Theoretically, data and analytics should be the backbones of decision-making in business. Yet, for too many enterprises, there’s a gap between theory and reality. Data pipeline automation aims to bridge that gap. 

A quick search on “problems with the data stack” will show you that traditional data architectures often fall short: they’re overly intricate, expensive, and not optimized for performance. Ironically, while these unresolved issues remain, business leaders are racing toward the next technological wave: LLMs and AI, Automated BizOps, and cloud FinOps. 

Yet these innovations all require a solid data foundation, and raise the urgency to address and mend the significant cracks in the data stack that will hinder their full implementation. In this light, data pipeline automation emerges as the singular paradigm shift that revolutionizes data engineering and unlocks the next wave of technologies. 

In this article, we explore what data pipeline automation is, how it works, and why we need it to produce business value from our data programs.

What Is Data Pipeline Automation?

Data pipeline automation is an intelligent control layer in a data pipeline platform designed to autonomously orchestrate and manage changes within data pipelines. 

There is a lot embedded in this simple sentence, so let’s unpack this definition. 

  • What is an intelligent controller? Think of an always-on engine that knows about your code, your data, and the relationships between the two, and performs a wide variety of intelligent operations autonomously to keep them in sync. While we’ll delve into its mechanics shortly, the key takeaway is that this controller understands absolutely everything you are doing with your data. 

  • What does “autonomously orchestrate” mean? Essentially, this means that the running and management of your data pipelines doesn’t require additional code or manual intervention. There is no need for complex conditional branches, scheduled jobs, or reruns. In other words, you don’t need Airflow. With data pipeline automation, the expected orchestration to correctly run the pipelines is dynamically generated behind the scenes as you build. 

  • What does “manage changes within pipelines” mean? Data pipeline automation constantly detects changes in your data and code across pipelines. For example, a new column is added to the source table or you make a change in your code logic. Once it detects a change, it responds in real time to propagate each change and keep the data pipelines synchronized end-to-end. 

Let’s pause here. If all this is starting to sound like a lot, and you’d rather skip the reading and just see data pipeline automation in action, let us know. We’d be happy to show you. But if you’re following along, trust us, it’s about to get even cooler.

Why does all of this matter? Pipeline orchestration and pipeline change management are by far the largest time-sinks for data engineers, and are the root of most outages and accumulation of technical debt. By removing these and other technical data pipeline concerns from the engineering agenda, automation exponentially raises the day-to-day productivity of data professionals, as they shift their focus from routine tasks to high-impact projects that propel the business forward.

How Data Pipeline Automation Works

Data pipeline automation is the key to greatly simplifying the entire data stack. It goes beyond merely scripting individual tasks like ingestion or triggering transformations within a pipeline. Instead, it encompasses a completely unified and streamlined data pipeline process, where each step seamlessly blends with the next, from drawing raw data from the initial data sources, right through to the final destination of the derived data product.

Visual representation of the modern data stack.

For data pipeline automation to work, consolidation is key. The entire pipeline, encompassing all its varied steps, must reside under one roof or within a single interface. This cohesive environment is essential because the intelligent controller requires this unified structure to function effectively. 

If these pipeline steps are scattered across different tools, the controller’s ability to seamlessly oversee and manage them becomes impossible. With everything centralized, the controller can build and leverage a vast but coherent metadata model that captures every nuance of the data pipeline, ensuring nothing slips through the cracks. 

This model operates through three core stages: Fingerprint, Detect, and Propagate.


In this stage, a powerful SHA (secure hashing algorithm) mechanism creates a unique “fingerprint” for every set of data that arrives in the system. It also fingerprints every snippet of code that users provide to define the transformation steps in the data pipelines. The controller links them into DAGs (directed acyclic graphs) to lock in their dependencies. This results in a lightweight immutable web, enriched with unique metadata to drive the autonomous orchestration processes.

Visual representation of the fingerprinting framework that allows automated change management in Ascend's data pipeline automation platform.


