Modern Data Stack for SMEs: What You Need, and What You Can Skip

A modern data stack is a short chain of tools that copies data out of the systems where it is created, stores it in one place, tidies it and shows it in dashboards. For most small and mid-sized businesses the right version is small: a scheduled script, a PostgreSQL database and one reporting tool. The name suggests a shopping list. It is better read as five layers, each of which you can keep as simple as your questions allow.

This guide is for a UK owner or operations lead whose reporting lives in spreadsheets pulled from Xero, Sage, Salesforce, HubSpot, Shopify or NetSuite. It walks through each layer, gives the smallest version that works and says when a larger tool earns its cost. We work with these tools through our data engineering and analytics service, so where doing less is the right answer, we say so.

Key takeaways

• A modern data stack has five layers: sources, a scheduled load, a warehouse, transformations and dashboards. You can run all five on a few modest tools.
• PostgreSQL is enough for most SMEs. Snowflake and BigQuery bill for use and suit large volumes and heavy queries, so they are a later step.
• If your team already has Microsoft licences, Power BI is usually the quickest route to reports. Metabase and Google’s Data Studio (formerly Looker Studio) suit teams without them.
• Fix data quality first. Duplicates and inconsistent records spoil reports and make any later AI project harder.
• With us, a data audit starts from £3,000, a warehouse and reporting build from £5,000 and a full data platform from £10,000.

What is a modern data stack?

It is the set of layers between your business systems and a report. Your accounting package, CRM and shop each hold a slice of the truth. The stack copies those slices into one database on a schedule, cleans and joins them, and puts the result in front of people.

The five layers are these:

1. Sources. The systems that hold the data, each with an interface (an API) that software can read from.
2. Load. A scheduled job that copies the data out.
3. Warehouse. One database that holds the copies.
4. Transformations. The rules that turn raw records into figures such as revenue by customer.
5. Dashboards. The screens people open.

Each layer is a separate choice. That is the useful part of the idea: a better dashboard tool does not mean rebuilding the warehouse underneath it.

When does an SME need one?

You need one when the same question gets different answers, and not before. The practical signs are someone spending a day each month copying figures between systems, two people producing different totals, or a report that does not exist because the data sits in three places.

If a single system answers your questions, report from that system and stop. Most accounting packages and CRMs have decent built-in reports. A stack becomes worth building when you must join data across systems, such as sales from the CRM against invoices in Xero, or orders from Shopify against stock in an ERP.

If the real problem is a spreadsheet that has become a business system, a different fix may fit better, and our guide on moving from Excel to a web app covers that.

Layer 1: Sources and their APIs

Almost every business system lets software read its data, and that is what makes the rest possible. Xero, Sage, Salesforce, HubSpot, Shopify and NetSuite all publish interfaces that a pipeline can read from on a schedule. We say “can”, because access varies by plan and edition, and some systems limit how fast you can read.

Two checks before anything else. First, list the systems that hold data you report on, and who owns each. Second, find out whether your plan includes API access. Our API integration guide covers plan-gated access and rate limits, and the Xero integration guide gives the figures for Xero.

Where a system has no interface at all, the data can often be reached another way, such as a direct database connection or a scheduled export. That costs more than a clean API, so it is worth knowing early.

Layer 2: Getting data out on a schedule

The smallest version is a Python script that runs every night, reads the new records and writes them to your database. It is cheap to run and easy to read.

The alternative is a managed connector service such as Fivetran, which you pay for rather than build. A connector can save build time when your sources are popular products. A script wins when a source is unusual, when you need only a few tables, or when you want to avoid another subscription. We work with both, and which fits depends on your sources, so we say so in the audit.

Whichever you pick, two habits matter more than the tool. Load only what changed since the last run, rather than everything every time. And make every run safe to repeat, so a failed night does not leave duplicate rows. Both are ordinary engineering, and our ERP integration guide explains the same idea for live systems.

Layer 3: The warehouse

A warehouse is a database that holds copies of your data, organised so it can be queried in one place. It does not replace your accounting or CRM software. It sits alongside them.

Start with PostgreSQL. It carries no licence cost and handles far more than most SMEs will ever store. It is also the database our own platforms are built on. Most of the case studies on our site list Python, Django and PostgreSQL in their stack, for example the market prediction platform.

Move to Snowflake or BigQuery when volume and query load justify it. Both charge for what you use rather than for a server running all day. Snowflake’s documentation says it bills compute in credits with per-second billing and a 60-second minimum each time a warehouse starts, and storage at a flat rate per terabyte (Snowflake). Google’s BigQuery documentation says its on-demand model bills queries by the number of bytes read, and that a capacity model charges for reserved slots (Google Cloud). Usage-based billing is good when load is uneven and costly when a careless query scans a huge table, so someone needs to watch it.

We also work with Amazon Redshift, SQL Server and Databricks. SQL Server makes sense where your business already runs on it. For a business of ten or twenty people, none of these is the usual starting point.

