What is a Distributed Data Pipeline and Why Is It the Future of Data Engineering?
The world runs on data. However, extracting insights out of data is far from easy.
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The world runs on data. The rise of data warehouses, data lakes, and analytical BI/AI engines are helping companies to revolutionize their operations and business models. However, extracting insights out of data is still far from easy.
What is a distributed data pipeline?
A distributed data pipeline moves data from various, separated locations to another while performing transforming and enriching the data long the way. This is suitable for organizations that have heterogeneous data sources that are not all located at a single site.
A distributed data pipeline can extract data from edge data sources such as sensors, machines, physical equipment, as well as data from the cloud such as databases and AI models. Then, it processes it together in a single workflow to unify the data under a common data model, to contextualize them as events, and to send them off to more relevant applications depending on the type of data processed. The distributed data pipeline is an essential component to the data engine.
You can learn more about it in our blog article here - Organization Need Data Engine
Data is inherently distributed and heterogeneous
The process for preparing data is called "ETL", which stands for "extract, transform, load." Organizations extract data from multiple sources, transform them into the right schema, and load them into a data warehouse or data lake. Once the data enters the data lake, BI/AI engines can work on this data.
ETL processes various data sources such as real-time data streams, databases, files and applications. These are sent to an ETL staging server and the ETL operation is then applied to the data. Post-processed data is then sent to the data lake.
All these data sources are very different. Within each data source category, there can be hundreds of different data types. This means that the data entering the data lake is very heterogeneous. BI/AI engines are not good at working with heterogeneous data. As a result, only a very small portion of the data lake gets utilized today.
Work with data as close to the source as possible for best results
A better way to work with data is to work on it as close to the source as possible. Why does this matter? There are multiple reasons:
The closer it is to the source, the more you are aware of the properties of the data, and the more effectively you can work on the data.
It is easier to remove non-useful data before data reaches the cloud. Most data is not useful, so this improves efficiency and reduces bandwidth and data cost.
You can apply analytics directly at the source, which can be highly impactful. You can then deliver cleansed data to the data lake or directly to BI/AI engines.
Tame your data at the source
Taming data complexity at the source is much more effective than taming it at the data lake. This is analogous to building dams to control floods on a major river - it is much more effective (and less dangerous) to build smaller dams on subsidiary streams than it is to build a huge dam on the river itself.
The above framework is called distributed ETL, or DETL. It is the future of data engineering. Software such as Prescient Designer enables organizations to apply DETL to any data source anywhere, and to build and manage distributed data pipelines at scale.