The autonomous controller compares all the SHA fingerprints to detect change as soon as it is introduced. For each change, the controller follows the lineage in the code to identify the dependencies in the downstream transformations and linked pipelines. It then automatically generates a plan from these dependencies to propagate the change.

Diagram to represent how Ascend's data pipeline automation detects change.


The controller propagates all changes autonomously through the pipelines end-to-end. If any one operation breaks because of errors in the code, the platform pauses for a fix by the user. It then resumes from the point of breakage with the latest data sets, without reprocessing any workloads — saving costs and guaranteeing the integrity of the change from source to sink.

Diagram to represent how Ascend's data pipeline automation propagates change.

This robust mechanism allows for automation on an unprecedented scale. The controller maintains an ongoing awareness of every node in the pipeline processing network. It promptly identifies shifts in data and code, and carries out all necessary operations to propagate those changes and realign all datasets.

Visual representation of change propagation by

Visual representation of how data pipeline automation manages change. Real-time awareness of every node’s status in the entire pipeline network means the controller can swiftly detect alterations in data and code. The automatic response generates the correct order of operations to bring all datasets back into alignment.

Benefits of Data Pipeline Automation

So why do we need data pipeline automation? Executives are increasingly taking note of the ability of their engineering organizations to deliver the data and insights they need to steer the business. Especially in light of the growing demands on data by new technologies like AI, ML, LLMs, and more, they expect their engineering organizations to keep pace:

  • Teams need to build data pipelines faster.

  • New hire ramp time needs to be quicker.

  • Changes and fixes need to happen in minutes, not days.

  • Pipelines need to grow faster than the cost to run them.

  • Pipelines need to use underlying compute and storage intelligently.

  • Technology needs to be accessible to a much broader user base.

the current state of data engineering: data teams are red-lining

However, a disconnect persists. The C-suite, while focusing on the next wave of technologies, often overlooks the challenges data engineering teams face. The manual, time-consuming processes they grapple with impede efficiency, quality, and the overall return on investment in the entire data capability.

Misalignment between data professionals on the ground and their executive counterparts.

Broadly speaking, data pipeline automation offers a host of transformative benefits that are nearly impossible to achieve any other way:

1. Cost Reduction

When data automation is purchased as an end-to-end platform, data engineering teams can reduce software costs from dozens of point solutions by at least $156k per year.

Additionally, the capabilities of data pipeline automation inherently reduce costs wherever possible, and reduce all redundancy in data processing. Companies experience a 30%+ reduction in cloud computing resources for ETL with these techniques, and significantly raise the value returned for compute costs incurred.

2. Productivity Boost

When the data team is no longer worrying about debugging vast libraries of code or tracing data lineage through obscure system logs, the speed of delivery increases exponentially — requiring 90% less effort to produce data for analytics.

This means that engineers gain the capacity to shift into higher-order thinking to solve new data problems in conjunction with business stakeholders, and pursue implementation of new technologies that build on a solid data foundation. 

3. Scalability and Efficiency

With data sources multiplying and data volumes skyrocketing, manual and scripted data processes are no longer viable. Automation allows data pipelines to scale effortlessly, handling vast quantities of data without increasing human intervention or cost. Additionally, automated systems are more adaptable to changes in data structures or business needs. They can be reconfigured or scaled as needed, ensuring businesses remain agile in the face of change and allowing data engineering team productivity to increase by 700%

For instance, automation makes it easy and efficient for engineers to interact with development as well as production pipelines, and easily implement no-touch CI/CD workflows. With data pipeline automation, engineers can pause, inspect, and resume pipelines anytime and at any point, meaning they can conserve costs while diagnosing pipeline logic and investigating complex issues — even if there is no obvious failure.

4. Consistency and Reliability

Human error is inevitable in manual processes, and a common source of unexpected costs and loss of confidence. Automation eliminates these sources of error and ensures consistent data processing, reducing discrepancies and ensuring dependable results.

For example, data pipeline automation automatically assesses data in the pipelines against quality rules in real time. Data quality rules, or assertions, are configurable at every processing step in every pipeline and evaluate every data record. The moment a record does not meet data quality assertions, the system automatically takes action according to pre-configured rules.

