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What is data integration?

See how data integration provides a unified view of your data using end-to-end workloads and tools.
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Data integration definition

Data integration refers to the process of merging data from multiple sources to create a complete and unified view of your data across the entire organization. It involves collecting data from various systems—such as databases, cloud applications, and APIs—then transforming and standardizing that data so it can be stored in a centralized repository, such as a data warehouse. The goal of data integration is to make data more accessible and usable for developing AI models, BI reporting, and analytics while still ensuring data quality, consistency, and compliance.

Key takeaways

  • Get an overview of data integration and why it matters.
  • Learn about the components that work together to provide a comprehensive view of your data.
  • Explore the benefits of data integration, including greater operational efficiencies and reduced costs.
  • Dive into common scenarios and use cases for data integration.
  • Find out about future trends, including the rise of AI-powered data platforms.

Key components of data integration

Key components


Data integration features a myriad of key components that work together to provide a unified view of your data. Here’s how it works:

Data sources

The first step of the data integration process is to identify the data sources that need to be integrated. Data sources are the origin points of data, such as databases, APIs, cloud platforms, and flat files.

Data extraction

Next, data integration tools and processes are used to collect raw data from sources. While there are many types of data integration methodologies, extract, transform, load (ETL) is the primary process used to migrate and merge data into a centralized warehouse. This extracted data is then mapped and validated for quality assurance.

Data transformation

The extracted raw data is cleansed, formatted, and converted into a standardized form of data that can be used effectively and accurately throughout the system.

Data loading

The transformed data is then loaded and stored in a centralized repository, typically a data warehouse or data lake, that has been designed for further analytics and reporting.

Synchronization and orchestration

Data synchronization coordinates the flow of data across systems, ensuring that the integrated data is synced and kept up to date over time.

Data governance, security, and compliance

Data governance practices should be established to manage how data is accessed, used, and protected across the organization. Security and compliance practices, such as access controls, encryption, and adherence to legal and regulatory standards, must also be enforced.

Data access and analysis

Once data is integrated, data scientists, data analysts, business users, and other users should have greater access to data for building AI models, informing machine learning, and conducting advanced analytics—leading to real-time insights and more informed decision-making.

Data integration benefits

Unite your data estate

Data integration provides organizations with a unified view of their data, allowing them to see all relevant data in one place rather than scattered across multiple systems, in turn eliminating sprawl and reducing duplication.

More informed decision-making

By ensuring access to consistent, accurate, and up-to-date data, data integration improves decision-making, allowing teams to respond quickly to changes and opportunities as they arise.

Greater operational efficiencies

Data orchestration tools automate the flow of data between systems, reducing the need for manual data entry—and greatly boosting operational efficiencies.

Better data quality and accuracy

During the data integration process, data is cleansed, standardized, and transformed, eliminating duplicates and identifying errors. As a result, data integration enhances data quality, reduces silos, and boosts cross-team collaboration.

AI and machine learning

By providing a larger, cleaner, and more connected dataset, data integration gives organizations the data foundation they need to create and manage AI models, reducing the time data scientists need to deliver value.

Reduced cost

Data integration lowers overall data management costs by consolidating systems and streamlining processes related to data access and analysis.

Use cases for data integration

AI and machine learning

Data integration lays the foundation for scalable, efficient, and insightful AI and projects by combining data from sources into a unified view. It is crucial in ensuring that models have access to comprehensive and consistent information. This process improves data quality, eliminates silos, and allows for more accurate and reliable model training. It also supports real-time analytics and decision-making by providing up-to-date, holistic datasets.

360-degree customer view

Many retail organizations use data integration to create a 360-degree view of its customers. By merging data from a variety of sources including CRM, ecommerce, and point-of-sale, organizations can gain deeper insights into customer behavior, preferences, and purchase history, resulting in personalized marketing campaigns, improved customer service interactions, and increased customer loyalty.

Cloud migration

Organizations undergoing digital transformation use data integration to support their migration to the cloud. As organizations transition from legacy systems to modern cloud-based applications, data integration helps maintain consistency across operations by merging data between on-premises databases and cloud environments. This ensures that financial reports, risk assessments, and compliance audits are based on accurate, up-to-date data—minimizing disruptions while maximizing scalability and flexibility.

Business intelligence and reporting

Data integration in BI reporting gives organizations the ability to consolidate data from multiple sources into a single, cohesive view, ensuring reports are accurate and comprehensive. A unified data foundation allows for deeper insights, trend analysis, and more informed decision-making. It also streamlines reporting processes by reducing manual data handling as well as the potential for duplicates and errors.

Future trends

The future of data integration is moving toward more intelligent, automated, and real-time solutions driven by AI and machine learning. As organizations across healthcare, finance, government, and education continue to adopt hybrid and multi-cloud environments, seamless integration of diverse systems will become critical for agility and innovation. Data integration will no longer just support decision-making—it will power dynamic, data-driven AI models, apps, and personalized experiences in real time.

To responsibly keep up with the rapid pace of innovation, organizations should look for a comprehensive, end-to-end data platform that can provide you with multiple experiences, including data integration, data engineering, data warehousing, data science, and real-time analytics.

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Additional resources

Explore additional guides, resources, and best practices to help you get started with data integration and Microsoft Fabric.
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Frequently Asked Questions

  • Data integration is the process of merging data from various sources into one single unified view. Extract, transform, load (ETL) refers to a specific type of data integration methodology where data is extracted from sources, transformed into a suitable format, and loaded into a system.
  • Yes. Extract, transform, load (ETL) is a process used to move and transform data from multiple sources into a centralized warehouse. Structured query language (SQL) is a language used to query, manipulate, and manage data within relational databases.
  • Data transformation refers to the process of cleaning, standardizing, and aggregating raw data, then converting it into a single format or structure. Data integration merges data from different sources to provide one unified view, often resolving inconsistencies in the process. While transformation focuses on reshaping data, integration focuses on merging multiple data sets into one coherent whole.
  • The four types of data integration methodologies include manual data integration, where data is entered manually; middleware data integration, which uses software to communicate between different systems; extract, transform, load (ETL), where data is extracted, transformed, and loaded into a data warehouse; and data virtualization, which allows real-time access to data without having to physically move it.
  • Some of the challenges faced during data integration include data inconsistency and poor data quality, as well as scalability when bringing together large datasets from multiple systems. Additionally, it can be challenging to maintain security and compliance across different platforms.
  • The three main data integration models are consolidation, federation, and propagation. Consolidation gathers data from multiple sources into a central repository, such as a data warehouse. Federation provides a unified view of your data through virtualization and does not require data to be physically moved. Propagation moves data between systems in real time or in batches, often using middleware.

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