What is real-time data integration?

Real-time data integration involves capturing and processing data from multiple sources as soon as it's available, then immediately integrating it into a target system.
 

As with traditional data integration, real-time data integration functions to combine and harmonize data that may be siloed or inconsistent across the organization. The process includes steps from data ingestion through to data analysis. It allows users to make faster, more informed decisions.

The difference lies in the speed of data availability. Real-time data integration enables users to extract insights from data with minimal delay—typically within a few milliseconds.

Instant access to high-quality data from a wide range of sources (such as databases, spreadsheets, applications and cloud services) and formats gives businesses the agility to react quickly to change. It drives use cases such as business intelligence (BI), generative AI (gen AI), hyper-personalization and more.

Traditional data integration processes, such as batch processing, can’t support growing data volumes and the high-speed data needs of modern enterprises. Real-time data integration uses various streaming technologies and real-time data processes, ranging from open-source solutions to comprehensive data integration platforms, that are designed to operate continuously and at scale.

Data is the driving force behind innovation and a critical asset to data-driven organizations. But today’s data volumes are growing: the global datasphere is expected to reach 393.9 zettabytes by 2028. Data is also becoming more distributed and diverse, stored across various systems and repositories, cloud and on-premises environments.

Managing this increasingly complex mountain of data is a significant challenge. Organizations struggle with silos, data staleness (which occurs when there are gaps in time when data has not been updated), data governance and high network latency.

Compounding the challenge of modern data management is the pressure to be agile and innovative. Today’s markets are volatile, and organizations understand they need real-time data processing to respond quickly to change. Gen AI has also emerged as a competitive imperative, expected to raise global GDP by 7% within the next 10 years.

However, gen AI requires huge amounts of high-quality data to produce worthwhile outcomes. And, for use cases where gen AI models must respond in real-time (such as fraud detection or logistics) it’s crucial that data is provided as soon as it’s collected. Currently, only 16% of tech leaders are confident their current cloud and data capabilities can support gen AI.¹

Real-time data integration helps satisfy this contemporary need for immediate data access, while also providing the benefits of traditional data integration—that is, reducing data silos and improving data quality. It also increases operational efficiency by enabling faster time to insights and data-driven decision-making.

Real-time data is often categorized into two types: streaming data and event data. Understanding how the types differ and relate is critical for organizations pursuing real-time integration and insights.

Streaming data is real-time data that continuously flows from various sources, such as Internet of Things (IoT) devices, financial markets, social media activity or e-commerce transactions. Streaming data is fundamental to big data and real-time analytics, artificial intelligence (AI) and machine learning. It’s also core to other use cases that require continuous, up-to-date information.

Events are a single change, occurrence or action important to a system—such as a product sale, money transfer or a temperature reaching a set threshold. Related events are grouped together. The continuous delivery of these grouped events can be considered a stream or, more specifically, an event stream. However, not every instance of real-time data streaming contains events.

There are several real-time data integration tools and methods, including:

• Stream data integration (SDI)
• Change data capture (CDC)
• Application integration
• Data virtualization

Unlike batch integration, which integrates snapshots of data from various sources at specific intervals, stream data integration (SDI) integrates data in real time as it becomes available. It constantly consumes, processes and loads data streams into a target system for analysis. These capabilities enable advanced data analytics, machine learning and other use cases for real-time data, such as fraud detection and IoT analytics.

Implementing SDI requires streaming data pipelines, which move millions of data records between enterprise systems with low latency and high speed. These pipelines help ensure data integrity by significantly reducing the risk of data corruption or duplication—common problems when processing large data volumes quickly.

Data integration platforms such as Apache Kafka and IBM StreamSets can help organizations build streaming data pipelines tailored to their unique IT ecosystems.

Change data capture applies changes as they happen from data sources—such as Microsoft SQL Server databases, Oracle or MongoDB—to data warehouses, ETL solutions and other data repositories or target systems. Changes may include data deletions, insertions and updates. Unlike data replication tools, CDC only captures and replicates changes, not the entire dataset.

Essentially, CDC helps keep systems up to date in real-time. By sending only the data that has changed, it also reduces data processing overhead, data load times and network traffic.

The average enterprise uses nearly 1,200 cloud applications to operate, and each app generates its own data, which has led to data silos. However, modern workflows require real-time data flows between apps and systems. Application integration, also called software integration, automates and streamlines data transfer processes between software applications to enable real-time or near-real-time data integration.

Businesses often use application programming interfaces (APIs) to build and automate application integration workflows. An API is a set of rules or protocols that enables applications to seamlessly communicate with each other and exchange data.

Enterprises may also use webhooks and middleware to facilitate application integration.

Data virtualization creates a virtual layer that provides a unified view of real-time data streams from various sources, such as sensor data and equipment logs. This aggregate view eliminates the need for data to be moved, duplicated or batch processed elsewhere. These capabilities significantly reduce integration time and costs, while minimizing the risk of inaccuracies or data loss.

Data virtualization tools may also provide a semantic layer, a user experience interface that converts data into meaningful terms for making business decisions.

Additionally, data virtualization is a data integration solution for both real-time and historical data, creating a comprehensive view of an organization’s entire operational data ecosystem. This rich dataset is ideal for training the foundation models behind gen AI.

There are additional types of data integration processes that can be used in tandem with real-time data integration, depending on an organization’s data needs.

• Batch data integration: In batch integration, data is collected and stored in a group. Then, when a specified period of time has passed or a certain data quantity is collected, the data is moved and integrated as a batch. This method is ideal for compute-intensive data workloads and when time is not a motivating factor.
  
  
• Micro-batch data integration: Micro-batch integration is often considered a near-real-time alternative to traditional batch processing. In this method, data is processed in smaller, more frequent workloads, enabling near-real-time insights with lower latency.
  
  
• Extract, transform, load (ETL): The ETL data integration process combines, cleans and organizes data from different sources (such as ERP systems and databases) into a single, consistent dataset for storage in a data warehouse, data lake or other target system. ETL data pipelines can be a good fit when data quality and consistency are paramount, as the data transformation process can include rigorous data cleaning and validation.

• Extract, load, transform (ELT): Like ETL, ELT data integration moves raw data from a source system to a destination resource. However, instead of cleaning the data upfront, it loads raw data directly into data storage to be transformed as needed. This allows for more flexible data management. ELT is typically used when speed and scalability are critical.

While these types of data integration are some of the most common, the list is not exhaustive. For instance, some organizations may also use federated data integration, manual data integration and uniform data access integration methods.

Real-time data integration is useful for many industries and scenarios. Some common use cases include: