Your data pipelines weren't designed for the challenges you're facing now, and they're definitely not ready for what's coming next. In this post, EASL co-founder John Derham walks through the six principles that separate fragile data infrastructure from systems built to scale: from thinking in workflows instead of connectors to embedding governance that enables speed.
Why do organizations spend millions on data engineers yet still can't trust their data pipelines? Because they're using legacy tools to solve modern problems. DataDevOps applies the automation and discipline that transformed software development to data infrastructure. These are the principles that make it effective.
The ETL platforms that have dominated enterprise data movement for the past two decades love to advertise their connector libraries. Hundreds of pre-built integrations, ready to plug in. Sounds efficient until you actually try to use them.
That’s because your data movement needs are never generic. You need specific business logic applied at specific transformation points. You need to handle edge cases the connector doesn't anticipate. You end up spending more time working around the connector's limitations than you would have spent building the workflow yourself.
Think about how DevOps teams approach this problem. They don't rely on pre-built deployment scripts that only kind of work for most situations. They build continuous integration and deployment workflows tailored to their actual requirements. Those workflows automate the specific tasks involved in building, testing, and deploying code changes for their environment.
Data infrastructure deserves the same treatment. Workflow-based approaches let you define exactly how your data should move, what transformations apply, what validations run at each stage. You're automating based on your business rules, not whatever the vendor decided was "best practice" 15 years ago.
If your team starts from scratch for every new data source, blame the tools. You're relying on infrastructure that was never designed for the flexibility modern businesses require.
Ask any DevOps engineer if they'd deploy code without version control. Ask if they'd make infrastructure changes without audit trails. The answer to both is obviously no. That's because these practices are fundamental to the work.
Now ask how many organizations apply the same standards to their data pipelines. Version control for data movement code? Rarely. Storing raw data feeds as immutable records before transformations? Sometimes. Complete audit trails showing who changed what and when? Almost never.
The gap exists because data teams have historically operated outside the disciplines that DevOps teams consider non-negotiable. That needs to end. Environmental controls for data aren't just about compliance or covering your ass when something breaks (although they help with both). They enable something more fundamental: the ability to move fast without breaking everything.
When you can audit processes and raw data from multiple sources, tracking down problems shifts from a multi-day archaeological dig to a straightforward exercise. When every change is versioned, rolling back a bad transformation takes minutes instead of hours. When raw data is preserved immutably, you can reprocess it with corrected logic without hunting down the original source again.
Most data problems get discovered the same way: Someone in operations or analytics runs a report that doesn't match their expectations. They dig into it and find that the data coming from upstream sources has issues. Maybe schemas changed without notice. Maybe data quality degraded months ago. Or perhaps a field that's supposed to contain dates sometimes contains strings.
By the time the problem surfaces, decisions have been made based on bad information. Reports have gone to executives and money has been spent. The damage is done.
End-to-end observability means catching these problems in the data before they become business issues. That requires transparency across all data functions: schema adherence, data quality checks, pipeline status, data drift detection. It involves monitoring at the record and variable level, because a date field that isn't consistently a date will break downstream processes in expensive and embarrassing ways.
This level of monitoring shouldn't be controversial. Gartner and McKinsey have documented that nearly 40% of technical debt in enterprise environments stems from poor data quality in failing integration systems. Organizations know they have data quality problems. They just lack the observability to identify exactly where those problems originate and how to fix them systematically.
Monitoring systems set up correctly turn auditing from a nightmare into routine maintenance. The goal isn't perfection, it's knowing where you stand at any given moment and having the tools to address issues before they cascade.
The concept of "data keepers" who control access and guard specialized knowledge needs to go away. This kind of specialization inevitably produces poorly documented data dictionaries, unclear communication about data sources, and bottlenecks that strangle any attempt at innovation.
Many organizations divide their data users into two groups: people who use data to operate the business (operations, customer service, sales) and people who analyze the business (analytics teams, data scientists, executives reviewing dashboards). These groups have different day-to-day priorities, but they're completely interdependent—and they need to share.
Make data shareable across functions with appropriate privileges. Let people experiment with new policies and procedures using real data. Ensure results get shared across teams instead of staying trapped in departmental silos. You'd be surprised what different groups don't know about each other's work and how vital closing that knowledge gap becomes to running efficiently.
When you apply IaC principles to data infrastructure, you shift your team's focus from building environments to automating and managing them through business logic. No more manually provisioning data handling servers, operating systems, and databases every time requirements change. No more one-off configurations that only work because someone remembers the magic settings from three years ago.
Your infrastructure specifications live in configuration files that anyone on the team can edit, review, and deploy. Changes happen through code instead of through console clicks and manual procedures. That means faster deployments, fewer errors, and dramatically lower operational costs.
The platforms your team already uses—Databricks, Snowflake, Azure, AWS—become tools you orchestrate through code instead of systems you manually configure. That's the difference between managing infrastructure and actually automating it. Your team stops being reactive and starts being strategic.
As AI and machine learning expand into more business functions, scrutiny on data handling will only intensify. The basics of protecting data at rest, including encryption, hashing PII, and maintaining backups, are table stakes. However, DataDevOps requires thinking about access at a more granular level.
Your data engineers need different permissions than your ML engineers. Your platform engineers need different access than your analytics team. Role-based access to every content library, sometimes down to the field level, becomes manageable within a DataDevOps framework instead of an overwhelming compliance burden.
These provisions are designed to speed things up, not slow them down. Good governance removes uncertainty and reduces risk. When people know exactly what data they can access and how they're allowed to use it, they move faster. When security is baked into the infrastructure instead of bolted on afterward, you stop treating it as an obstacle and start leveraging it as an enabler.
These principles don't work in isolation. You can't have workflows without observability. You can't enforce governance without environmental controls. You can't scale infrastructure without IaC. They function as a system that addresses the core challenges holding back data infrastructure in most organizations.
Yes, the companies that adopt these principles are solving technical problems, but they're also eliminating the frustration that comes from knowing there's a better way but not having the framework to implement it. They're stopping the cycle of hiring more people to do work that should be automated. They're treating data as the foundation of their technology and their business instead of an afterthought to core processing.
The organizations still limping along with 15-year-old ETL tools and manual processes will eventually hit a wall. The question that remains is whether they recognize that before their competitors do.
John is a visionary architect of innovative technology stacks. His products are state-of-the-art layers of integrated AI and proprietary contextualizing software, and the platforms have utility for measurement in industries including media, financial services, e-commerce, and various other B2C and B2B applications. He is a pioneer in building and leading diverse data analytics teams and strategies. He also has an uncanny ability to effectively communicate between technology and executive layers to advance innovative strategies and solve real-world problems. Derham has many noted successes in marketing, product, risk management, and other operational disciplines. John has a Bachelor of Science from the Villanova School of Business with a concentration in Financial analytics.
We're data geeks who love to chat with anyone who appreciates clean infrastructure and issue-free data streams.
