A data pipeline architecture is complex. With the continuous advancement, it becomes even more challenging to comprehend the structure and be decisive about the tools and technologies for the data pipelines. However, it is easy to understand it when you connect it with the day-to-day scenario.
For example, a data pipeline is very similar to a water pipeline. Back in the old days, people used to walk miles to fetch water. Later, they invented a mechanism to bring water through water pipelines from one point to another, which saved them a lot of effort.
Similarly, a data pipeline can transfer data from one point to another through some intermediary steps. These steps are essential as these are the points where data is processed, enriched, and cleansed.
Need for a data pipeline
As most businesses turn data-driven today, the data pipelines become vital in providing good quality data for analytics, AI, and machine learning purposes. Unfortunately, data flows are not easy to maintain. This complexity requires sturdy and secured data pipelines for further analysis and visualization.
Data pipeline vs. ETL
A data pipeline is considered the parent set, the broad umbrella under which ETL is one of the mechanisms for data processing. ETL refers to the batch processing of data. For example, if you need a daily sales report, you can set up batch processing, taking the data at a particular time and processing it to generate the information.
If you want to move beyond this and need real-time analytics of your sales data, then you may not need ETL. Instead, it would help if you had real-time data streaming. There is a third type of architecture for a data pipeline – Lambda, which enables both real-time and batch processing of data in the same architecture.
In batch processing, data is mainly produced on-premise, whereas real-time data comes from various sources, including satellite sensors, log devices. In this, data comes as a message and requires tools like Kaka to process the data.
The data producers then get stored in the staging environment and then flowing to the data lake or data warehouse. Then, finally, it goes to the data consumer (the last point for the data streaming) to generate BI, operational reports, or visualization reports through various tools.