What Are Declarative Data Pipelines? A Guide

Runaway platform costs from tools like Splunk and Snowflake can feel unavoidable, but they are often a symptom of inefficient data processing. When your pipelines require manual tuning and oversized resources to maintain stability, your cloud bill inevitably climbs. This is where a strategic shift can make a massive difference. By implementing declarative data pipelines, you move from telling the system how to do its job to simply defining the what—the final state of your data. This allows the underlying platform to automatically optimize execution, reduce resource consumption, and streamline operations, directly impacting your bottom line and bringing predictability back to your budget.

Key Takeaways

• Define the Destination, Not the Directions: Shift your team's focus from writing step-by-step procedural code to simply declaring the final state you want for your data. This allows the underlying system to handle the complex execution logic, making pipelines simpler to build and maintain.
• Automate Stability to Reduce Engineering Toil: Declarative frameworks are designed for resilience, with built-in features like automatic error recovery and dependency management. This reduces the time your engineers spend firefighting brittle pipelines, allowing them to focus on high-value work instead of manual maintenance.
• Start with a Pilot Project and the Right Tools: You don't need to overhaul your entire infrastructure at once. Begin by identifying a single, impactful pipeline to migrate and select tools that match your team's existing skills, whether it's managing transformations with SQL or handling compute across distributed environments.

What Are Declarative Data Pipelines?

If your data engineering team spends more time fixing brittle scripts than delivering insights, you're not alone. The traditional, step-by-step approach to building data pipelines is often the culprit. Declarative data pipelines offer a modern alternative. Instead of micromanaging every task, you simply define the end result you want, and the system handles the rest. This shift from writing procedural code to defining outcomes is what makes declarative pipelines so powerful for creating resilient and scalable data workflows. It’s about working smarter to get data where it needs to be.

Defining the "What," Not the "How"

A declarative data pipeline focuses on the destination, not the journey. Think of it like using a GPS: you enter your destination, and the system calculates the best route. You don't provide turn-by-turn directions. Similarly, with a declarative approach, you specify what you want the final data to look like—a clean, aggregated table ready for analysis. You don't write code that dictates the exact sequence of steps. The underlying platform interprets your definition and automatically figures out the most efficient way to produce that outcome. This declarative programming model is a fundamental shift from traditional, imperative pipelines where every action must be explicitly scripted.

Prioritizing Configuration Over Code

This "what-not-how" approach is made possible by moving from custom code to simple configuration. Instead of writing hundreds of lines of Python or Spark, your team defines pipelines using straightforward configuration files, often in a format like YAML. This separates the business logic from the implementation details. The benefit is massive: pipelines become easier to read, modify, and manage for everyone, not just senior engineers. This abstraction allows your team to focus on high-value data modeling instead of getting bogged down in boilerplate code. It’s a key principle behind modern data processing solutions that simplify enterprise-scale data management.

Core Traits of a Declarative Pipeline

Declarative pipelines have a few key characteristics that make them so reliable. First, the system automatically manages dependencies, so you don't have to manually chain tasks together. If one dataset depends on another, the orchestrator processes them in the right order. Second, they are often idempotent, meaning you can re-run a pipeline multiple times and get the same result without causing errors or data duplication—a lifesaver for error recovery. Finally, the system handles the entire execution plan, optimizing tasks and resources behind the scenes. These core features create a self-healing and efficient environment, turning fragile data workflows into robust, automated systems.

Declarative vs. Imperative: What's the Difference?

To really get why declarative pipelines are such a big deal, it helps to compare them to the way things have traditionally been done. The core difference comes down to telling a system what you want versus telling it how to do its job, step-by-step. This shift from "how" to "what" has huge implications for your team's speed, your pipeline's reliability, and your bottom line.

The Old Way: Manual Complexity in Imperative Pipelines

Think of an imperative pipeline as giving a new hire a painfully detailed list of instructions for a task. You have to specify every single action, in the exact right order, and account for every possible exception. An imperative pipeline gives exact instructions for every step and the order to follow. This means your engineers are manually coding the logic for data extraction, transformation, loading, error handling, and retries.

This approach is incredibly rigid. If a data source changes, a network connection hiccups, or a new compliance rule appears, the entire script can break. Your team then has to dive back into the code, troubleshoot the issue, and manually patch the pipeline, which is a huge drain on resources.

The New Way: Simplicity Through Automatic Orchestration

Declarative pipelines flip the script. Instead of writing step-by-step instructions, your team simply declares the end state they want to achieve. You define what data you need, where it should go, and what it should look like. The system handles the rest.

