How to Architect a Modern Data Platform in 2025

If you work with data, you’ve likely felt the strain of legacy architecture. Maybe it’s a report that takes hours to generate or a pipeline failure that goes unnoticed until it’s too late. As teams take on more complex projects, connect more systems, and adopt AI-driven tools, the old ways of managing data just don’t hold up.

That’s where modern data platform architecture comes in. It’s not just about upgrading technology. It’s about designing systems that support how teams work: collaboratively, cross-functionally, and at speed.

According to Gartner, modern data architecture should be modular, scalable, and adaptable. But knowing what to build (and of course, how to build it) isn’t always clear.

In this article, we’ll walk through what defines a modern data platform in 2025. We’ll look at what to include in its architecture, where traditional systems fall short, and how to design a platform that works for your team today and grows with the needs of tomorrow.

What is data platform architecture?

At its core, a data platform is the foundation that allows your team to collect, transform, store, and access information from across the business. But architecture is what determines whether that data gets where it’s supposed to go—on time, in the right format, and with the right context.

Data platform architecture is the structured design behind how your data flows. It defines how raw inputs from different tools and systems are ingested, processed, and made available to the people who need them, whether that’s a data analyst running queries, a marketer reviewing campaign metrics, or a team lead tracking KPIs on a dashboard.

When the architecture is sound, data becomes more than rows in a spreadsheet; data becomes something you can act on. It supports how you make decisions, surfaces valuable information, and keeps pace with your team. A weak architecture, on the other hand, often leads to delays, rework, or data that arrives too late to be useful.

Ultimately, the goal of a modern data platform architecture is to make data useful and usable for the people working closest to it.

The core layers of modern data platform architecture 

A modern data platform is a coordinated system made up of layers that work together to manage the entire lifecycle of data. When these layers are clearly defined and intentionally designed, teams spend less time troubleshooting and more time working with data that’s timely, reliable, and ready for use.

Here are six essential layers that form the backbone of a modern architecture:

1. Data ingestion

This layer handles how data enters your platform, from SaaS tools and APIs to flat files, event streams, or databases. A well-designed ingestion layer supports real-time, batch, and streaming data so your team can work with what’s happening now, not just what happened last week.

2. Data storage

Once data is ingested, it needs a place to live. Cloud data lakes and warehouses offer scalable, cost-effective storage while maintaining structure and security. This layer should support raw and transformed data, historical archives, and fast retrieval.

3. Data processing

Processing is where data is cleaned, formatted, enriched, and transformed for use. Teams may use SQL, no-code tools, or ETL pipelines to transform data into a usable format. Data integration plays a key role here, ensuring that inputs from different systems work together and can be analyzed side by side.

4. Data access

This layer determines who can see what, and how. Business teams may explore data through dashboards or apps, while data analysts dig deeper via queries. Role-based permissions ensure sensitive data is protected without slowing down access for everyday use.

5. Data pipelines

Pipelines connect all of the above. They’re responsible for moving data between systems and layers automatically and reliably. When built with observability and alerting, they make it easier to identify delays or broken connections before they impact your team’s work.

6. Data security and governance

Governance keeps everything aligned. This layer manages ownership, documentation, and compliance requirements. Strong data governance practices also help your team trace where a value came from and how it was transformed, adding confidence to every analysis.

Limitations of traditional data platform architecture

Many teams still rely on data architecture that was built for a different era—one with fewer tools, smaller data volumes, and slower expectations. As the pace and complexity of data work increase, those older systems start to show their cracks.

Limited visibility

When data flows through disconnected tools, it’s hard to know what’s working, what’s broken, and where delays are happening. A failed job or stale feed can quietly throw off an entire report, and fixing it often means hours of manual troubleshooting.

Inflexible scaling

Traditional architecture wasn't designed to grow or adapt quickly. When your team wants to add a new data source, like a marketing platform or a real-time event stream, it often requires custom workarounds just to make things compatible.

Weak governance

Without clear rules for access, ownership, and auditing, it’s difficult for people to trust the data in front of them. That lack of structure creates confusion and risk, especially for teams handling sensitive or regulated information.

Workflow bottlenecks

Delays in reporting, duplicated work, and confusion about data ownership can grind progress to a halt. These aren’t just technical issues; they directly impact how quickly your team can act on what they’re seeing.

When these patterns show up consistently, it’s a sign that your architecture may be holding your team back.

Modern data platform architecture benefits

When your data platform is designed for how your team actually works, it becomes more than a system. It becomes a foundation for quick answers, smoother collaboration, and more confident decision-making. Here’s what a modern data platform makes possible:

Supports real-time and high-volume workflows

Modern platforms are built to handle high-frequency, high-volume data without breaking down or slowing teams down. That means no more waiting for batch updates to finish or wondering whether a report reflects what’s happening right now.

Brings disparate data together

With the right architecture, data from CRMs, ERPs, marketing tools, and custom apps can live in the same environment—modeled, cleaned, and ready to analyze. The result is a complete picture of what’s happening, rather than just a narrow slice.

Enables predictive and AI-powered work

When clean, connected data flows freely, AI tools become more reliable and more accessible to the people using them. Whether it’s forecasting, anomaly detection, or classification, AI-powered predictive analytics are only as good as the architecture beneath them.

