How do you load data incrementally in a warehouse?

1 Benefits of incremental loading

Incremental loading offers several advantages over full loading, such as faster and more efficient ETL processes, reduced network and system resource consumption, improved data quality and consistency, and enhanced data availability and freshness. This is due to the fact that only data that has changed since the last load is processed, avoiding transferring and storing duplicate or unnecessary data. In addition, the risk of data loss, corruption, or duplication due to human errors or system failures is minimized. As a result, you can load data more frequently and update your data warehouse in near real-time.

3 Methods of incremental loading

Depending on the data source, data warehouse, and business requirements, there are various methods of incremental loading available. For instance, the 'Incremental load by last modified date' method involves using a timestamp column in the source table to track the last modified date of each record and comparing it with the last load date in the data warehouse to identify and load new or updated records. This method is simple and efficient, however, it requires that the source table has a reliable timestamp column and can handle deleted records. Alternatively, the 'Incremental load by flag' method involves using a flag column in the source table to mark inserted, updated, or deleted records and filtering them to load into the data warehouse. This method is flexible and robust but requires a flag column that is updated by triggers or other mechanisms and reset after each load. Lastly, 'Incremental load by CDC' uses a CDC tool or feature in the source system or ETL tool to capture and store changes in the source data and use them to load changed records into the data warehouse. This method is powerful and scalable but requires that both the source system or ETL tool supports CDC and can handle complex transformations and dependencies.

4 Tools for incremental loading

There are various tools and platforms that you can use to implement incremental loading in your data warehouse, depending on your data source, data warehouse, and ETL tool. For example, Microsoft SQL Server Integration Services (SSIS) is a widely used ETL tool that supports incremental loading with components such as CDC, lookup transformations, merge join transformations, and conditional split transformations. It also integrates with Microsoft SQL Server as a data source and a data warehouse, and supports other data sources and destinations. Additionally, Amazon Redshift is a cloud-based data warehouse that supports incremental loading with the COPY command or the MERGE command. It also integrates with Amazon S3 as a data source and staging area, and supports other sources and destinations. Finally, Stitch is a cloud-based ETL platform that supports incremental loading with Replication Key and Replication Method settings or Append-Only Incremental Loading feature. Furthermore, it integrates with several data sources and warehouses while providing a user-friendly interface and monitoring dashboard.

5 Best practices for incremental loading

Incremental loading is an effective way to optimize data warehouse performance, reduce storage costs, and ensure data freshness. To implement it successfully, you should define your business requirements and expectations clearly, such as the frequency, granularity, and latency of the data loading. Additionally, you need to select the appropriate method and tool for incremental loading based on your data source, data warehouse, and ETL tool capabilities and limitations. It's also important to document and maintain your incremental loading process, including the logic, metadata, and mechanisms used. Finally, you should monitor and troubleshoot your incremental loading process regularly with tools or alerts to detect any errors or anomalies in the data flow.

6 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?