What are the most common cloud-based ETL patterns?

1 Batch ETL

Batch ETL is the traditional and most widely used pattern, where data is extracted from sources at regular intervals, transformed in batches, and loaded into a target data warehouse or data lake. Batch ETL is suitable for scenarios where data volume and velocity are moderate, data quality and consistency are high, and analytics requirements are predictable and stable. Batch ETL can leverage cloud services such as storage, compute, and orchestration to scale up and down, reduce costs, and automate workflows. However, batch ETL can also face challenges such as latency, complexity, and dependency issues, especially when dealing with multiple sources and targets.

2 Stream ETL

Stream ETL is a modern and emerging pattern, where data is extracted from sources continuously, transformed in real-time or near-real-time, and loaded into a target data warehouse or data lake. Stream ETL is suitable for scenarios where data volume and velocity are high, data quality and consistency are low, and analytics requirements are dynamic and complex. Stream ETL can leverage cloud services such as messaging, streaming, and analytics to handle large and fast data, enable real-time insights, and support event-driven applications. However, stream ETL can also face challenges such as scalability, reliability, and security issues, especially when dealing with unstructured and sensitive data.

3 Hybrid ETL

Hybrid ETL is a flexible and adaptive pattern, where data is extracted from sources using both batch and stream methods, transformed using both batch and stream logic, and loaded into a target data warehouse or data lake using both batch and stream modes. Hybrid ETL is suitable for scenarios where data volume and velocity are mixed, data quality and consistency are variable, and analytics requirements are diverse and evolving. Hybrid ETL can leverage cloud services such as integration, orchestration, and governance to combine the best of both batch and stream ETL, optimize performance and costs, and manage data lifecycle and quality. However, hybrid ETL can also face challenges such as complexity, compatibility, and maintenance issues, especially when dealing with heterogeneous and changing data sources and targets.

4 Serverless ETL

Serverless ETL is a novel and innovative pattern, where data is extracted from sources using cloud functions or triggers, transformed using cloud services or scripts, and loaded into a target data warehouse or data lake using cloud APIs or connectors. Serverless ETL is suitable for scenarios where data volume and velocity are unpredictable, data quality and consistency are not critical, and analytics requirements are simple and occasional. Serverless ETL can leverage cloud services such as functions, services, and APIs to eliminate the need for servers, infrastructure, and configuration, reduce costs and complexity, and enable scalability and agility. However, serverless ETL can also face challenges such as performance, security, and integration issues, especially when dealing with large and diverse data sources and targets.