What are the most common data warehouse design patterns for real-time reporting?

2 Lambda architecture

Another way to enable real-time reporting is to use a lambda architecture, which combines two layers: a batch layer and a speed layer. The batch layer processes and stores the historical data in a structured and optimized format, such as a star or snowflake schema. The speed layer processes and stores the recent data in a fast and flexible format, such as a key-value store or a document database. The data warehouse then merges the results from both layers to provide a unified view of the data. The lambda architecture can handle large volumes of data, complex transformations, and fault tolerance, but it also adds more complexity and maintenance overhead.

3 Micro-batch processing

A third way to enable real-time reporting is to use micro-batch processing, which is a hybrid approach between streaming and batch processing. Instead of processing each event individually, micro-batch processing groups the events into small batches, and processes them at regular intervals, such as every few seconds or minutes. This reduces the latency and the storage costs of streaming, while also improving the data quality and the consistency of batch processing. However, micro-batch processing still requires some buffering and synchronization mechanisms, and may not be suitable for very low-latency scenarios.

4 Change data capture

A fourth way to enable real-time reporting is to use change data capture, which is a technique that captures the changes made to the source systems, and applies them to the data warehouse. Change data capture can be implemented using triggers, logs, or APIs, depending on the source system. Change data capture can reduce the load on the source systems, and ensure that the data warehouse is always in sync with the source data. However, change data capture also requires some extra configuration and monitoring, and may not capture all the relevant changes, such as deletions or updates.

5 In-memory database

A fifth way to enable real-time reporting is to use an in-memory database, which is a database that stores the data in the main memory of the server, rather than on the disk. In-memory databases can provide very high performance, low latency, and high concurrency for real-time reporting, as they eliminate the need for disk I/O and indexing. However, in-memory databases also have some limitations, such as higher cost, lower capacity, and higher risk of data loss in case of power failure or system crash.