It automatically figures out the most efficient way to execute the pipeline, handling complex tasks like orchestration, error handling, and updates behind the scenes. This abstraction is the key. Your engineers can focus on the business logic and the value of the data, not the low-level mechanics of moving it. This is the core principle behind Expanso's distributed computing solutions, which automate the execution of tasks across any environment.

How Declarative Pipelines Reduce Code Maintenance and Errors

When you’re not writing thousands of lines of code to define every step, you naturally reduce the surface area for bugs. Declarative pipelines simplify development and maintenance by abstracting away the complex execution logic. This means your team spends less time debugging brittle, custom scripts and more time building valuable data products.

This approach also makes your pipelines more resilient. The system can automatically retry failed tasks, adjust to changes in the environment, and manage dependencies without manual intervention. For data-heavy operations like enterprise log processing, this built-in resilience is critical for maintaining stability and preventing costly downtime. Your team is freed from constant firefighting and can focus on higher-impact work.

Speeding Up Development and Improving Teamwork

Declarative pipelines focus on what you want to achieve, not how to do it. You describe the final result using simple settings, and the system automatically figures out all the steps needed to get there. This approach dramatically accelerates development cycles because engineers aren't starting from scratch every time. They can use reusable components and configurations to build and deploy new pipelines in a fraction of the time.

This also fosters better collaboration. Data analysts and scientists can define their data needs in a clear, concise way without needing to be experts in pipeline orchestration. This shared understanding closes the gap between data engineers and data consumers, allowing your entire organization to move faster and make better, data-driven decisions. It’s a smarter way to work that lets you get to insights faster.

Why Go Declarative? The Key Benefits for Your Business

Shifting from an imperative to a declarative approach isn't just a technical upgrade; it's a strategic move that directly impacts your business's agility, reliability, and bottom line. When your data teams can focus on outcomes instead of getting bogged down in operational details, they can deliver value faster and more consistently. This approach simplifies how you manage everything from log processing to complex AI workloads, making your entire data ecosystem more resilient and efficient.

The core advantage is abstraction. By defining what you want the end state of your data to be, you let the underlying system figure out the most efficient how. This leads to more robust, scalable, and cost-effective data operations, freeing up your most valuable technical talent to solve business problems instead of wrestling with infrastructure. Let's break down the four key benefits you can expect.

Build Faster with Less Complexity

Declarative data pipelines let your engineers define the desired outcome of a data transformation without having to spell out every single step. This abstraction layer is a game-changer for productivity. Instead of writing and maintaining thousands of lines of complex, sequential code, your team can create concise configurations that describe the data's final state. This dramatically simplifies pipeline development and maintenance.

This approach means your team spends less time on boilerplate code and more time on high-value business logic. It also makes the entire system easier to understand, which speeds up onboarding for new engineers and improves collaboration across teams. When you can build and deploy pipelines more quickly, you can respond to business needs with greater agility.

Gain Stability with Automatic Error Recovery

One of the biggest challenges with traditional imperative pipelines is their fragility. A single failure can bring an entire workflow to a halt, forcing engineers to spend hours troubleshooting. Declarative frameworks are designed with resilience in mind. They often include automated reliability features, such as intelligent retries, dependency management, and data quality enforcement.

These systems can automatically catch issues like schema inconsistencies before a job even runs, preventing bad data from corrupting your analytics. Because the framework manages the execution logic, it can gracefully handle transient errors and recover without manual intervention. This built-in stability means fewer late-night alerts for your team and more trustworthy data for your stakeholders, strengthening your overall security and governance posture.

Scale Effortlessly with Smart Resource Management

As your data volume and complexity grow, manually managing resources for imperative pipelines becomes unsustainable. Declarative systems solve this by automatically optimizing the execution plan. The framework analyzes your pipeline's dependencies and determines the most efficient way to arrange and execute each step, ensuring tasks run in the right order and as quickly as possible.

This intelligent orchestration allows your pipelines to scale dynamically without requiring engineers to re-architect the workflow. The system handles resource allocation and parallelization, so you can process massive datasets efficiently across distributed environments. This is especially critical for demanding use cases like building a distributed data warehouse, where performance and scalability are non-negotiable. Your infrastructure adapts to your needs, not the other way around.

Cut Costs by Streamlining Operations

Faster development, greater stability, and smarter scaling all lead to one crucial business outcome: lower costs. By automating away the manual, error-prone tasks that consume so much engineering time, declarative pipelines significantly reduce your total cost of ownership. Your team can manage more pipelines with less effort, freeing them to focus on innovation.

The performance gains are also substantial. Optimized execution plans and serverless computing models can lead to dramatic cost savings on infrastructure. For example, some teams have seen up to a 98% reduction in costs for complex data transformations. When you choose a solution that streamlines operations, you're not just buying technology; you're investing in a more efficient and sustainable data strategy that delivers a clear return.

