From real-time analytics to actionable insights, the world of business is continuously pushed forward by the insights that data can offer. Data has become such a core part of business strategy that those who don’t elect data-driven decisions are often seen as outliers in their industry. Due to the sheer scale of data produced, we now have more insight than ever before to help guide our business decisions.
However, the final products of data analytics – insights, trends, and actionable advice – don’t materialize out of thin air. Before entering into the realm of analytics, data has to move through several stages. The journey data takes, moving from its raw form into highly structured insights, is called the data pipeline.
Data engineers are tasked with building efficient, responsive, and rapid data pipelines. With the invention of modern platforms, systems, and strategies, the modern data pipeline is now more effective than ever before. In this article, we’ll dive into everything you need to know about data pipelines, breaking down:
- What is a data pipeline
- What data pipelines do
- Features of data pipelines
- Future trends for modern data pipelines
What is a data pipeline?
A data pipeline is a system that processes a continual flow of data. It captures it in its raw form and then moves it through several stages. These stages include processing, transformation, organizing, integrating, and more.
Several distinct sources can feed into data pipelines, taking data across APIs, files, databases, live social media data, and more. Often, this data needs cleaned and aggregated before proceeding, helping businesses to ensure they only receive high-quality data.
Once data passes through several capturing, processing, and standardization stages, it is then delivered to target data management systems. For structured data, data warehouses are a popular choice for storing data. However, for unstructured and semi-structured data, this data infrastructure could be data lakes or flexible analytics platforms.
Data pipelines ensure that businesses have a constant flow of fresh, useful, and accurate data to conduct analysis on. Modern data pipelines are one of the foundational pieces of effective data architecture. Countless organizations use them all across the globe.
What do data pipelines do?
Data pipelines are composed of several stages; each one offers distinct interactions and operations with the data. For example, the early stages of a data pipeline would be extraction and transformation, taking data from sources and structuring it for analysis.
As we progress further down the data pipeline, we see phases like processing, integration, and loading. These mid to late processes ensure that data is unified, analyzable, and processable by applications that rely on data input.
Over the past decade, we’ve seen data pipelines become more automated than ever before. While a great deal of maintenance was once required, automated tools and structures have allowed data pipelines to increase their workflow and bandwidth without sacrificing quality.
An unfathomable amount of data is produced on a daily basis in 2023. In fact, internet users generate around 2.5 quintillion bytes of data every single day. As the volume of data that we produce has increased, data pipelines have had to adapt, improve, and optimize in order to manage the rising tide.
Modern data pipelines have a series of features that allow them to process lots of data without hindering quality. Here are a few core features of data pipelines:
- Monitoring Processes. Data pipelines use a whole host of monitoring and logging systems in order to track the location, status, and performance of data inside a pipeline. Reporting from these systems allows data engineers to continually optimize their pipelines, contributing to a more stable, effective, and rapid system.
- Distributed Networks. Especially considering the vast amount of data moving at any one moment, modern data pipelines use distributed computing networks and parallel processing to optimize performance and provide concurrent processing.
- Data Governance Policies. To ensure that the data that a pipeline is secure and follows data privacy rules, all data pipelines have governance policies in place. These governance policies dictate how a pipeline manages, processes, and stores data, helping businesses to comply with regulatory policies.
- Workflow Management Systems. Data pipelines use scheduling, task coordinators, and dependency management systems in order to effectively manage their workflows. These systems ensure that data continuously flows without inefficient processes hindering it.
- Data Processing Engines. Some data needs to go through processes like calculations, analytics, or application to ML algorithms before it becomes useful. Many modern data pipelines include data processing engines that facilitate this process without creating bottlenecks.
Data pipelines are incredibly complex. A series of components, systems, and tools all work together in tandem to support the movement of data from start to finish.
Future trends for modern data pipelines
The past few years have represented a turning point for modern data infrastructure, with the introduction and proliferation of new tools providing alternative and more effective ways of processing data. One of the most obvious changes that have occurred in the last decade is the introduction of real-time and streaming data, allowing organizations to make instant decisions based on up-to-date information.
Another contender that has distributed the data pipeline architecture world has been artificial intelligence and machine learning. Both AI and ML allow developers to automate large portions of the data pipeline, optimize its performance, and push the bounds of possibility.
The development of other technologies, like Natural Language Processing, has also allowed for developments within data pipelines. NLP allows pipelines to ingest written data with high fidelity. This provides the basis for large-scale analysis of social media data, customer reviews, and other textual information.
One trend within modern data pipelines is to do with security and data privacy. Especially in light of the development of AI, more developers are focusing on ensuring that their pipelines remain within the boundaries of global legislation.
Data pipelines are an essential part of modern data infrastructure, providing an architectural base for the capturing, processing, and usage of data. Without efficient data pipelines, businesses would be unable to generate insights, leaving the world without the power of data-driven decision-making.
Although incredibly efficient, room for progress exists for the modern data pipeline. With the rising power of AI tools and ML, the next decade could radically transform the data pipeline as we know it today, pushing us even further into an age of efficiency. Although we’re unsure how quickly these developments will come to pass, they’re certainly just on the horizon.