Understanding Data Processing Pipeline Types
Data, like water, is now an essential element of modern life. Necessary as it is, though, most of us hardly realize we’re swimming through oceans of data every day. And a recent publication from Cisco gives us a sense of how vast these oceans have grown:
Imagine we gathered up 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. But even more impressive is the digital infrastructure that can pipe blockbuster amounts of data around the globe, each and every minute.
This is why, in addition to matters of size and storage, understanding this speed of transfer is a fundamental aspect of roles that rely on data management and orchestration (like DataOps, data engineering, and business intelligence & data science).
And this necessitates a fundamental understanding of data processing pipelines, and how they help make it possible.
What is a Data Processing Pipeline?
Data processing pipelines (or, simply, data pipelines) refer to the method of taking data from one or many systems (data sources), and transporting that data to a separate destination repository, typically an on-premises or cloud data store, like a data lake or data warehouse.
The architecture of data pipelines typically involves four steps that happen in a sequential order:
Data Ingestion: The data pipeline process begins as data is ingested from its primary location. These source systems might be cloud databases like Amazon Web Services (AWS), APIs, SaaS apps, the Internet of Things (IoT) (or all of the above).
Data Integration: A subset of data transformation, data pipelines can then aggregate and format the collected data as the needs of the destination repository dictate.
Data Cleansing: Most source data is validated as its owners initially loaded onto its respective source destination. However, to ensure high data quality on delivery, the data pipeline process can check that the newly aggregated dataset is consistent, error-free, and accurate pre-delivery.
Data Copying: In the final processing step, the data processing pipeline loads a completed copy of dataset onto the destination repository.
Even though data processing pipelines sharing 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 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 team referred to when jobs required multiple cards to accomplish a task. And the necessary cards would be batched in hoppers and fed into computer’s card reader and run together.
As 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 batches of data can be prioritized 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. And, while the lack of human oversight needed to work can result in higher-quality dater sets, batch processing can speed up business intelligence projects due to how it efficiently processes large amounts of data all at once.
Streaming pipelines
As opposed to transferring large batches of data during specific windows, streaming data pipelines deliver a steady, real-time, low-latency trickles of data, continuously as needed. Source data may be ingested by the pipeline architecture the moment its created, placing great demand on hardware and software than those of batch processing pipelines.
While the need for more specialized equipment and maintenance can be a challenge, the benefits of streaming pipelines are that, for instance, data analytics can be applied 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 Data Ops specifically, cloud-native pipelines can break down siloes that traditional naturally form around data sources and analytics elements. By doing so, cloud-native pipelines better support demands for machine learning, streaming analytics, and real-time insights when compared to [transactional processing?}.
[Open-source pipelines]
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Business Use Cases for Data Processing Pipelines
One common use case of data processing pipelines for businesses to use them as a means of ensuring high data quality. Back in 2018 as reported by Gartner, poor data was already costing businesses and 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 also benefit business by squeezing more out of their analytics projects. Especially when data pipelines are fast enough to shift analysis from away from reactive models, instead using stream processing pipelines to fuel predictive analytics and data modeling.
And, finally, the increasing available of low-code data processing pipelines benefit business users who don’t have the technical data engineering and coding expertise (or staff who do). In these cases, businesses benefit from automated process flows that can rapidly fetch requested datasets from source systems on all their own. In doing so, the ability to write code no longer serves as a barrier to creating and using important data visualizations, like dashboards and reports.
Looking to Benefit from Data Orchestrators...Pick Your Pipelines Wisely
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 and deeply understands the problems that come with scale. Observability and alerting are built into the Shipyard platform, ensuring that breakages are identified before being discovered downstream by business teams.
With a high level of concurrency and end-to-end encryption, Shipyard enables data teams to accomplish more without relying on other teams or worrying about infrastructure challenges, while also ensuring that business teams trust the data made available to them.
For more information, visit www.shipyardapp.com or get started for free.