DataOps. The Foundation for AI, Apps, and Analytics

Data is no longer used just to analyze the business—it’s now the backbone of operationalizing it. This shift has dramatically escalated demands on data teams. They are under pressure to provide higher quality data faster, working with a complex array of different technologies, often with limited resources. Throwing more tools at data teams won’t solve these challenges - instead we need to rethink the way our teams work

With DataOps, data teams can unlock 10x higher productivity, improve reliability, and reduce downtime—all essential as organizations embrace new AI, apps, and analytics initiatives.

Read on to learn what DataOps is, why it matters now more than ever, and shared learnings from enterprises that have adopted DataOps.

---

What Is DataOps?

Defining the New Era of Data Engineering

15 years ago DevOps arrived to deliver huge advances in agility, time to market, and quality of software. So DataOps aims to do the same for today for data. How much of an impact will DataOps make? In its 2024 Market Guide for DataOps, Gartner® predicts data engineering teams can be 10x more productive by adopting DataOps.

As its name suggests, DataOps is all about operationalizing data. It orchestrates sophisticated workflows that transform raw inputs from source systems (operational apps, databases, sensors, logs, APIs, etc.) into reliable and trusted data products ready for consumption. It automates critical stages in the data lifecycle like ingestion, integration, transformation, and ML/AI processes, augmenting them with controls that take care of discovery, observability, quality monitoring, and governance.

Figure 1: DataOps sits above the data compute layer, and is responsible for critical tasks used to create reliable and trusted data products

As data volumes surge and use cases become more sophisticated, DataOps is essential for unifying, governing, and scaling your data—ensuring every insight and innovation is fueled by the most reliable information possible.

Data’s Growing Strategic Importance

AI, apps, and analytics are the most important data-driven software initiatives enterprises are working on today, shaping success or failure in the digital economy. But no matter how smart your models, innovative and responsive your apps, or insightful and timely your analytics, they are only as good as the data feeding them.

In real terms, high quality and trusted data gives you a competitive edge. You stand out through great customer experiences, you innovate faster, and operate with consistently lower cost and risk.

Why DataOps?

Despite data being ever more abundant, enterprises still face obstacles to unlock value.

• 1. Siloed, Fragmented Environments Legacy on-premise systems collide with cloud-native data stacks, leading to duplicate datasets, confusion over ownership, and a lack of cohesive standards. This fragmentation only grows more complex as organizations experiment with new data platforms and tools.
• 2. Limited Skills Availability Data engineers juggle heavy backlogs, work with suboptimal tooling, and struggle to collaborate effectively with adjacent teams such as software and ML engineers. The result is slower turnaround times, communication breakdowns, and missed opportunities to innovate.
• 3. Non-Differentiated Toil & Expense As data teams patch together a plethora of disparate tools to operationalize data, they sink excessive hours and budgets into complex configurations rather than focusing on delivering business value.
• **4. Flying Blind Without robust observability and governance controls, it’s difficult to ensure data quality, track lineage, or measure the direct impact data products have on business outcomes. This lack of oversight opens the door to poorly performing AI, costly errors, and missed insights with the added risk of regulatory and compliance issues.

This reality has spurred the need for a fundamental shift—a move toward DataOps that can dramatically streamline operations and foster innovation.

The benefits of DataOps

By applying DataOps practices, data engineering teams can:

Increased development agility and collaboration: DataOps streamlines the development lifecycle by automating testing, deployment, and continuous integration processes, enabling teams to respond to change more swiftly. This heightened agility fosters a collaborative environment where multiple engineering teams and the business work in tandem, breaking down traditional silos and accelerating innovation.

Improved speed, scale & predictability of data delivery: By implementing automated orchestration and monitoring, DataOps ensures that data pipelines are not only faster but also capable of handling exponential scale with consistent performance. The predictable nature of these processes allows for precise forecasting of pipeline behavior and resource needs, thereby reducing bottlenecks and minimizing downtime during peak loads.

Enhanced data quality, trust, and governance with cost transparency: DataOps integrates data quality controls, lineage tracking, and real-time observability to create a robust framework for ensuring data integrity and compliance. Additionally, by providing granular insights into resource utilization and costs, DataOps enables teams to optimize expenditures and maintain a transparent governance model, ultimately fostering greater trust in the data produced and consumed.

Challenges in adopting DataOps

A common challenge data teams encounter when shifting to DataOps is that the technology stack powering it is chaotic. It’s overloaded with a proliferation of vendors and fragmented tools—each handling just a tiny slice of the data lifecycle with very few that actually integrate cleanly For enterprises, this is more than just an implementation challenge — from simple dashboards to cutting-edge AI, a disjointed DataOps stack constrains their ability to turn data into a competitive advantage.

