Real-time extract-transform-load (ETL) & integration describes systems that are processing & writing data as soon as it becomes available. This allows for near-instant analysis and decision-making based on the most up-to-date information. ETL patterns refer to the continuous processing of data, while integration broadly refers to writing the results of these pipelines to various systems (e.g. data warehouses, transactional databases, messaging queues). Adopting real-time ETL & integration architectures are generally regarded as an essential part of modernizing your data systems, and confer a number of competitive advantages to the company adopting them.
For the full version of this solution guide, please refer to:
- One pager: ETL & reverse ETL in real-time with Dataflow (PDF)
- ETL & reverse ETL Solution Guide and Architecture (PDF)
- Terraform code to deploy a project for ETL integration
- Sample pipelines in Java for ETL / Integration
Dataflow provides enormous advantages as a platform for your real-time ETL and integration use cases:
- Resource efficiency: Increased resource efficiency with horizontal & vertical autoscaling
- Unified batch & streaming: Dataflow’s underlying SDK, Apache Beam, allows developers to express batch & streaming pipelines with the same SDK, with minor modifications required to turn a batch pipeline into a streaming one. This simplifies the traditionally accepted practice of maintaining two separate systems for batch & stream processing.
- Limitless scalability: Dataflow offers two service backends for batch and streaming called Shuffle and Streaming Engine, respectively. These backends have scaled