What are the best ETL design patterns for loading data into a target database or data warehouse?

1 Incremental load

An incremental load is a design pattern that only loads the data that has changed or is new since the last load. This can reduce the amount of data to be transferred, improve the performance and efficiency of the ETL process, and avoid duplicating or overwriting existing data. To implement an incremental load, you need to have a way to identify and capture the changes in the source data, such as timestamps, version numbers, flags, or change data capture (CDC) tools. You also need to have a way to apply the changes to the target database or data warehouse, such as merge, update, or insert statements.

2 Staging area

A staging area is a design pattern that uses a temporary location to store and transform the data before loading it to the target database or data warehouse. A staging area can help you overcome some of the challenges of data integration, such as data cleansing, validation, transformation, aggregation, and consolidation. By using a staging area, you can also isolate the source and target systems from each other, reduce the impact of network failures or source system downtime, and optimize the load process by batching or parallelizing the data transfers.

3 Lookup pattern

A lookup pattern is a design pattern that enriches or validates the data by comparing it with another data source. A lookup pattern can help you add more information or context to the data, such as customer names, product descriptions, or reference codes. It can also help you check the accuracy or completeness of the data, such as verifying if the customer ID or product ID exists in the target database or data warehouse. To implement a lookup pattern, you need to have a way to access and query the lookup data source, such as a database, a flat file, or an API. You also need to have a way to handle the cases where the lookup fails or returns multiple results, such as using default values, error logs, or custom logic.

4 Error handling

Error handling is a design pattern that deals with the errors or exceptions that may occur during the ETL process. Error handling can help you ensure the quality and reliability of the data, as well as the continuity and stability of the ETL process. To implement error handling, you need to have a way to detect and capture the errors or exceptions, such as using try-catch blocks, error codes, or error tables. You also need to have a way to handle and resolve the errors or exceptions, such as using retries, skips, alerts, or corrective actions.

5 Dimensional modeling

Dimensional modeling is a design pattern that organizes the data into a specific structure that facilitates analysis and reporting. Dimensional modeling can help you improve the performance and usability of the target database or data warehouse, as well as the clarity and consistency of the data. To implement dimensional modeling, you need to have a way to identify and separate the data into dimensions and facts, such as using star or snowflake schemas, surrogate keys, or hierarchies. You also need to have a way to load and update the data into the target database or data warehouse, such as using full, incremental, or historical loads, or using slowly changing dimensions (SCD) techniques.

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?