Layer 4: Transformations and scheduling

Raw copies are not reports. Someone must decide what “revenue” means, how to match a customer in the CRM to the same customer in Xero, and what to do with a record that is missing a date. Those rules are the transformation layer.

dbt is the common tool for this. Its documentation describes writing a SQL select statement per model, with dbt handling the surrounding work, plus data quality tests and generated documentation (dbt). Its open-source distribution is licensed under Apache 2.0. The value for a business is that rules live in readable, tested files rather than in someone’s spreadsheet formulas.

Airflow is the common tool for running steps in order, on a schedule, with a person told when one fails. Its documentation describes it as an open-source platform for developing, scheduling and monitoring workflows, defined in Python (Apache Airflow).

For the smallest stack you need neither. A scheduled script and a few SQL views will do, as long as a failure sends someone an alert. Add dbt when the SQL grows past what one person can hold in their head. Add Airflow when you have many dependent steps. Alerts on failure matter at every size, because a silent failure means people act on yesterday’s numbers without knowing.

Layer 5: Dashboards, and Power BI vs Metabase

Pick the dashboard tool your team will actually open. Power BI is the quickest route where Microsoft licences already exist. Microsoft’s UK page lists Power BI Pro at £10.80 per user per month, paid yearly, and says the price does not include VAT (Microsoft). That is the list price at the time of writing (November 2026), and the final price shows at checkout.

Where there is no Microsoft licence to build on, there are two common choices:

• Metabase has an open-source edition, and its pricing page lists it with free unlimited users (Metabase). Paid plans add a monthly base fee and per-user fees. Self-hosting means you run the server.
• Google’s Data Studio, which Google rebranded from Looker Studio on 16 April 2026 (Google Cloud release notes). Google describes Data Studio Pro as the paid version (Google Cloud), so the standard tool is the free one. It is worth a look if your team already works in Google’s tools.

Tool	Best fit	Licence note
Power BI	Microsoft 365 teams	Pro £10.80 per user a month, yearly
Metabase	Self-service questions	Free open-source edition
Data Studio	Google Workspace teams	Free tool, paid Pro tier

Whichever you choose, keep the number of dashboards small. Ten reports that people trust beat sixty that nobody maintains.

Where should it all run?

On whichever cloud you already buy from. We host on AWS or Azure, depending on how you already buy software, and on Supabase where a smaller estate does not warrant the weight of either. Choose the hosting region at the start so data stays in the UK or the EU if your policies or customers require it.

For us, the platform runs in your own cloud account, so the keys stay with you, and all code and IP transfers to you on payment. Running costs are separate from the build and estimated before it starts: cloud hosting for a small warehouse starts at around £120 a month.

What do three sizes of stack look like?

Here are three sizes, from the smallest that works to a full platform. Treat them as starting points, not a prescription.

Size	Layers	Typical fit
Small	Script, PostgreSQL, Power BI or Metabase	5 to 30 staff, 2 to 4 sources
Medium	Adds dbt tests and Airflow alerts	Many sources, several report owners
Large	Adds Snowflake or BigQuery, managed connectors	Heavy volume, many teams querying

Most SMEs belong in the first row for a good while. The jump to the third row is driven by data volume and query load, which you can measure, rather than by company size.

Data quality comes before AI

If you plan to use forecasting or AI, the warehouse comes first. Models learn from consistent historical records, and the usual reason AI projects disappoint is that the underlying data was never organised for them. Duplicate customers, free-text fields and missing dates are normal, and they are dealt with in the transformation layer rather than treated as a reason to wait.

You rarely need a full platform for this. A narrower groundwork is often enough, and our AI readiness checklist shows how to judge it. When the groundwork is done, machine learning work on tables of numbers can use the same PostgreSQL database for training data and predictions. Our post on computer vision examples shows what comes after for image-based work. If you are weighing AI more broadly, our AI consulting service starts with an assessment of the data platforms you already run.

What does it cost with us?

Our pricing for data engineering and analytics has three steps:

• Data audit, from £3,000. Every system holding your data reviewed, data quality assessed, a recommended architecture in plain English and a costed plan. It takes one to two weeks.
• Warehouse and reporting, from £5,000. A defined set of source systems connected, a warehouse your reports can query, dashboards and automated refresh with alerts on failure. A piece of work like this typically runs six to sixteen weeks.
• Full data platform, from £10,000. Every source connected, data quality checks and governance rules, and ongoing support after launch.

Builds carry our satisfaction guarantee: cancel within the first 21 days for a full refund, and otherwise pay by milestone as each stage works. Ask for a costed plan in writing on any quote you are weighing, ours included.

A sensible next step

Write down the three questions you most want answered without a spreadsheet, and the systems each answer comes from. That list tells you which sources to connect first and whether a small stack will do. If you cannot get the same answer from two people today, start there.

If you want an outside view, a data audit from £3,000 reviews each system, assesses data quality and ends with a costed plan, before any build is agreed.