A Primer on Distributed Data Pipelines




Wissen Team


June 30, 2023

With the advent of cloud computing, a business is no longer restricted from trying out newer digital capabilities for fear of cost escalations. The SaaS-based subscription model of pricing brings high-end computing and digital services within reach of even small-scale businesses.

While this growth in digital is welcomed by enterprises with open hands, the real challenge surfaces when they struggle to make sense of their data pipeline — the core network responsible for moving data between the organization’s digital systems.

Over the past decade, there has been exponential growth in the amount of data that enterprise systems must handle both from customer-facing channels as well as from internal operations. But when data gets distributed over a large landscape of digital systems operating in a mix of cloud and on-premises deployment, enterprises need to go beyond the basics and build a newer distributed data pipeline to cater to the scale of growth.

What Is a Distributed Data Pipeline?

Enterprise applications today are hosted or deployed across multiple distributed environments, as we have already covered. To work efficiently, they need access to critical data from across the spectrum of business operations and, more importantly, ensure that the data is in the format they need and delivered at the right time.

A distributed data pipeline is the infrastructure or network that is connected to multiple data sources and orchestrates the transfer of the right format of data between systems on demand. In short, it is the data itself that is supplied to various business systems.

The Evolution of Distributed Data Pipelines

In a traditional environment, data extracted from multiple sources is modeled into the right schema and then stored away in data warehouses or data lakes. It is from these data warehouses or lakes that further computational processing is carried out by digital services such as analytical or AI services.

However, over time the data ingested by data lakes becomes populated with a large quantity of heterogeneous unstructured data. Besides, every data source present in the data pipeline may comprise multiple data types and categories. This furthers the complexity levels. To prevent this scenario, the concept of distributed data pipelines has evolved and is adopted today by several leading enterprises.

A distributed data pipeline, in simple terms, refers to the data infrastructure that is located closer to the source of data handling, local computing, and information management needs rather than following a model of centralized data management and processing via data lakes or warehouses.

Why Should Enterprises Pay Attention to Distributed Data Pipelines?

Gartner predicts that 95% of all new digital workloads will be deployed on the cloud by 2025. As organizations embrace cloud-native technology to run their mission-critical operations, they need access to powerful data services that translate incoming data into insights. Moreover, the incoming data will involve many data types, including streaming data which is exponentially larger than the workload traditional systems put on the enterprise data infrastructure.

With data coming in from all directions at multiple speeds and patterns, it is not a wise decision to sort and store it centrally in a data warehouse for further processing. Enterprises need to make real-time data from the source available to digital services that leverage the data for decision-making after computational processing. Distributed data pipelines allow such a configuration to be made.

This would result in the following three benefits for enterprises:

Better Awareness of Data

When data pipelines exist closer to the source, it presents an opportunity for digital services to explore and learn more about the data in an isolated view rather than studying it from a group of data within a data warehouse or lake. Data science can be applied to unique behavioral aspects of the data, and more credible knowledge about different data patterns can be established.

Eliminate Non-Useful Data

A major advantage of distributed data pipelines is that they can help prevent a large chunk of unstructured and non-useful data from being made available for processing by different cloud services. The minimal scope of coverage for distributed data pipelines makes it easy to achieve this feat.

Near Source Computation

With distributed data pipelines, it is now possible to run powerful analytics computation directly at the source of data. This allows for cleaner data to be generated for final consumption by different digital services.

Wrapping Up

Concentrating efforts to harmonize and make sense of data at the warehouse or data lake level is not encouraged in today’s high-speed cloud-driven technology ecosystem. To ensure maximum efficiency with minimal hassle, distributed data pipelines can help establish a more reliable data management hub to support growth ambitions. Get in touch with us to learn more about setting up and deploying your custom distributed data pipeline.