Data integration refers to the process of combining and harmonizing data from multiple sources into a unified, coherent format that can be put to use for various analytical, operational and decision-making purposes.
In today's digital landscape, organizations typically can’t function without gathering data from a wide range of sources, including databases, apps, spreadsheets, cloud services, APIs and others. In most cases this data is stored in different formats and locations with varying levels of quality, leading to data silos and inconsistencies.
The data integration process aims to overcome these challenges by bringing together data from disparate sources, transforming it into a consistent structure and making it accessible for analysis and decision making.
Unlike, say, data ingestion, which is just one part of data integration, integration carries through into the analysis phase of data engineering. This means it encompasses data visualization and business intelligence (BI) workflows. Thus, it carries more responsibility for data outcomes.
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Data integration involves a series of steps and processes that brings together data from disparate sources and transforms it into a unified and usable format. Here's an overview of how a typical data integration process works:
Overall, data integration involves a combination of technical processes, tools and strategies to ensure that data from diverse sources is harmonized, accurate and available for meaningful analysis and decision making.
Several types of data integration exist, each with its own strengths and weaknesses. Choosing the most appropriate data integration method depends on factors such as the organization's data needs, technology landscape, performance requirements and budget constraints.
Extract, load, transform (ELT) involves extracting data from its source, loading it into a database or data warehouse and then later transforming it into a format that suits business needs. This might involve cleaning, aggregating or summarizing the data. ELT data pipelines are commonly used in big data projects and real-time processing where speed and scalability are critical.
The ELT process relies heavily on the power and scalability of modern data storage systems. By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods.
With extract, transform, load (ETL), the data is transformed before loading it into the data storage system. This means that the transformation happens outside the data storage system, typically in a separate staging area.
In terms of performance, ELT often has the upper hand as it leverages the power of modern data storage systems. On the other hand, ETL data pipelines can be a better choice in scenarios where data quality and consistency are paramount, as the transformation process can include rigorous data cleaning and validation steps.
Real-time data integration involves capturing and processing data as it becomes available in source systems, and then immediately integrating it into the target system. This streaming data method is typically used in scenarios where up-to-the-minute insights are required, such as real-time analytics, fraud detection and monitoring.
One form of real-time data integration, change data capture (CDC), applies updates made to the data in source systems to data warehouses and other repositories. These changes can then be applied to another data repository or made available in a format consumable by ETL, for example, or other types of data integration tools.
Application integration (API) involves integrating data between different software applications to ensure seamless data flow and interoperability. This data integration method is commonly used in scenarios where different apps need to share data and work together, such as ensuring that your HR system has the same data as your finance system.
Data virtualization involves creating a virtual layer that provides a unified view of data from different sources, regardless of where the data physically resides. It enables users to access and query integrated data on demand without the need for physical data movement. It is useful for scenarios where agility and real-time access to integrated data are crucial.
With federated data integration, data remains in its original source systems, and queries are executed across these disparate systems in real-time to retrieve the required information. It is best suited for scenarios where data doesn't need to be physically moved and can be virtually integrated for analysis. Although federated integration reduces data duplication, it may suffer from performance challenges.
Data integration provides several benefits, which enable organizations to make more informed decisions, streamline operations and gain a competitive edge. Key benefits of data integration include:
Data integration brings together information from various sources and systems, providing a unified and comprehensive view. By breaking down data silos, organizations can eliminate redundancies and inconsistencies that arise from isolated data sources.
Through data transformation and cleansing processes, data integration helps improve data quality by identifying and correcting errors, inconsistencies and redundancies. Accurate, reliable data instills confidence in decision makers.
Integrated data enables smoother business processes by reducing manual data entry and minimizing the need for repetitive tasks. It also minimizes errors and enhances data consistency across the organization.
Data integration allows for quicker access to data for analysis. This speed is crucial for timely decision making and responding to market trends, customer demands and emerging opportunities.
Data integration is a fundamental aspect of any business intelligence initiative. BI tools rely on integrated data to generate meaningful visualizations and analysis that drive strategic initiatives.
Integrated data can uncover patterns, trends and opportunities that might not be apparent when enterprise data is scattered across disparate systems. This enables organizations to innovate and create new products or services.
Data integration is used in a wide range of industries and scenarios to address various business needs and challenges. The most common data integration use cases include:
For many years, the most common approach to data integration required developers to hand code scripts written in Structured Query Language (SQL), the standard programming language used in relational databases.
Today, various IT providers offer many different data integration tools that automate, streamline and document the data integration process, ranging from open-source solutions to comprehensive data integration platforms. These data integration systems generally include many of the following tools:
IBM® Databand® is observability software for data pipelines and warehouses that automatically collects metadata to build historical baselines, detect anomalies and triage alerts to remediate data quality issues.
Supporting ETL and ELT patterns, IBM® DataStage® delivers flexible and near-real-time data integration both on premises and in the cloud.
An intelligent data catalog for the AI era, IBM® Knowledge Catalog lets you access, curate, categorize and share data, knowledge assets and their relationships—no matter where they reside.
IBM named a Leader for the 18th year in a row in the 2023 Gartner® Magic Quadrant™ for Data Integration Tools.
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