Leaving Legacy Behind: Why Australia Is Moving Fast to Databricks and Snowflake

Most legacy data systems don't fail loudly. They just quietly get more expensive.

The old setup still runs — the on-premise warehouse, the overnight batch jobs, the sprawl of SQL Server and hand-stitched pipelines. The reports still come out. But every year the licences cost a bit more, scaling means buying more hardware, unstructured data has nowhere to go, and anything resembling AI is simply off the table. And the numbers always land a day or a week late, so the business is forever driving by the rear-view mirror. That's the real cost of legacy: not one big bill, but a slow leak of money, speed and opportunity that most teams stop noticing because they've lived with it so long.

Australian businesses are done living with it. Walk into almost any mid-sized company here right now and someone is either mid-migration, planning one, or nursing the scars of one that went sideways. The shift to modern platforms has gone from "something the big banks are doing" to something a distribution business in Dandenong or an allied-health group in Perth is doing this quarter.

The two engines — and the tool that quietly does the work

If you're modernising, you'll meet the two names in the room fast. In plain English, here's how I explain them to clients.

Snowflake is the turnkey one. It's a clean, reliable cloud warehouse that's easy to run and predictable to budget for, and you can get dashboards and reporting flowing without a heavy engineering lift. If what you need is governed, fast SQL reporting that just works, Snowflake fits the "I don't want to babysit infrastructure" mindset.

Databricks is the build-your-own lakehouse. It puts data engineering, analytics and machine learning on one open platform, keeps your data in open formats you actually own, and pulls ahead the moment AI, real-time processing or serious data science become the point rather than an afterthought.

Honestly, the gap between them is narrowing every release — Snowflake keeps pushing into Python and ML, Databricks keeps sharpening its SQL and BI. So I don't have a religious answer. The right call depends on your workloads, your team's skills and where you're trying to get to. Anyone who tells you one platform always wins is selling something.

And then there's dbt — the piece people forget to mention. It's the transformation layer that turns raw, messy ingested data into clean, tested, business-ready tables, using version-controlled SQL. It runs on both Snowflake and Databricks, and it's the thing that brings actual engineering discipline — testing, documentation, reuse — to the muddy middle of the pipeline where most reporting bugs are born. It's not glamorous. It's usually the workhorse everything downstream quietly depends on.

The honest part: migration is not lift-and-shift

Here's where good intentions die. The most expensive mistake I see is treating a migration like a straight copy — point the old system at the new one, press go, declare victory. All that gets you is yesterday's mess running in a more expensive postcode.

Two things I wish every team knew before they start. First, most legacy estates are carrying a lot of dead weight. When we actually audit what's running, a big chunk of it turns out to be pipelines nobody reads, dashboards nobody's opened in a year, and jobs that were "decommissioned" in conversation but never in code. Migrating that is paying to move furniture you were about to throw out. Second, timelines are all over the map — a focused data mart can move in a couple of weeks, a full enterprise warehouse can take the better part of a year. The projects that land on time are the ones that start by working out what's actually used and worth keeping, not the ones that try to heroically move everything.

Done properly, a migration isn't a transplant. It's the one chance you get to redesign how data flows — cut the dead weight, consolidate the tangle, and build governance in from the first table instead of bolting it on at the end.

What you actually get on the other side

This is the part that makes the pain worth it. When a business gets off legacy and onto a platform that's been designed rather than just assembled, the change shows up everywhere:

• One version of the truth. No more three spreadsheets arguing about last month's revenue. Dashboards, finance and any AI models all read from the same governed layer, so the numbers reconcile because they were built to.
• Speed where it matters. The gap between when data exists and when someone can use it collapses. Reporting moves from "wait for the overnight run" to near real time, and people start making calls on what's happening now instead of last quarter.
• Cost you can actually take out. Retiring dead workloads and collapsing a stack of overlapping tools genuinely lowers the bill — how much depends entirely on how bloated things were to begin with, but there's almost always fat to trim.
• AI stops being a someday. Clean, unified, historical data is the fuel modern AI needs. Once it's there, you can move from reporting on the past to getting ahead of it — forecasting demand, flagging churn, catching risk early.
• Your team stops being the reconciliation engine. When the logic lives in the platform instead of in someone's head and a Friday-afternoon spreadsheet, that recurring tax on every reporting cycle just goes away.

I've watched a finance team go from a multi-day month-end scramble to having the numbers ready before their first coffee. That's the shape of the win — the exact figures vary, but the direction rarely does.

Where to start

Modernisation rewards the people who design for where they're going, and punishes the ones who rush in without a map. The first step is almost never "pick a platform." It's a short, honest look at what you're running now, what's actually used, where the quick wins are, and which engine genuinely suits your workloads and your team.

If your legacy setup is quietly taxing you — in licence fees, in slow reports, in AI you can't touch yet — that's usually the moment worth a conversation. We're happy to help you scope what leaving it behind would actually look like.