital requirement. Therefore, we can define several data integration patterns for different scenarios.

  • Data Migration. The data is retrieved from one source and routed to the destination system. To set this data integration pattern, you need to define the source and how often you need to migrate the data, in real-time or by schedule.
  • Broadcast. The data is retrieved from the source and distributed to applications in real-time. It is one of the most common data integration approaches because it requires retrieving and sending only the data that has been changed since the last request. Thus, it saves time and resources.
  • Bi-Directional Sync. In this case, two systems work together, and the data is constantly in sync between them.
  • Correlation. The principle is the same as for bi-directional sync, but the data synchronization is not continuous – it happens only when both systems must have identical data for some task.
  • Aggregation. The data is collected from several sources and presented as a unified picture in one place for further processing.

The above data integration approaches are realized through the following practical methods:

  • ETL
  • ELT
  • Data Streaming
  • Application Integration
  • Data Virtualization

ETL Data Integration

ETL is a system that suggests extracting the data from the sources, transforming it according to some common standard, and loading the data into the destination system. Sometimes, people use ETL as a synonym for data integration, but it is incorrect. The difference between the data integration and ETL terms is that data integration has a broader meaning – it is the process of collecting data from different sources and compiling a comprehensive picture for the users. The ETL data integration is a more concrete process applied to the specific scenario – extract, transform, load.

The data integration in ETL means transforming it “in the middle” of the process. As a result, it gets to the data warehouse (or another repository) ready to be used by analytic solutions. This technology is widespread, and the best ETL tools are used by most organizations worldwide.

ELT Data Integration

ELT is the abbreviation for extract, load, transform – the approach suggests that the data from the sources gets to the repository, and there it undergoes a transformation. This way, ELT data integration is much better for dealing with large datasets. Loading the data is faster, and then it gets converted to match the target applications. Besides, the system can retrieve updated data and thus load a portion only.

Data Streaming

The streaming data integration means the data migrates from the sources to the destination repository continually. Instead of retrieving it in batches by schedule or manually, we have the real-time data integration in the data warehouse or data lake as updating non-stop.

Application Data Integration

The application data integration suggests migrating and syncing the data between different applications. It is the most common use case in businesses of any size. When various departments need the same data, this method is used. Accordingly, there are already plenty of software tools that ensure the automation of these processes.

Data Virtualization

Virtual data integration is a method similar to streaming. It also ensures real-time data delivery from the sources to the destination repository. However, with data virtualization, this process is not continuous. The data is requested and delivered on request only (a user or the dedicated software).

CDC (Change Data Capture) Data Integration

As we already mentioned earlier, the key demand for data integration is to ensure that the data is fresh. The most common method of accomplishing this task is the CDC data integration, usually applied to databases. The system checks whether the data has been updated in the database. Then it retrieves the changed data only to deliver it to the destination.

The advantage of this approach is getting the updated data only, thus saving time. Besides, it uses SQL scripts only. On the other hand, CDC data integration demands massive storage. We must keep three copies of the data. First, it is the original data set. Then, we need two database snapshots (the previous and current versions) to compare them for changes. It is not the best option for heavy workloads.

Data Integration Techniques

Depending on the volume of the data in work and the specific business demands, we can apply one of the three established different techniques for data integration. Let’s consider them more in-depth.

  • Data Consolidation. The data comes from different sources into the central repository. It is the default method that we often suggest when talking about data integration. The key factor in this method is the data latency, how long it takes to deliver the data to the repository after getting it in the source. The modern data integration technique allows for real-time or near real-time data transfer.
  • Data Federation. The data does not move physically. Instead, there is the virtual database with the unified data model. When the end-user queries that virtual database, the query is transferred to the correlated data source. The process is on-demand – there aren’t real-time integrations.
  • Data Propagation. The data in the central repository gets transformed and delivered to the destination applications. The data is updated in that repository directly, and then it has to be updated in the target systems too. It can be real-time or on-demand.
data integration techniques correlate with the data integration patterns

This way, you can see how the data integration techniques correlate with the data integration patterns.

