Data, like water, is an essential element of modern life. Though necessary as it is, most of us hardly realize we’re swimming through oceans of data every day. A recent publication from Cisco gives us a sense of how vast these oceans have grown:
Imagine we collected every movie ever made up through 2021 and stored them all together. In that year’s Global Forcast Highlights, Cisco estimated the gigabyte equivalent of “every movie ever made” was approximately the amount of data crisscrossing the entire Internet every minute.
That’s impressive to say the least. But even more astounding? The infrastructure needed to continuously pipe blockbuster amounts of data around the globe, every single minute.
This is why, in fields like DataOps, data engineering, and business intelligence, the scale of big data is important. But in data orchestration, tech that allows for the reliable, real-time velocity of big data is mission-critical. And a fundamental enabler of this velocity are data processing pipelines.
What is a data processing pipeline?
Data processing pipelines (or just data pipelines) refer to the method of moving data from one place to another. The data needed may reside in one source location, or many. Typically, the destination repository for this data is a single, centralized repository, such as a data lake.
The process of moving data from sources to a repository typically involves four interlinked steps that, together, make up a pipeline's data architecture.
- Data Ingestion: First, data pipelines ingest data from source locations. These locations might be cloud databases like Amazon Web Services (AWS), APIs, SaaS apps, the Internet of Things (IoT), or all of the above.
- Data Integration: Data pipelines then aggregate and format the sourced data based on the technical requirements of its destination.
- Data Cleansing: To ensure high data quality, data cleansing makes sure the freshly integrated dataset is consistent, error-free, and accurate.
- Data Copying: During the final step, the data processing pipeline loads a completed copy of the cleansed dataset onto the destination repository.
Even though data processing pipelines share similar architecture, not all pipeline systems are built to run the same way. This is why the broad term “data pipeline” cannot be used synonymously with specific types of pipeline processes, like ETL and ELT.
And it's why we can't say that one pipeline system is better than another. Instead, each is simply better or worse in the context of a given situation or use case.
Types of data pipelines
Batch processing pipelines
The idea of a “batch job” in computer science goes way back to the days when CPUs took up entire rooms, and they ran through the use of physical punch cards. “Batching” at this time referred to when jobs required multiple cards to accomplish a task. The necessary cards would be batched in hoppers, fed into a computer’s card reader, and then run together.
As a specific type of data transfer system, batch processing ingests, transforms, cleanses and loads source data in large chunks, during specific periods of time, referred to as batch windows.
This type of data pipeline system is very efficient, since it can prioritize batches of data based on need and available resources. Batch processing is low-maintenance and simple compared to other data pipeline systems, rarely if ever requiring specific systems to use.
Streaming pipelines
As opposed to transferring large batches of data during specific windows, streaming data pipelines deliver a steady, real-time, low-latency trickle of data. Unlike batch processing systems, streaming pipelines ingest source data the moment it's created, placing greater demand on hardware and software than those of batch processing pipelines.
While the need for more specialized equipment and maintenance can be a challenge, streaming pipelines offer notable benefits, like the ability to apply data analytics in the moment, or at specific moments. This doesn’t make streaming pipelines superior. It simply means their use cases tend to be more project- and industry-specific.
Cloud-native pipelines
Cloud-native pipelines are more than data pipeline systems that are cloud-based. There are many cloud-based systems that aren’t cloud-native. The term refers to data pipeline systems that are built to take full advantage of everything cloud hosting services like Amazon’s Web Services (AWS) can provide.
These data pipeline systems afford greater elasticity and scalability than their non-native counterparts. For those in DataOps specifically, cloud-native pipelines can break down silos that naturally form around data sources and analytics elements.
Business use cases for data processing pipelines
One common use case of data processing pipelines for businesses is to simply help maintain high data quality. Back in 2018, as reported by Gartner, poor data was already costing businesses an average of 18 million dollars per year. That, and nearly 60% of organizations surveyed at that time weren’t even keeping track of how much poor data was costing them each year. Just two years later, a 2020 global research study by American data software company Splunk found that businesses using better data added an average of 5.32% to their annual revenue.
This means, if nothing else, using data processing pipelines to ensure data is accurate, complete, and consistent would be a wise business decision. But data pipelines offer many additional benefits.
Data processing pipelines help teams squeeze more out of their analytics projects. This is especially beneficial when processing pipelines are fast and reliable enough to shift analysis away from reactive models, the door to predictive analytics and modeling.
While the lack of human oversight needed to work can result in higher-quality data sets, batch processing can speed up business intelligence projects due to how it efficiently processes large amounts of data simultaneously.
Finally, implementing data processing pipelines increasingly requires less data engineering and coding skills. Lowering the technical expertise required to use data pipelines increases access to important forms of data visualizations, like dashboards and performance reports.
Getting the most out of your data processing (and pipelines)
So, yes, the oceans of data we’re swimming through on a daily basis are vast. But equally vast are the options we have to get the data we need from one shore to another. And with data orchestration, the trick is choosing the right vessel to do the work. Or building your own to meet your exact needs.
The Shipyard team has built data products for some of the largest brands in business. We deeply understand the problems that come with scale. That's why we engineered observability and alerting directly into the Shipyard platform. This allows product owners to identify breakages before business teams discover them downstream.
Shipyard products also offer exceptional levels of concurrency and end-to-end encryption. As a result, data teams enjoy more autonomy and less stress while they deal with infrastructure challenges. Working together, business owners and decision makers get data that's easier to understand.
For more information, get started with Shipyard for free.