The Modern Toolkit for Declarative Pipelines

Switching to a declarative model doesn’t mean throwing out everything you know and starting from scratch. It’s about adopting a new set of tools designed to abstract away complexity so your team can focus on business logic. The modern data stack is full of incredible options that help you build, manage, and run declarative pipelines. Think of it less as a single product and more as a flexible toolkit where you can pick and choose the right components for the job.

You’ll find tools that specialize in different parts of the pipeline, from data transformation and processing to orchestration and compute. Some are great for teams that live and breathe SQL, while others are built for large-scale, distributed environments that span multiple clouds and edge locations. The beauty of this approach is that you can assemble a stack that perfectly matches your team’s skills and your organization’s unique challenges, whether that’s taming runaway cloud costs or ensuring data stays within a specific geographic region for compliance. The goal is to find a combination that lets you define the "what" and automates the "how."

Expanso: Declarative Computing for the Enterprise

For large organizations dealing with data spread across different clouds, on-premise data centers, and edge devices, the compute layer is critical. This is where Expanso comes in. It provides a declarative computing platform that separates your pipeline's business logic from the technical details of where and how the work gets done. You simply define the job, and Expanso’s distributed computing solution figures out the most efficient and secure way to process it, whether that’s on a server down the hall or in a cloud region halfway around the world. This approach is perfect for maintaining data residency and governance without having to build complex, brittle logic into every pipeline.

dbt for SQL-Based Transformations

If your team is strong in SQL, dbt (Data Build Tool) is an essential part of the declarative toolkit. It allows your analysts and engineers to transform data that’s already in your warehouse using simple SELECT statements. Instead of writing complex procedural code, you define your data models and their relationships, and dbt handles the dependencies and materializes them as tables or views. This makes the transformation process more transparent, testable, and collaborative. It’s a fantastic tool for focusing on the analytics and reporting layers of your data stack, letting you build reliable data models with a language your team already knows well.

Modern Orchestrators: Dagster, Prefect, and Kestra

Orchestration is the heartbeat of your data pipelines, and modern tools have embraced the declarative model. Tools like Dagster, Prefect, and Kestra are designed to manage complex workflows with a focus on reliability and observability. Unlike older, imperative tools, they let you define your pipelines as code or configuration, clearly outlining dependencies and data flow. Dagster, for example, excels at tracking data assets and providing a clear picture of your workflow's health. Kestra uses a simple YAML interface to define tasks, making it easy for anyone on the team to understand and manage the orchestration logic without deep programming knowledge.

Apache Spark and Delta Live Tables

When you’re working with massive datasets, you need a processing engine that can handle the scale. Apache Spark has long been a go-to, and its newer features are leaning into the declarative model. A great example is Delta Live Tables, a framework built on Spark that lets you build reliable ETL pipelines declaratively. You define the desired end state of your data—the tables you want to create and the transformations they require—and Delta Live Tables automatically manages the underlying infrastructure, data quality checks, and error handling. This lets your team build scalable, production-ready pipelines without getting bogged down in the operational details.

How to Choose the Right Tool for Your Team

With so many options, how do you pick the right ones? The best approach is to start with your team’s needs and existing environment. Consider the skills you have in-house—are you a SQL-heavy shop or a team of Python experts? Look at the complexity of your data workflows and where your data lives. If you’re managing a distributed fleet or have strict data residency rules, a solution like Expanso for edge machine learning might be a foundational piece. The key is to choose tools that integrate well and solve specific problems, creating a cohesive stack that makes your team more efficient and your pipelines more reliable.

Your Game Plan for Implementing Declarative Pipelines

Moving to a declarative model isn't just a technical switch; it's a strategic one. A successful transition requires a thoughtful approach that considers your technology, your team, and your business goals. Here’s a practical, step-by-step plan to guide you through the process of implementing declarative data pipelines in your organization.

Start with a Clear Assessment and Plan

Before you write a single line of configuration, take a step back and assess your current environment. Where are the biggest points of friction? Are your engineers bogged down by brittle, custom scripts? Are your cloud costs spiraling due to inefficient processing? A declarative data platform can address these challenges by separating technical implementation from business logic.

Start by auditing your existing pipelines to identify a candidate for a pilot project—something that’s impactful but not mission-critical. Define the scope, outline the goals, and get a clear picture of the "before" state. This initial planning is the most important step you'll take, as it sets the foundation for everything that follows and helps you understand why a new approach is necessary.

Prepare Your Team for the Shift

Adopting a declarative model is as much about changing mindsets as it is about changing tools. Your team is likely accustomed to writing imperative code, so you need to prepare them for a new way of thinking. Effective adoption requires proper planning, team alignment, and strategic execution.

