What are some of the data integration methods that support real-time or near-real-time data processing?

1 Streaming Data Integration

Streaming data integration is a method that uses data streams to continuously capture, transform, and deliver data from various sources to target systems. Data streams are sequences of data records that are produced and consumed in real-time or near-real-time. Streaming data integration can handle high-volume, high-velocity, and high-variety data, such as sensor data, web logs, social media data, and event data. Streaming data integration can enable faster and more accurate insights, improved operational efficiency, and enhanced customer experience.

2 Change Data Capture

Change data capture (CDC) is a method that tracks and captures the changes made to the source data, such as inserts, updates, and deletes, and applies them to the target data. CDC can reduce the latency and the load of data integration, as it only transfers the incremental changes, rather than the entire data sets. CDC can also preserve the history and context of the data changes, which can be useful for auditing, compliance, and analysis. CDC can support real-time or near-real-time data integration for various use cases, such as data replication, data synchronization, and data migration.

4 API-Based Data Integration

API-based data integration is a method that uses application programming interfaces (APIs) to connect and exchange data between different applications and systems. APIs are sets of rules and protocols that define how data can be requested, transmitted, and received. API-based data integration can support real-time or near-real-time data integration, as it enables direct and secure data communication, without relying on intermediate layers or formats. API-based data integration can also facilitate data interoperability, as it allows different applications and systems to interact and share data, regardless of their underlying technologies or architectures.

5 Micro-Batch Data Integration

Micro-batch data integration is a method that processes data in small batches, rather than as individual records or as large batches. Micro-batch data integration can balance the trade-off between latency and throughput, as it can deliver data faster than batch processing, but more efficiently than streaming processing. Micro-batch data integration can support near-real-time data integration for scenarios where data needs to be processed in short intervals, such as every few seconds or minutes. Micro-batch data integration can also handle complex and heterogeneous data, as it can apply transformations, validations, and aggregations to the data batches.

These are some of the data integration methods that support real-time or near-real-time data processing. Depending on your data sources, data quality, data volume, data velocity, data variety, and data requirements, you may choose one or more of these methods to optimize your data integration strategy and achieve your data goals.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?