Explore data pipeline orchestration, its strategic role in data management, and how it differs from general data orchestration.
Updated November 14, 2024
The terms ‘data orchestration’ and ‘data pipeline orchestration’ are often used interchangeably, yet they diverge significantly in function and scope. Understanding these differences is not just an exercise in semantics; it’s a critical distinction that, if overlooked, could lead to misallocated resources and substantial financial implications when developing data infrastructure.
Data orchestration refers to a wide collection of methods and tools that coordinate any and all types of data-related computing tasks. This includes job process sequencing, metadata synchronization, cataloging of data processing results, triggering data loads and quality checks, detecting when one task is done and triggering another, and setting the timing of scripts and system commands.
In contrast, data pipeline orchestration is a more targeted approach. It zeroes in on the specific tasks required to build, operate, and manage data pipelines. Here lies the crucial difference: data pipeline orchestration is inherently context-aware. It possesses an intrinsic understanding of the events and processes within the pipeline, enabling more precise and efficient management of data flows. In contrast, general data orchestration lacks this level of contextual insight.
This article delves deep into data pipeline orchestration, exploring how its context-aware capabilities and potential for extensive automation can revolutionize the way data pipelines are managed, offering a strategic advantage in data handling and processing.
If you are interested in the broader context of data orchestration, including pipeline considerations, here is our dedicated article on the broader topic.
Data pipeline orchestration is the scheduling, managing, and controlling of the flow and processing of data through pipelines. At its core, data pipeline orchestration ensures that the right tasks within a data pipeline are executed at the right time, in the right order, and under the right operational conditions.
Imagine each data pipeline as a complex mechanism composed of numerous interdependent components. These components must work in harmony, driven by a defined operational logic that dictates the activation, response, and contribution of each segment to the collective goal of data processing.
The essence of data pipeline orchestration is its seamless management of these interactions. It goes beyond mere task execution. It’s about orchestrating a dynamic, interconnected sequence where the output of one process feeds into the next, and where each step is contingent on the successful completion of its predecessors.
Read More: Explore general data orchestration and discover how it differs from data pipeline orchestration
Data pipelines are embedded in highly technical execution environments that present challenges of process timing, workload distribution, and computational resource allocation. At the highest level, data pipelines can be seen as a seamless progression of data through various stages of processing. Universally, these stages span extraction, through transformation stages, and finally to loading into target systems (a.k.a ETL).
While this simple beginner’s perspective seems approachable enough, there are many aspects of data pipeline orchestration that exponentially make it more complicated. Take a few examples:
These types of complexities are daunting when approached from a general orchestration perspective. However, in the focused context of orchestrating data pipelines, a refined approach to the control over data movements and transformations becomes not only possible, but automatable (more on this below).
Read More: Moving Past ETL and ELT: Understanding the EtLT Approach
Given that data pipeline orchestration is a specific domain, let’s take a look at some of the primary technical components that should work in unison in order to streamline these data pipeline workflows.
These components are the backbone of data pipeline orchestration, working together to optimize the data flow, and ensuring that large-scale data processing is efficient, reliable, and scalable.
While data pipeline orchestration offers numerous benefits, it comes with its own set of challenges that organizations must navigate. Understanding these challenges is key for data engineering professionals to effectively design, implement, and maintain orchestration systems. Here are some common hurdles:
Addressing these challenges requires a strategic approach that includes selecting the right tools, adopting data pipeline best practices, and continuously refining processes as technologies and requirements evolve.
Read More: Data Pipeline Automation: The What, How, and Why
As explained above, data pipelines generally adhere to predictable patterns, encompassing key components like workflow definition, task scheduling, dependency management, and error handling. This predictability is a gateway to unparalleled automation opportunities.
As we push the boundaries of what is possible, we begin to see orchestration as a fundamental, yet initial phase in the journey toward full-scale data pipeline automation. Essentially, this means that the running and management of your data pipelines doesn’t require additional code or manual intervention. Sophisticated data automation platforms, like Ascend, eliminate the need for data teams to entangle themselves in the intricacies of conditional branching, hard-coded schedules, and manual reruns.
Ascend’s intelligent data pipelines are capable of ensuring data integrity from source to sink, autonomously propagating changes, and eliminating unnecessary reprocessing. They offer the flexibility to be paused, inspected, and resumed at any point, coupled with the ability to provide comprehensive operational visibility in real-time.
In Ascend’s ecosystem, data pipelines transform into dynamic, self-aware entities. They are capable of self-optimization and self-healing, adapting in real-time to the fluctuating demands and volumes of data. This level of intelligent automation signifies a paradigm shift. Here, data pipelines are not merely orchestrated; they are intelligently automated.