The modern data stack is the group of tools a business uses to move data out of its source systems, store it, structure it, and put it in front of the people who need to make a decision from it. The term describes an approach as much as a set of products: specialised, often cloud-based components that each perform a specific role and can be scaled independently as needs change. 

A traditional stack built around a single SQL database handled structured data well, but reaching into machine learning or AI meant standing up separate technology to process anything outside that structure. The modern data stack removes that split. Structured, semi-structured and unstructured data sit on the same platform, so a team can move from reporting into data science and AI work without changing the underlying technology stack. 

For companies evaluating a first modern data stack, or replacing an existing one, the first step is to define which of the five layers below is already covered. 

Every modern data stack, regardless of the platform underneath it, breaks down into the same five layers. 

• Ingestion. Pulls data out of source systems and applications into central storage. 
• Storage. Holds raw and processed data, usually a data lake or lakehouse rather than a single database. 
• Transformation. Cleans, joins and structures raw data into tables a report can use. 
• Orchestration. Schedules pipelines and manages the order they need to run in. 
• Consumption. Puts the finished data in front of the people making decisions, usually through dashboards. 

What goes in each layer depends on the platform a business uses, but the categories stay the same. The table below sets out a typical example for each layer, along with Eunoia’s usual recommendation depending on the client’s existing ecosystem. 

Ingestion tools such as Fivetran or Fabric pipelines exist to solve one problem: extracting data from a source system reliably, without manual file copying. 

Storage sits underneath everything else. The Delta Lake format, whether accessed through Databricks or through OneLake in Microsoft Fabric, is now the common solution for most mid-market builds because it supports both structured tables and raw files in the same place. 

Transformation turns raw data to meaningful information, yet it is often underestimated during planning.  

Orchestration and consumption complete the stack, scheduling the pipelines with tools such as Apache Airflow and, ultimately, putting a number in front of someone in Power BI. 

The clearest way to see what changed is to compare a modern data stack against the infrastructure it replaced. 

On a virtual machine or an infrastructure-as-a-service setup, a resource is either on or off. A business might run a small, continuously‑active application alongside a process that runs only once a day. Because both sit on the same machine, neither can be switched off or scaled independently.  

The industry’s next step was managed databases, which solved some of that problem but tied compute and storage together in a different way: a high-performance, highly available production database scales its storage whether or not that storage is actually needed, and vice versa. 

A modern data stack decouples these components. Each layer, ingestion, storage, transformation, orchestration, consumption, is a separate, configurable resource, and a business pays close to nothing for storage sitting idle.  

From our team’s experience, the cost shift at mid‑market scale comes from two changes: 

Fewer always‑on machines.   

Components billed only while they are doing work. 

The pricing model of the modern data stack makes a real difference compared to a traditional one. On a virtual machine or a single managed database, compute and storage are combined. If a business needs more processing power for one workload, it often has to provision more storage too, whether it needs it or not, and pay for a machine that runs continuously even when only a small part of it is doing anything.In a modern data stack, storage costs a tiny fraction when idle, and compute is paid for only while it runs.  Across the manufacturing and iGaming projects we have worked on, we have seen that this flexibility can have a greater impact on cost at mid-market scale than the choice of platform itself. 

The trade-off is that a modern data stack has more components to understand than a single database server did, which is part of why the next section matters more than most vendor comparisons suggest.

Eunoia holds the Analytics on Microsoft Azure Specialisation, Microsoft’s highest-level credential for data and analytics partners, and is highly knowledgeable and experienced in both Microsoft Fabric and Databricks. 

In practice, the choice between the two comes down to fewer variables than most comparisons suggest. Technical specifications matters less than people expect. What decides it is a team’s existing skills, its business goals, and  budget. 

Microsoft Fabric – When a business already uses Microsoft 365 or Dynamics, Fabric is the natural next step. It gives compute and pricing options that are straightforward to explain to a non-technical stakeholder, which lowers the barrier to getting a first stack running. Fabric also suits businesses that want to stay fully on the Microsoft stack, keeping data, reporting and governance under one vendor rather than integrating tools across providers. The trade-off is less granular control over individual components. 

Databricks – Ideal for teams managing heavier workloads or building data science models, where detailed control of compute, storage, and cost improves performance. How to control a platform like that is hard to explain to a casual business user, and Databricks’ dashboarding, while capable, can be maintained only by engineers. Regardless of which platform sits underneath, data can land in whichever dashboarding tool the client prefers, Databricks dashboards, Power BI, or another BI layer entirely. 

Specification aside, the mistake Eunoia sees most often is misconfiguration. Storage accounts left public-facing with no IP whitelisting, databases reachable from anywhere, and communication between components passing over the Azure backbone rather than tunnelled and routed through a hub-and-spoke network design. Encryption in transit is usually there by default. The least-privilege principle, and network design that respects it, usually is not, because most teams configure a stack through a setup wizard rather than against a documented standard. 

Fabric’s shared-capacity model adds a related risk worth planning for early. Power BI, pipelines, notebooks and lakehouse operations draw from the same capacity pool, so a solution that performs well in testing can throttle once refreshes and user activity overlap in production. Sizing that capacity against measured concurrency, not assumptions, is worth doing before go-live rather than after. 

For a business starting from scratch, the minimum viable stack is less about which tools to buy and more about how much is understood before building. Teams often default to the simplest possible setup, planning to refactor later. Changing the architecture after a business has built on it is usually harder than it looks at the outset, which is why being knowledgeable on the available components, and their trade-offs, matters most before the first tool is chosen. Where that knowledge is not there yet, working with a data engineering services partner who has made these decisions before is usually faster than learning by trial and error. 

For a wider view of how these platforms compare against traditional data warehouses and data lakes, see Eunoia’s modern data infrastructure guide. 

A modern data stack consists of five layers that each perform a distinct job and are priced by use rather than bundled into a single machine that runs regardless of demand.  Whether the underlying platform is Microsoft Fabric or Databricks matters less than most vendor comparisons suggest. Success depends on a team’s understanding of the components, adherence to documented security standards, and alignment of each layer with its skills and budget. Getting this right at the start is far easier than rebuilding later.