With the rise towards cloud-native applications, the industry is shifting towards streaming ETL with real-time data stream processing.
Streaming ETL is the processing and movement of real-time data from one place to another. ETL is short for the database functions extract, transform, and load.
What is Traditional ETL?
The term ETL is a short form of Extract Transform and Load:
- EXTRACT data from its original source(s)
- TRANSFORM data by deduplicating it, cleaning it, reformatting it, combining it, and ensuring quality
- LOAD data into the target data destination
ETL tools enable data integration strategies by allowing companies to gather data from multiple data sources and consolidate it into a single, centralized location. ETL tools also make it possible for different types of data to work together.
A typical ETL process collects and refines different types of data, then delivers the data to a data lake or data warehouse such as BigQuery.
ETLs typically operate as "batch" systems, where they extract batches of data from a source system usually based on a schedule and then load the data into the desired destination.
What is Streaming ETL?
Streaming ETL (also called an event-driven ETL) is the processing and movement of real-time data from one place to another. This entire process occurs against streaming data in real-time in a stream processing platform. This type of ETL is very important given the velocity with which new technologies are generating data.
As the data world is ever-changing, providers find that they must now respond to new data in real-time as the data is generated. A purchase or reservation process is a good example of a streaming ETL in action. When you book a product or service, the transaction data is sent to, or extracted by ETL where transformations and algorithms can be run before delivering the data to the data warehouse destination, where even more automation can take place.
This can potentially enable the hotelier or service provider to make realtime, proactive decisions about a guest or purchaser, offering a more seamless experience by shortening the time between purchase and action.
Two Main Benefits of Stream Processing
- Fresh data will always be available because the processing is being done one event at a time in realtime, reducing the latency of data.
- The latency between the data capturing event and any processing and warehousing is reduced, allowing customers to make decisions and take action sooner via automation
Conclusion
At Calibrate Analytics, we’ve worked hard to introduce streaming features into Launchpad. Contact us today to find out how we can help you integrate this realtime, game-changing architecture as a solution to your needs.
Launchpad is our powerful and easy to use one stop shop to transfer data from an array of domains to destinations of your choice. It helps customers easily access their data and transfer it to their preferred data warehouse where it can then be transformed, analyzed and visualized.