Benefits of Data Integration

The advantages of using data integration tools are many. As we already mentioned, all businesses rely on data.

The data integration methods allow any business to explore the information efficiently. And while listing all the benefits of data integration may take many hours, we can define the three most essential values:

  • More accurate and trustworthy results. A clear understanding of KPIs and no errors are the direct data integration benefits, and this is what every business requires.
  • Data-driven decisions. Proper data integration across the company ensures that all departments can collaborate effectively and understand how they influence each other.
  • High efficiency. Without collecting and processing data manually, specialists can focus on more priority tasks. Powerful professional tools let them use all the advantages of data integration at full capacity.

Any business can identify the weaknesses and improve the strengths, and thus achieve success.

Challenges of Data Integration

It’s impossible to deny that appropriate data integration is a must for any organization. However, it can be challenging. We can’t say there are many disadvantages of data integration – when set correctly, it brings nothing but good. Still, those trying to configure it without professional help stumble across troubles.

Speaking of the data integration challenges, we can define the following ones as the most common and annoying:

  • Data or a part of the data is unavailable or lost. It can be a grave problem because the absence of some details spoils the results terribly. You should collect the data from all sources and store it in one place.
  • Delays in data delivery. The data is valuable when it is fresh. Otherwise, you risk making wrong decisions based on obsolete data. Accessing data in real-time is feasible with dedicated software tools.
  • Problems with data formats. This issue is the most burning of all challenges of data integration. The solution is applying professional tools for data transformation.
  • Low-quality data. It can be obsolete data, duplicate data, irrelevant data, etc. Using it, you are risking again getting incorrect results for your work. It is essential to check and validate the data before using it.
  • Insufficient users’ qualifications. All users must have the required skills to process and understand the results. It is the organization’s responsibility, but professional tools also help by providing detailed data break-down.
  • Security issues. The possibility of data corruption and data leaks are among the most threatening factors. That’s why it is essential to use quality data integration solutions that ensure a high level of security for the data.

All these problems are resolved by applying dedicated systems for data integration.

Data Integration Software

The data integration software is a tool or toolset designed to perform the practical tasks of collecting and extracting the data, delivering it to the system, processing, and preparing it for the users. A customer data integration software can be free or paid, with a convenient GUI for users with few or even without coding skills to operate it efficiently.

Large businesses apply enterprise data integration software – powerful systems with elaborate architecture. They retrieve and combine the data from many sources, prepare it for further use, and distribute it to all end-users in different departments. Such systems can include hundreds of various sources and move the information seamlessly.

Cloud Data Integration Solutions by Skyvia

Skyvia is a comprehensive cloud data integration platform for businesses. It is an ETL data integration solution. It extracts the data from any supported sources, transforms it, and loads it to the destination systems. It ensures the following qualities that are necessary for the efficient accomplishing the business goals:

  • Supports cloud applications, CRMs, databases, and data warehouses to import the data from them.
  • Supports reverse ETL — from data warehouses to cloud apps and other sources.
  • Automates all the data integration tasks necessary for the particular organization, according to custom logic.
  • Replicates the data from sources to the target storages (databases and data warehouses) with accurate data mapping.
  • Ensures bi-directional synchronization and loads the data to multiple applications.
  • Exports data to CSV, saving it in the file storage of uploads to FTP servers.
  • Provides detailed error logs for users to define any errors, causes of problems and solutions.
  • Ensures complete security with the Azure cloud features. It is mandatory for the best data integration platform, and Skyvia is one of the best services.

Thus, we have reviewed essential aspects of modern data integration, including its patterns, technologies, challenges, and advantages for any business. Stay tuned for the next article!

Amanda Claymore
Amanda Claymore
Content Marketer
Guide to evaluate ETL solutions