As an example of this challenge, consider the number of AI investments getting stuck in prototyping, never making it to production or delivering ROI. The fallout is significant: budgets spiraling out of control, missed opportunities to act on cutting-edge ideas, and no clear understanding of the value data products are supposed to bring. The promise of data is there, but for too many enterprises, it’s slipping out of reach.

The good news is that data teams don’t have to tolerate this challenge for much longer. A cohesive and streamlined data stack is coming in the form of the unified DataOps platform that is free from vendor lock-in.

Orchestration: The foundation for DataOps

With control, management, and visibility of both data and its associated metadata, workflow orchestration offers a unique architectural advantage in unifying the DataOps stack.

This is because orchestration connects to all your tools and data sources. It knows where your data comes from, where it’s going, and how it’s being used. With deep integration and unparalleled context, orchestration is the ultimate control plane to unify the DataOps layer, letting teams quickly adapt workflows, adjust pipelines, and stay agile as priorities shift.

Orchestration lets you integrate with the best tools on the market, so you’re never locked into outdated technology. Beyond just handling data pipelines, orchestration sets you up for what’s next. It’s the foundation for data science, machine learning, and AI operations, while also giving businesses the flexibility to avoid vendor lock-in and maintain strategic leverage.

Airflow: Leading the orchestration charge

Apache Airflow® isn’t just the leader in orchestration—it’s the industry standard. No other solution, open-source or proprietary, comes close to its adoption or impact. With over 3,000 contributors—more than Apache Spark® and Apache Kafka®—Airflow is central to the data teams at many of the world’s most innovative and sophisticated companies. It’s downloaded over 30 million times every month, and that number is continuing to rapidly grow.

The demand for reliable, secure, and scalable orchestration has never been higher, and Airflow’s user base reflects that. Once primarily used by data engineers, it’s now also a critical tool for AI/ML engineers, and software developers. Generative AI, MLOps, and real-time analytics rely on Airflow to deliver the high-quality, trustworthy data products these use cases demand.

Astronomer: From orchestration to DataOps

At Astronomer, we’re supporting the rise of DataOps, powered by the unstoppable momentum of Apache Airflow. Astronomer leads the Airflow ecosystem, managing 100% of new releases, contributing 55% of the codebase, and employing 18 of the top 25 committers and 8 PMC members.

Astro is our unified DataOps platform based on Airflow. It introduces exclusive capabilities that enable data teams to seamlessly BUILD, RUN, and OBSERVE all their data products, with further plans to incorporate other layers of the DataOps stack over time. To break the platform down, it applies DataOps best practices to every stage of a data product’s lifecycle:

• Astro Build: Developer tooling that empower engineers from multiple teams to efficiently build, test, and deploy data products on Airflow, even if they lack Python skills.
• Astro Run: Reliable, elastic, secure, multi-tenant data product delivery across hybrid and multi-cloud environments with detailed reporting on cost and usage
• Astro Observe: A single pane of glass to govern and optimize the data product lifecycle with full lineage, alerting, and proactive recommendations

Today over 700 customers, from startups to Fortune 500 enterprises, trust Astro to power their data operations.

Figure 2: Astro is positioned to be the leader in unified DataOps as the data landscape continues to evolve

AI-driven enhancements are also being added into Astro today that boost reliability, efficiency, and productivity across the entire data lifecycle. This includes using natural language authoring to build pipelines founded on best practices, automated tuning and self-healing pipelines, and proactive detection of data issues with expert recommendations to optimize costs, reliability, and upgrades.

The result? A unified DataOps platform that cuts through the chaos above the compute layer, replacing fragmentation with end-to-end visibility, control, and automation. With Astro, data teams achieve massive gains in reliability, efficiency, productivity, and the business value their data products deliver.

DataOps in Action – Use Cases and Success Stories

DataOps at Ford: PB-Scale Innovation for Autonomous Driving

Challenge: Ford engineers faced scalability issues with their legacy, cron-based system, which was ill-equipped to process over 1 petabyte of sensor data weekly for autonomous driving. This limited system hindered visibility and efficiency, delaying AI model training and tuning.

Solution: To overcome these obstacles, Ford adopted Airflow to automate and centralize complex workflows across both cloud and on-prem environments. They further enhanced their operations by implementing Astronomer’s enterprise-grade Airflow management platform, which provided integrated CI/CD capabilities and a unified view of hybrid pipelines, optimizing resource allocation for both CPU and GPU-intensive tasks.

Results: This strategic shift enabled the team to process over 1 petabyte of data weekly, run 300+ parallel workflows, and significantly reduce errors. Now, Airflow and Astronomer together manage thousands of pipelines daily, accelerating AI model development and boosting overall operational efficiency.

Read more in the Ford’s Airflow in Action case study.

DataOps at Northern Trust: Modernized Financial Services

Challenge: Northern Trust, responsible for managing $1.5 trillion in assets, was hindered by fragmented data workflows. Poor visibility into job execution and late detection of failures resulted in recurring data quality issues, manual troubleshooting, and delayed delivery of critical financial data.

