What is Streaming ETL?
Streaming ETL is an approach for moving data from one database to another. ETL is a short-form term for extract, transform, load, referring to database functions required to accomplish this task.
First established in the 1970s, ETL looked much different than today. As data grew, so too did the need to store and manage it. Organizations began using multiple data repositories (databases) to keep various business information.
Streaming ETL pipelines are the result of decades of evolution. It was once a technique fraught with challenges owing to batch processing delays and disparate databases or locations. Today it means data can be processed and moved reliably in real-time.
How Does Streaming ETL Work?
During the ETL process, data is extracted from one location (source) and converted into a compatible format that can be added to another system, database, or data warehouse. The components are:
Extraction
In the extraction phase, data is collected from the source(s), which may include databases, data warehouses, data streams, or event streams.
Transformation
After extraction, various operations are performed on the data to transform it, which means cleaning and preparing it for reporting and analytics.
Load
Finally, the cleaned and transformed data is loaded into a destination database or data warehouse. This can include MySQL (relational) or MongoDB (non-relational) structures.
Whether you choose to stream with a real-time ETL pipeline or opt for batch ETL, these elements are the same.
When to Use Streaming ETL
Historically, ETL was performed in batches without any alternative. These data environments would leverage ETL software to extract data from the source in batches on a predefined schedule. The software would then transform the data and load it to the destination repository.
Today, an alternative is available, and organizations need to consider the benefits of batch data vs. streaming data. Each has its purpose, though streaming ETL has gained popularity, making it a favored approach.
The backbone of streaming ETL is event-driven architecture. This approach triggers data streaming when events – any action that triggers data recording – occur. Due to the speed and always-on nature of ETL streaming, the traditional method is best suited for things like online retail and banking transactions when analysis and to-the-minute movement are not required.
For time-sensitive data availability, including fraud detection, cybersecurity risk monitoring, and some IoT devices, streaming ETL is a better choice.