Communicate the "why" behind the change. Explain how declarative pipelines will reduce manual toil, minimize firefighting, and allow engineers to focus on higher-value work. Provide training and resources, like clear documentation, to help them get comfortable with the new tools and concepts. Fostering a supportive environment where your team can learn and experiment is key to building momentum and ensuring long-term success.

Choose Your Migration Strategy

You don’t have to overhaul your entire data infrastructure overnight. A phased migration is almost always the best approach. Start with the pilot project you identified during your assessment. This allows your team to learn, build confidence, and demonstrate value quickly without risking major disruption.

Consider whether you’ll re-architect your pilot pipeline from the ground up or take a "lift and shift" approach by wrapping existing components in a declarative framework. Many organizations migrate their pipelines to achieve greater scalability and flexibility, and a declarative model is a powerful way to get there. By starting small and iterating, you can build a repeatable process for migrating more complex systems, like a distributed data warehouse, over time.

Monitor and Optimize Performance from Day One

Declarative pipelines are designed for stability, but that doesn't mean you can set them and forget them. Implementing robust monitoring and observability from the very beginning is critical. You need to track performance, cost, and data quality to ensure your new pipelines are meeting expectations and to catch issues before they become problems.

Establish key metrics to watch, such as data quality indicators like row counts, null rates, and schema drift, which can help you detect anomalies before they impact reports. Set up dashboards and alerts to keep your team informed. This continuous feedback loop is essential for optimizing your pipelines, controlling costs, and building trust in the data your systems produce. Efficiently handling the data from this monitoring, such as through optimized log processing, is also a crucial part of the puzzle.

Define What Success Looks Like

How will you know if your move to declarative pipelines was successful? You need to define what success looks like before you begin. These metrics should tie directly back to the pain points you identified in your initial assessment. Success isn't just about deploying a new technology; it's about achieving specific business outcomes.

Measure how well your pipelines perform with metrics like throughput rate, data completeness, and downtime. Track operational improvements, such as a reduction in engineering hours spent on maintenance or a decrease in production incidents. Most importantly, measure the business impact: faster time-to-insight for your analytics teams, lower infrastructure costs, and improved compliance. These clear, measurable goals will prove the value of your investment and guide your future data processing solutions.

Related Articles

• Distributed Computing Applications: A Practical Guide | Expanso
• Automated Data Governance: A Practical Guide | Expanso
• Why 80% of Your Data Should Never Hit the Cloud
• Cloud Data Governance and Catalog: A How-To Guide | Expanso
• Cluster Computing in Cloud Computing: Your Complete Guide | Expanso

Frequently Asked Questions

Can I apply declarative principles to my existing pipelines, or is this only for new projects? You can absolutely apply this to your current setup. A complete overhaul isn't necessary or even recommended. The best way to start is by picking one or two of your most problematic imperative pipelines for a pilot project. You can begin by wrapping your existing scripts in a declarative framework to gain better orchestration and error handling. This phased approach lets your team learn and see the benefits firsthand before you commit to re-architecting more critical workflows.

Does "configuration over code" mean my engineers won't need their coding skills anymore? Not at all. In fact, their skills become even more valuable. This approach shifts their focus from writing tedious, low-level instructions to defining high-level business logic and sophisticated data models. Instead of spending their time on boilerplate code for retries and error handling, they can concentrate on the core transformations that deliver real value. Strong skills in languages like SQL and Python are still essential for defining the "what" of your data's end state.

How is this different from just using a better scheduler for our existing scripts? A scheduler simply triggers a script at a specific time or after another script finishes. It doesn't understand what's happening inside the script itself. A declarative system, on the other hand, manages the entire workflow from a holistic perspective. It understands the dependencies between tasks, optimizes the execution plan for efficiency, and automatically handles failures without manual intervention. It’s the difference between a simple timer and an intelligent project manager.

How do declarative pipelines help with complex issues like data residency and compliance? This is one of the most powerful benefits. By separating the business logic from the execution details, you can enforce governance rules at the platform level. For example, you can declare that a pipeline processing sensitive customer data must run on infrastructure within a specific country. A distributed computing platform like Expanso can then automatically route that job to a compliant server, ensuring you meet regulations like GDPR without having to build custom, brittle logic for every single pipeline.

Do we have to commit to a single, all-in-one declarative platform to make this work? No, and you probably shouldn't. The modern data stack is all about using the right tool for the right job. You can build a powerful declarative system by combining best-in-class tools that fit your team's needs. You might use dbt for your SQL transformations, an orchestrator like Dagster to manage the workflow, and a compute platform like Expanso to run the jobs efficiently across your different environments. These tools are designed to integrate, giving you the flexibility to create a stack that solves your unique challenges.