5. More Time for Cutting-Edge Initiatives

Data pipeline automation isn’t merely about efficiency; it’s a catalyst for innovation and role transformation.

  • Democratizing data interaction: Automation opens doors for data analysts and data-savvy business professionals to participate in pipeline design, allowing more hands-on interaction with data and fostering a culture of inclusive decision-making.

  • Liberating data engineers: By eliminating the never-ending tweaks tied to iterative software development of the translation layer, data engineers escape the constraining “doom loop” common in many enterprise software projects. Freed from these routine tasks, they can channel their expertise towards groundbreaking projects and new solutions.

In short, automation reshapes the data landscape, prioritizes innovation and maximizes team potential.

The Inevitability of Automation in Technology Advancements

Automation has been a recurring theme in software engineering. When a new innovation triggers a wave of product development, startups and large innovators race to build point solutions. 

Through the practical use of these point solutions, the key areas of value crystallize, and engineers assemble all the working pieces to form the “perfect” tech stack. However, as time progresses, these tools, much like today’s modern data stack, reveal inefficiencies. This recognition paves the way for a transformative wave of integrated solutions that prioritize end-to-end automation.

To contextualize this pattern, let’s delve into three pivotal examples: RPA, React, and Kubernetes.

RPA (Robotic Process Automation)

  • Problem: In the early 2000s, sectors from manufacturing to healthcare were bogged down by manual, error-prone tasks like data entry and payroll.

  • Solution: Innovators turned to emerging machine learning and AI technologies, birthing RPA software. This technology streamlined repetitive tasks, offering both speed and enhanced accuracy.

  • Results: Companies rapidly adopted RPA platforms, like UiPath and Blue Prism, leading to improved efficiency across many industries, from finance to healthcare.


  • Problem: As websites grew feature-rich, they suffered from slow load times and inefficient data exchanges between servers and browsers.

  • Solution: Facebook’s engineers introduced React, which converted code into reusable components for dynamic rendering — optimizing the loading of specific web page components.

  • Results: React revolutionized web application performance, becoming a foundational tool in modern web development.


  • Problem: With the cloud’s rise, managing vast, similar virtual machine infrastructures remained manual and labor-intensive.

  • Solution: Google introduced Kubernetes, an automated platform for container orchestration, simplifying large-scale application management.

  • Results: Kubernetes transformed cloud computing, reducing the need for extensive manual oversight and powering numerous global applications efficiently.

Historical shifts in technology, exemplified by the rise of RPA, React, and Kubernetes, underscore a pattern: as industries mature, the complexities they face outgrow the patchwork of initial solutions. This evolution invariably drives the transition from disjointed tools to cohesive, automated platforms. Data management is on a similar trajectory. 

The initial burst of diverse data solutions, though innovative, has begun to show its seams in today’s data-heavy environment. As businesses become more reliant on accurate data, the inefficiencies of managing individual data tools become apparent. Just as RPA transformed routine tasks, React redefined web interactions, and Kubernetes streamlined cloud computing, data pipeline automation is the next inevitable step. It promises to unify and optimize the data lifecycle, ensuring businesses can keep pace with the increasing demands of the data-driven era.

The innovation cycle stages

Data Pipeline Automation Yields High-Leverage Capabilities

The domain of data pipeline automation is uniquely challenging in that:

  • Data is “heavy”, meaning it is costly to move and even more costly to process

  • Data in an enterprise has thousands of sources, each of which is well-defined

  • Data-driven business outcomes are well understood, but hard to achieve 

  • The space between sources and outcomes is chaotic and poorly understood

Automation has traditionally been synonymous with efficiency. However, when we delve into the world of data pipeline automation, we uncover a realm that transcends mere operational streamlining. It heralds capabilities once considered beyond our grasp.

The innovations now within our reach are nothing short of groundbreaking. From automatic propagation of change and guaranteed data integrity to quantifying data product costs and optimizing production costs. For forward-thinking companies, harnessing this pioneering technology is not just a recommendation — it’s an imperative. Lest they risk being left in the technological dust and buried in resume-driven architectures