As anyone in IT operations will tell you, the demand for DataOps is exploding. As just one indicator, the global DataOps platform market is anticipated to experience “exponential” growth through 2032, more than tripling its current value at a Compound Annual Growth Rate (CAGR) of 25.7%.
This means, over the next decade, chances are good that if you aren’t already dabbling in DataOps, you will be soon. Rightly so, as the amount of data in our daily lives grows by the minute. At the same time, technology costs continue to fall, while improved data connectivity through hybrid and multi-cloud environments makes it even easier to incorporate digital transformation in business.
And all this growth rests on one simple yet vital aspect of DataOps: pipelines, the basic infrastructure that’s making all this growth possible.
That’s why it’s increasingly essential in business to understand what DataOps pipelines are and how to create one.
DataOps pipelines: The basics
DataOps (short for data operations) is an offshoot of DevOps and drafts off the former’s use of Agile Methodology for software development and data governance. At its most basic, this includes a holistic approach to the people, processes, and tech needed to build and automate data pipelines. Doing so encompasses four main practices: continuous integrations/continuous development (CI/CD), data orchestration, testing, and monitoring.
Data pipelines are designed to port data from one place to another. But doing so involves more than copying and pasting, as we might do with text in a word processor. The typical A-to-B flow of data through the pipeline will commonly include data integration, data transformation, validation, version controls, and more as the data itself is used in the DevOps lifecycle.
All this processing is important for ensuring that data doesn’t just make it out the other end of the pipeline. It needs to be of use to stakeholders when it does. Further, while the specifics of data pipelines themselves can vary, each will involve some data ingestion, the storage and management of data, data processing and analysis, and data visualization.
Exactly what it sounds like, a given pipeline ingestion process begins by drawing new data in from wherever it resides, typically a repository like a data lake or data warehouse. Normally, ingestion occurs intermittently, batch by batch, or in real-time as a smaller, continuous stream of raw data.
As the data flow moves through the pipeline, it’s organized and stored as datasets. As part of the process, the pipeline will optimize stored datasets and apply validation as needed, ensuring data quality remains high while processed into whatever form use cases dictate.
These data sets can then be analyzed by data scientists and visualized using dashboards for use by other data professionals.
DataOps pipelines (essentials for starting out)
With a basic understanding of DevOps and data pipelines, we can wrap our heads around the groundwork each needs in order to be built. And that groundwork revolves around clearly outlining what your pipeline needs to accomplish.
As with investing in any new piece of software or tech, every pipeline first needs a use case, and for DataOps pipelines, these typically include the joining of existing but disparate databases, the need to pull user data together from multiple sources, or moving data from legacy storage to modern systems. Writing strong use cases is an art unto itself, so they're worth the time it takes to get them right.
With your use case clearly defined, four major areas of consideration are involved in choosing the right data pipeline architecture, with speed being the first. Of course, in big data, faster isn't always better. Specifically, faster isn't always a necessary expenditure.
Think of it this way:
Speed (i.e., the lack of latency as data goes from one point to another) is a must-have in certain use cases. For instance, text prediction APIs powered by machine learning succeed or fail based on their ability to keep up with us as we type away on our keyboards.
This requires faster data transfer than a pipeline built to collect point-of-sale information from a national chain of dog park breweries. In the latter case, near real-time data transfer would be overkill if franchise owners wouldn't need each batch of data until the following business day. However, throughput and reliability might be dual priorities in the latter's pipeline architecture.
Throughput refers to the volume of data the pipeline can handle at any given time. Like speed, pipelines built to haul and handle more data than they actually have the capacity for could be seen as inefficient. (Unless you've prioritized adaptability when developing your pipeline architecture.) And no matter your priorities, reliability by way of data accuracy and consistency will be a crucial factor.
Finally, it's wise to account for how adaptable and/or scaleable your pipeline will need to be. For example, will data ingestion take place via a consistent set of data sources? Or will you be adding new data sources periodically over time? Adaptability determines how set-in-stone your data pipeline architecture will be. Adaptability is also the starting point of the pipeline setup process.
Building on the basics
Once you’re ready to build a pipeline based on everything above, it’s time to drill down and make specific, tactical decisions. When creating a pipeline from scratch, you’ll need to prepare your data for ingestion, pick which tools and technologies you’ll use in the creation process, establish what level of automation your pipeline will use, then monitor and troubleshoot any problems that arise within the pipeline process itself.
To select which data to use in the pipeline, you’ll need to define specifics regarding the inputs (data sources) and outputs (data sets) your pipeline will bridge. Getting the most out of your data begins with understanding that applications or APIs may have totally different schemas than, say, sensor data from physical-based products that make up the Internet of Things (IoT). Harmonizing data sources at the point of pipeline ingestion is especially crucial from an analytics perspective since visibility upon ingestion removes the need for workarounds, like blind ETL-ing and ELT-ing.
Why pipelines are worth prioritizing
With pipelines established, the benefits quickly become apparent.
Fast, well-engineered pipelines accelerate the time needed to integrate new data sources as businesses grow. This benefit alone is vital, as semi and unstructured data accounts for up to 80% of the data the average company will collect.
And, while the business is growing, leadership can trust that the data they will increasingly rely on to make decisions will remain efficient, stable, and secure.
The result at the end of the day? Agility.
Customer needs and trends are evolving and changing at a frightening pace. However, a solid pipeline infrastructure provides the necessary confidence to keep pace with consumers without worrying about IT putting out fires behind the scenes.
What’s next? Partnering to keep up with data(ops) demand
As data demands grow, so too do the demands for DataOps, data science, engineering, and data analytics. This means that the capacities to design, build, and maintain data pipelines are increasingly mission-critical. So, it’s the savvy operator who knows working smarter is just as important as working harder. Often, the smartest moves involve the tools you tap into when custom pipeline creation isn’t an option.
Take Shipyard—we’re built from the bottom up to help those with data processing pipeline needs excel at record speeds (without burning out in the process). We offer an ever-expanding collection of integrations and solutions designed to help you do 10x the work in half the time.
And the ability to deal with more big data, faster, provides business teams the agility, flexibility, and scale they need to create and manage data pipelines for their unique needs. Now, the next step is to get started. Sign up for our free version today.
