Understanding ETL
ETL, which stands for Extract Transform and Load, is a process that merges these three database functions. It's all about taking data from one place and moving it to another. The concept of ETL has been with us since the 1970s and 80s. Back then, the procedure was more linear, data processing was a bit slower, and analytics and reports weren't produced as often.
Comparing Batch ETL and Streaming ETL
The traditional ETL software would take chunks of data from a source system, often following a schedule, modify it, and then move it to a storage spot like a data warehouse or database. This is referred to as the “batch ETL” model. However, in today's fast-paced world, many businesses can't afford to wait hours or even days for their applications to process data batches. They need to react to fresh data instantly.
Instances of Streaming ETL
Streaming ETL comes into play in situations that call for a comprehensive view of the customer, especially those that enhance real-time interactions between the business and the client. For example, a client may be using a company's services while simultaneously looking for support on their website. All these interactions are transmitted to the ETL engine in a streaming fashion to be processed and converted into a format suitable for analysis. This evaluation can unveil details about the customer that aren't immediately evident from the raw interaction data.
Another instance where streaming ETL proves useful is in credit card fraud detection applications. When you swipe your card, the transaction data is sent to the fraud detection application. The app combines this data with more information about you and runs fraud detection algorithms. Finally, the app sends the approval or denial to the credit card reader. By utilizing streaming ETL, banks and credit card issuers can save hundreds of millions of dollars annually that would have otherwise been lost to fraud.
The Structure of Real-time Streaming ETL
The architecture of real-time streaming and traditional ETL are fundamentally alike. The ETL process largely consists of a data source, an ETL engine, and a destination. In the real-time streaming model, data from the sources serves as the input for ETL tools. This transformed data is then sent to the Data Warehouses, which serve as the heart of your data universe.
Data sources deliver data to a stream processing platform, which is the backbone for streaming ETL applications. The ETL application can extract a data stream from the source or the data source can send or publish the data to an ETL tool for transformation. After processing, the data is moved to the destination.
In conclusion, the transition from traditional batch ETL to streaming ETL represents a significant evolution in how businesses handle data for real-time applications. As we've explored, streaming ETL allows for immediate processing and analysis of data as it's generated, offering a competitive edge in scenarios that demand fast, informed decisions, such as customer interaction enhancement and fraud detection.
