Making the Case for Data Ingestion Pipelines

Data engineering teams have access to a tremendous amount of information. However, collecting and consolidating all this information efficiently is hard, especially as companies add more and more data sources to the mix. This is where having well-designed data ingestion pipelines comes into play.

Data ingestion pipelines are a crucial part of the modern big data management ecosystem. They are how businesses pull information from the real world and transform it so that it can create tangible value. What's exciting is that today's leading cloud service providers, like AWS, make it easier than ever to build pipelines that are capable of handling big data volumes with incredible efficiency. The key is knowing what tools to use and how to customize data ingestion pipelines to the unique needs of the organization.

In this post, we explain what data ingestion pipelines are and where they fit in the broader data management ecosystem. We'll also cover how AWS simplifies the data ingestion process and empowers data engineering teams to maximize the value of their data.

What Are Data Ingestion Pipelines?

Data ingestion refers to the process of moving data points from their original sources into some type of central location. Data ingestion pipelines represent the infrastructure and logic that facilitate this process. They are the bridges that connect data sources to data repositories, like databases and data lakes.

So, when discussing data ingestion pipelines, there are really three primary elements:

Data sources can be anything from IoT devices and legacy databases to ERPs and social media feeds. The processing that happens in a data pipeline is relatively light compared to what happens during ETL (Extract, Transform, Load). And where data pipelines lead ultimately depends on what type of storage or processing data engineering teams need to do to accomplish their goals.

The types of data that data ingestion pipelines can move include both streaming data and batched data. Streaming data is information that is collected and processed continuously from many sources. Examples of streaming data include log files, location data, stock prices, and real-time inventory updates.

Batched data is information that is collected over time and processed all at once. Simple examples of batch data include payroll information that gets processed biweekly or monthly credit card bills that are compiled and sent to consumers as a single document. Both types of data are important to modern organizations with modern applications.

Building Data Ingestion Pipeline on AWS

Building data ingestion pipelines in the age of big data can be difficult. Data ingestion pipelines today must be able to extract data from a wide range of sources at scale. Pipelines have to be reliable to prevent data loss and secure enough to thwart cybersecurity attacks. They also need to be quick and cost-efficient. Otherwise, they eat into the ROI of working with big data in the first place.

For these reasons, data ingestion pipelines can take a long time to set up and optimize. Furthermore, data engineers have to monitor data pipeline configurations constantly to ensure they stay aligned with downstream use cases. This is why setting up data ingestion pipelines on a cloud platform like AWS can make sense.

AWS provides a data ingestion pipeline solution, aptly named AWS Data Pipeline, and an ecosystem of related tools to manage big data effectively from source to analysis. AWS Data Pipeline works for moving data between different cloud services, or from on-prem to the cloud.

It's scalable, cost-effective, and easy to use. The service is also customizable so that data engineering teams can fulfill certain requirements, like running Amazon EMR jobs or performing SQL queries. With AWS Data Pipeline, the biggest pain points of building data ingestion pipelines in-house disappear, replaced by powerful integrations, fault-tolerant infrastructure, and an intuitive drag-and-drop interface.

AWS gives developers everything needed to set up new-age data ingestion pipelines successfully. What's left is plugging these pipelines into a larger data management system that can scale and evolve with the organization over time.

Anthony Loss is an AWS Ambassador and Lead Solutions Architect for ClearScale.