Strengthens governance without slowing teams down

Governance doesn’t have to mean restriction. When done right, it’s how teams move quickly, with confidence that the data is current, compliant, and secure. Big data and AI go hand in hand here, especially when used to automate monitoring and flag potential risks.

Scales as you grow

Whether your team is adding new data sources, increasing volume, or expanding who gets access, modern platforms scale with minimal disruption, both vertically (computing and storage) and horizontally (data types, use cases, teams).

Use cases for modern data platform architecture

The value of modern data architecture shows up in day-to-day decisions and across teams, not just IT. Whether you’re analyzing customer behavior, responding to market changes, or working toward performance goals, having a system that supports fast, trustworthy insights makes the difference.

Banking and finance

Modern banking teams need more than compliance. They’re looking for fast, up-to-date insights and scalable systems that support both risk management and innovation. Risk and analytics teams depend on clean, connected data to detect fraud, meet audit requirements, and make fast, accurate decisions.

As McKinsey points out, next-gen banking requires modular, cloud-native architecture that supports interoperability and speed. With the right platform, teams can move from static reporting to dynamic, personalized services and respond to change as it happens.

E-commerce

Merchandising, operations, and customer experience teams often work from different tools. A unified platform brings data together from inventory systems, web analytics, and purchase history, so teams can adjust campaigns, forecast demand, and personalize offers without delay.

Marketing and sales

With real-time dashboards and shared data models, marketing and sales teams can align more easily, tracking campaign performance, lead quality, and deal velocity in one place. This kind of connected insight is the backbone of effective AI-powered business analytics, helping teams respond quickly and refine strategies as they go.

How to build a modern data platform

Designing a modern data platform centers on removing the friction your team faces every day. That means starting with your real-world problems and building something that scales with your needs, not the other way around. Here’s how to approach it.

1. Audit what you have

Before designing anything new, map what already exists. Which tools are involved in data collection, transformation, and reporting? Where are things breaking down? Ask your team: What’s working? What’s always a struggle? Look for delays, duplicated effort, or workarounds that result from the system failing to meet what people actually need.

This step focuse on visibility. Without understanding the current state, you can’t build something that meaningfully improves it.

2. Set clear goals

Next, define what success looks like. Is your team trying to reduce report turnaround time? Give non-technical stakeholders access to real-time data? Improve forecasting? Setting clear, measurable goals keeps the architecture grounded in real-world outcomes rather than abstract features.

It also helps prioritize. You don’t have to solve everything at once. Start with the capabilities that will have the biggest impact on how your team works today.

3. Design for scale

Growth isn’t just about headcount. It’s about more data, more tools, and more questions. A platform that works for one team today should be able to serve multiple departments tomorrow. 

Choose tools that scale for storage and computing capacity, as well as for increased data types and use cases. without re-engineering everything. Look for architecture that’s cloud-native and elastic. These features make it easier to scale on demand and respond to spikes in usage without hitting performance walls.

4. Build for resilience and monitoring

A modern platform should make it easy to spot problems early and recover quickly when they happen. Include logging, alerting, and pipeline monitoring from the start. Real-time status dashboards, job-level error tracking, and automated alerts when pipelines fail or stall can save hours of troubleshooting.

Teams should never be surprised by a broken report. Visibility and observability should be built in from the start, not bolted on later.

5. Automate repetitive work

If your team is spending time on manual data prep, repetitive report creation, or constantly updating dashboards, it’s a clear sign your platform needs more automation. With the right tools, you can automate data ingestion, transformation, and delivery, freeing up time for deeper analysis and decision-making.

Augmented analytics can also support your team with built-in suggestions, anomaly detection, and forecasting, without requiring advanced modeling skills.

6. Choose tools that can grow with you

Select platforms designed for change. Look for analytics as a service, no-code/low-code options, powerful APIs, and app development environments that let you adapt and build as you evolve. This flexibility ensures you don’t outgrow your architecture before you even finish implementing it.

7. Monitor and improve continuously

Your platform isn’t “done” once it’s launched. Set regular checkpoints to evaluate performance: How fast are dashboards loading? Are pipelines running reliably? Are people using the tools the way you intended?

Make adjustments often. The best data platforms are the ones that keep evolving alongside your team.

Architecture for what’s next

A modern data platform isn’t just infrastructure. It’s the reflection of how your team collaborates. And how they make decisions. 

When the architecture is flexible, connected, and transparent, people spend less time chasing down numbers and more time putting them to use.

If your current system slows you down, keeps data locked in silos, or makes troubleshooting feel like guesswork, it may be time to rethink what your platform is doing for you—and what it could be doing instead.

Rebuilding your data architecture doesn’t mean starting from scratch. It means creating the foundation that can adapt to new questions, new tools, and new team members without constant rework.

Domo’s platform is designed with this adaptability in mind. With built-in governance, automation, AI tools, and enterprise-scale business intelligence, it’s how teams connect data, act on it, and adjust quickly when priorities shift.

As your data grows, your platform should grow with it—not become something your team has to work around. Build for the future by designing for the people who use data every day. Get in touch with Domo to see how we can help you make it happen.