What are the best ETL design patterns and frameworks for extracting data from different sources?

1 Incremental loading

Incremental loading is a design pattern that extracts only the new or updated data from the source, instead of loading the entire data set every time. This reduces the amount of data transferred, the network bandwidth, and the processing time. Incremental loading can be implemented by using timestamps, version numbers, flags, or change data capture (CDC) mechanisms to identify the changes in the source data. Some ETL frameworks that support incremental loading are Apache Airflow, Apache NiFi, and Talend.

2 Parallel processing

Parallel processing is a design pattern that divides the data into smaller chunks and processes them simultaneously on multiple threads, cores, or nodes. This increases the performance and scalability of the ETL workflow, especially when dealing with large volumes of data. Parallel processing can be implemented by using partitioning, batching, or streaming techniques to split the data into manageable units. Some ETL frameworks that support parallel processing are Apache Spark, Apache Flink, and AWS Glue.

3 Staging area

Staging area is a design pattern that uses an intermediate location, such as a temporary table or a file system, to store the extracted data before transforming and loading it to the target destination. This provides several benefits, such as isolating the source and target systems, enabling data cleansing and validation, facilitating data reconciliation and auditing, and supporting incremental loading and parallel processing. Some ETL frameworks that use staging area are SSIS, Pentaho, and Informatica.

4 Data pipeline

Data pipeline is a design pattern that organizes the ETL workflow into a series of discrete and reusable steps, such as extraction, transformation, loading, validation, and monitoring. This provides several benefits, such as modularity, flexibility, maintainability, and testability of the ETL logic. Data pipeline can be implemented by using a graphical user interface (GUI), a domain-specific language (DSL), or a code-based approach to define and execute the ETL steps. Some ETL frameworks that support data pipeline are Luigi, Prefect, and Dagster.

5 Lambda architecture

Lambda architecture is a design pattern that combines batch and stream processing to handle both historical and real-time data. It consists of three layers: the batch layer, which performs ETL on the entire data set at regular intervals; the speed layer, which performs ETL on the incoming data streams in near real-time; and the serving layer, which merges the results from both layers and provides a unified view of the data. Some ETL frameworks that support lambda architecture are Apache Beam, Apache Kafka, and Azure Data Factory.

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?