What are the best patterns and technologies for data replication and synchronization in a data warehouse?

1 Full Load

Full load is the simplest and most straightforward pattern for data replication and synchronization, where the entire source data is copied to the target data warehouse at regular intervals, such as daily, weekly, or monthly. This pattern ensures that the data warehouse has a complete and consistent copy of the source data, and it is easy to implement and monitor. However, full load also has some drawbacks, such as high network bandwidth consumption, long execution time, potential data loss or corruption, and lack of real-time or near-real-time data availability.

3 Streaming Load

Streaming load is an emerging and advanced pattern for data replication and synchronization, where the source data is continuously captured and delivered to the target data warehouse in near-real-time, using technologies such as message queues, event hubs, or stream processing engines. This pattern provides the highest level of data freshness, responsiveness, and scalability, and supports complex and dynamic data transformations and analytics. However, streaming load also has some limitations, such as high cost and resource consumption, difficulty of error handling and recovery, and need for robust and reliable streaming infrastructure and pipelines.

4 ETL vs ELT

ETL (extract-transform-load) and ELT (extract-load-transform) are two common technologies for data replication and synchronization, where ETL performs the data transformation before loading it to the target data warehouse, while ELT performs the data transformation after loading it to the target data warehouse. ETL is more suitable for structured and homogeneous data sources, where the data quality and schema are predefined and consistent, and the data warehouse is optimized for query performance. ELT is more suitable for unstructured and heterogeneous data sources, where the data quality and schema are dynamic and diverse, and the data warehouse is optimized for storage capacity and flexibility.

5 Cloud vs On-Premise

Cloud and on-premise are two common deployment options for data replication and synchronization, where cloud refers to using a third-party service provider to host and manage the data warehouse and the replication and synchronization processes, while on-premise refers to using the organization's own infrastructure and resources to do so. Cloud offers many benefits, such as lower upfront cost, higher scalability, elasticity, and availability, and easier integration and maintenance. On-premise offers more control, security, and customization, and may be more compliant with certain regulations and standards.

6 Hybrid and Multicloud

Hybrid and multicloud are two emerging and complex deployment options for data replication and synchronization, where hybrid refers to using a combination of cloud and on-premise resources, and multicloud refers to using multiple cloud service providers. Hybrid and multicloud offer more flexibility, diversity, and redundancy, and can leverage the best features and capabilities of different platforms and providers. However, hybrid and multicloud also introduce more challenges, such as higher complexity, cost, and risk, and need for effective coordination, integration, and governance.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?