What is the difference between batch and real-time ETL processing?

2 Real-time ETL

Real-time ETL is the opposite of batch ETL, where you process the data as soon as it is generated or received, without waiting for a batch window. For example, you might use real-time ETL to stream the data from your website or mobile app to your analytics platform, or to send the data from your sensors or IoT devices to your dashboard. Real-time ETL is suitable for scenarios where you need the data to be updated constantly, and where you can't afford to have any latency or delay in your data delivery. Real-time ETL also enables you to react faster to events and anomalies, and to provide more accurate and timely insights to your users or customers. Real-time ETL, however, tends to be more complex, expensive, and challenging than batch ETL, as you need to use streaming-oriented tools and frameworks, and deal with the issues of scalability, concurrency, fault-tolerance, and consistency of streaming data.

3 Hybrid ETL

Hybrid ETL is a combination of batch and real-time ETL, where you use both approaches depending on the type and source of the data, and the requirements and expectations of the data consumers. For example, you might use batch ETL for historical or archival data that is not time-sensitive, and real-time ETL for operational or transactional data that is time-critical. Hybrid ETL allows you to balance the trade-offs between batch and real-time ETL, and to optimize your data pipeline for different use cases and scenarios. Hybrid ETL, however, requires you to integrate and coordinate multiple tools and frameworks, and to manage the consistency and quality of the data across different modes of processing.

4 How to choose

The choice between batch and real-time ETL depends on several factors, such as the volume, velocity, and variety of the data, the frequency, timeliness, and accuracy of the data delivery, the complexity, cost, and reliability of the data processing, and the expectations, preferences, and feedback of the data consumers. There is no one-size-fits-all solution; you may need to experiment and evaluate different options to find the best fit for your data needs and goals. To determine which option is best for you, consider questions such as: how fast is the data generated or received and how fast do you need to process it? What resources do you have to store and process large amounts of data? How much transformations do you need to apply to diverse data? How often do you need to update or access the data? How accurate and consistent does it need to be? How complex and expensive are the tools and frameworks that you need to use for ETL? And how satisfied are your users or customers with the data that you provide?