What Is Real-Time Streaming Integration? How ETL Tools Handle Custom Sources in 2026

Real-time streaming integration continuously captures and moves data as events occur, delivering sub-second to seconds-level latency for operational needs such as fraud detection, live inventory, cybersecurity, and IoT monitoring, whereas batch ETL processes accumulated data on scheduled intervals and is generally simpler and less costly to operate. Common streaming approaches include event streaming through durable logs such as Kafka, log-based change data capture (CDC) that reads database transaction logs for ordered inserts, updates, and deletes, query-based change polling for sources without log access, and hub-and-spoke architectures that reduce point-to-point integration complexity. Proprietary databases, legacy ERPs, and custom APIs complicate these patterns because they may lack accessible logs and require source-specific handling for pagination, authentication, rate limits, schema changes, and deletion tracking. Standards-based interfaces such as ODBC, JDBC, and REST can abstract these differences through reusable connectors, allowing integration tools to work with diverse sources through consistent access methods. The discussion contrasts hand-coded, serverless, hosted, and pre-built connector approaches, noting that custom development offers control but increases maintenance demands, while managed infrastructure and connector platforms can reduce operational work; CData Sync is presented as one option supporting CDC for selected databases and incremental extraction for many other sources.

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