Solution: The firm adopted Astronomer’s orchestration platform. This move centralized their data pipelines, provided real-time monitoring and alerts, and streamlined the troubleshooting process, ensuring that job failures were identified and resolved quickly.

Results: By implementing this solution, Northern Trust significantly improved data reliability and operational efficiency. The enhanced visibility and proactive management of workflows ensured timely and accurate delivery of analytics data products, reducing manual interventions and ultimately strengthening their data-driven decision-making process.

Read more in the Northern Trust and Astronomer case study.

DataOps at Autodesk: Cloud Transformation in 12 Weeks. 90% Fewer Errors and 33% Faster Deployments

Challenge: Autodesk’s legacy systems struggled to manage the increasing complexity of its cloud-based data workflows. The existing orchestration tools lacked scalability and visibility, resulting in frequent pipeline errors and slowed deployment times that hindered efficient data processing.

Solution: Autodesk turned to Astronomer. By centralizing and automating data workflows across its cloud environment, the company gained enhanced real-time monitoring, improved visibility, and seamless integration of diverse data sources—addressing the scalability and reliability challenges head on.

Results: The migration was completed smoothly and ahead of schedule, with no long-tail issues. The new Airflow platform eliminated major operational burdens, allowing teams to concentrate on accelerating product innovation and supporting Autodesk’s cloud transformation. Autodesk has extended its use of Astronomer in its CI/CD platform, leading to fewer data errors and faster code deployments.

Read more in the Autodesk and Astronomer case study and in the Airflow in Action blog post.

Building a Future-Proof DataOps Strategy

By embracing DataOps and partnering with Astronomer, you’re not only future-proofing your data operations but also positioning your organization at the forefront of a new era where data drives every decision and innovation. Experience the transformative power of unified orchestration and observability—experience Astro. Here’s what the DataOps journey looks like.

1. Evaluate Your Current State: Engage with Astronomer to assess your data operations and identify pain points.
2. Pilot Astro: Start with foundational use cases such as ETL and data delivery to realize immediate benefits.
3. Scale and Standardize: Gradually incorporate more complex workloads such as ML/AI Ops and formalize DataOps practices through a Center of Excellence.

Frequently Asked Questions

What is DataOps and why is it important?

DataOps is the practice of applying agile, DevOps-like principles to data engineering, streamlining workflows, automating processes, and ensuring high-quality data delivery. It’s critical for today’s enterprises because data is the operational backbone that powers AI, analytics, and business innovation.

How does DataOps differ from DevOps?

While DevOps focuses on software development and operational efficiency, DataOps is tailored to the unique challenges of data—its volume, timeliness, completeness, and integration. DataOps integrates orchestration, automation, and observability to transform raw data into trusted, actionable insights.

What challenges does DataOps solve for modern data teams?

DataOps addresses fragmented data environments, manual and error-prone processes, and the lack of unified visibility across complex data workflows. By automating and streamlining data operations, DataOps reduces downtime, cuts operational costs, and accelerates innovation.

Why is orchestration the foundation for DataOps? What about using tools that I also use today like data integration, transformation, cataloging?

Lets review each category of tool in turn:

• Data Cataloging Tools: These give a passive view of your data—great for “reporting the news,” but limited for actually managing or operationalizing data at scale. To even “report the news,” cataloging tools rely on tight dependencies with systems like warehouses and workflow orchestration, not to mention cross-team buy-in and adoption. This often creates roadblocks. If DataOps unification is the goal, cataloging tools are not the best place to start.
• Data Observability Tools: Like data catalogs, data observability tools also “report the news,” providing visibility but often without real control of data platform components. Although the insights from data observability tools are invaluable, this lack of control makes them ill-suited as the foundational building lock for an operating system for DataOps.
• Data Transformation Tools: Data transformation tools excel at enabling end users to repeatably prep data where complex logic is required. But they can feel like just one stop in the data lifecycle journey. Unless tightly coupled with a system that delivers a comprehensive view of the entire workflow, they have no clue where the data came from or how it’s going to be used downstream, both of which are critical for seamless DataOps. Plus, they’re often SQL-centric, built for analysts focused on reporting, not engineers working on AI or software applications.
• Data integration tools: Whether its legacy tools or the more modern approach, data integration vendors do the job of moving data from point A to point B well (it just might hurt your wallet!). But like transformation tools, they only address one part of the data lifecycle with no context on how the data is consumed.

How does the Astro platform support a DataOps strategy?

Astro empowers data teams by providing a unified platform that integrates:

• Astro Build for agile development
• Astro Run for reliable and scalable data pipeline delivery, and
• Astro Observe for comprehensive real-time observability. This integration ensures that data products are built, deployed, and maintained with the highest levels of trust and reliability.