X vs Y - which BI tool handles large datasets better?

I'm a backend engineer at a mid-sized e-commerce company, handling our data infrastructure where we process around 500 million sales events monthly. We run both X and Y in production for different teams, so I've had to benchmark them directly.

1. **Data Ingestion and Model Build Times**: For a dataset of 50 million rows, X's in-memory engine can build an analytical model in 8-12 minutes using columnar compression. Y, using a traditional row-based approach in my tests, took 35-50 minutes for the same initial build. The gap narrows on incremental updates, but the first load is a significant hurdle.

2. **Concurrent User Query Performance**: Under load, X maintains sub-5-second response times for about 12-15 concurrent marketing users running typical aggregate queries. Y began queueing queries and saw times spike to 20+ seconds with just 8 concurrent users. This is the difference between your marketing person exploring freely and waiting frustratedly.

3. **Total Cost of Ownership for Scaling**: X's pricing is based on compute credits, which at our scale costs roughly $4,000/month for reliable performance. Y's per-user model starts cheaper ($25/user/month) but the required add-ons for performance (dedicated query nodes, premium connectors) pushed our projected cost to over $7,000/month for a similar user count, making it more expensive at scale.

4. **Operational Overhead for Non-Experts**: Y wins on initial setup; you can connect a PostgreSQL DB and build a dashboard in an afternoon. X requires a more deliberate data modeling phase (defining relationships, hierarchies, aggregations) which takes 2-3 days of learning. However, that upfront work is why X performs better later. Y's "easy" model leads to confusing, slow reports as data grows because it lacks those enforced optimizations.

Given your Shopify use case and focus on large, growing datasets, I'd recommend X. It demands more initial data modeling effort but will reliably serve your marketing person without degrading. The choice depends entirely on your internal skill set; if you have 2-3 days to learn star schema design, pick X. If you need a simple report tomorrow and can accept performance limits later, pick Y.

Wow, this is incredibly helpful, thank you. Seeing actual numbers like 8-12 minutes vs 35-50 for the model build really puts it in perspective. That initial wait would be a huge blocker for us.

The concurrent user point is exactly what I was worried about without knowing how to phrase it. If my marketing person has to wait 20 seconds for every filter they change, they'll just stop using it.

Can I ask a follow-up on the cost? You mentioned Y's per-user pricing and add-ons. For a small team, maybe 5 users, does that pricing advantage hold up, or do the necessary add-ons to handle the data size basically erase the savings?

Hey, welcome! I've been in your exact spot, trying to move from spreadsheets to something that won't buckle under a few million rows.

For your Shopify use case, I'd actually suggest looking at your data warehouse first before picking X or Y. Both tools will struggle if they're trying to crunch raw data directly from Shopify/ads platforms. The real "gotcha" with big datasets isn't usually the BI tool itself, but how you prepare the data for it.

I'd recommend setting up a simple pipeline into something like BigQuery or Snowflake (they have very generous free tiers for your size), then connect your BI tool to that. That way, the heavy lifting happens in the warehouse, and both X and Y will feel fast for your marketing person to explore. It adds a setup step, but saves so many headaches later.

From there, Y tends to be a bit more forgiving for beginners on the modeling side, in my experience. X is a powerhouse, but its initial learning curve for building reports is steeper.

Totally get the feeling of being out of your depth - been there! Everyone's throwing around terms like "data modeling," but for your Shopify case, it basically means how you organize your raw data (like orders, customers, ad spend) into logical tables so the BI tool can understand it quickly.

For setup and not being a full-time data person, honestly, both X and Y will make you want to pull your hair out if you try to connect them directly to your platforms for millions of rows. The real gotcha isn't the tool, it's the setup before it. You'll spend more time waiting for refreshes and fixing broken connections than building reports.

I actually agree with user319's point below. Your first step shouldn't be X vs Y, it's getting a simple data warehouse layer in place, like BigQuery. Then, either BI tool will feel fast for your marketing person. It's an extra step, but saves you from the "why is this taking forever?" phase later.

Okay, this makes a lot of sense. So you're saying the bottleneck isn't really the BI tool at all, it's asking it to do the heavy lifting from raw sources.

The "waiting for refreshes" part is what I'm afraid of. If it takes an hour to update my daily numbers, the tool is useless.

I have a dumb question about BigQuery: does using it mean I need to write a lot of SQL to get my data in there? Or are there Shopify connectors that handle that automatically?

Your core question about "query performance" and "data modeling" for day-to-day use is exactly right. Query performance is how long you wait after clicking "run" on a report. Data modeling is the upfront work to structure your raw sales and ad data into clean, related tables; a poor model forces the tool to scan millions of rows for a simple question, which is why reports crash or take forever.

Given your stated need for reports that don't crash and enabling a marketing user, the architectural choice precedes the X vs Y debate. Both tools will perform poorly connected directly to Shopify's API for millions of rows; the refresh will be slow and unstable. The critical first step is a dedicated data warehouse layer, as others noted. This moves the "heavy lifting" out of the BI tool. With a warehouse like BigQuery handling the computation, both X and Y become primarily visualization layers, and their performance differences become less significant for a team of your size.

The gotcha at scale isn't primarily the BI tool's engine, but whether your data model is built for analytical queries. Even in a warehouse, if you simply dump all events into one massive table, performance will suffer. You'll need to create aggregated summary tables (e.g., daily sales by product) for frequent reports. Some tools, like X, have stronger built-in semantic layer capabilities to help manage this, while Y often relies more on the warehouse's pre-aggregations. Your setup effort shifts from BI tool configuration to basic data pipeline and modeling.

The real answer is you shouldn't be comparing them yet. Your bottleneck is the spreadsheet, not the BI tool's engine.

"Query performance" means how long your marketing person waits. "Data modeling" is the work you do once to stop scanning millions of rows for a simple question. Neither X nor Y fixes a bad source.

Connect either directly to Shopify with millions of rows and you'll spend your life waiting for refreshes. The setup you choose matters more than the tool.

Get your data into BigQuery first. Use a Shopify connector like Stitch or Fivetran. Then connect X or Y to that. The warehouse does the heavy lifting. Both tools will feel fast. The gotcha is skipping this step.

Yes, exactly. The "fixing broken connections" part is so real - I tried connecting a BI tool to Google Ads directly once and it broke every other day 😅. That was more frustrating than the slow speeds.

So for someone just starting, is it better to use a managed connector like Fivetran or just schedule a CSV export from Shopify to BigQuery manually? I worry about the cost of adding another tool.

Managed connectors break in predictable ways, which is better than random CSV failures. Fivetran's cost is clear, but the manual export has a hidden tax: the hour you spend each week debugging schema drift or failed jobs. For five users, that's a poor trade.

If cost is the absolute blocker, use the manual CSV method but commit to a weekly integrity check. Most teams outgrow it within six months and then pay the connector tax anyway.

Exactly. That hidden tax is real, but I'd quantify it differently. A manual pipeline's failure mode is often silent - you get stale data, not a broken dashboard. Debugging that eats more than an hour.

We used CSV loads for a client and spent three weeks before a major report because a date column format changed in Shopify's export. The connector cost isn't just about time, it's about catching schema drift before it corrupts your history.

If cost is the blocker, start with the manual method but instrument it. Alert on row count deltas and load latency. Treat it like any other pipeline, because it is one.

Welcome! That feeling of being out of your depth is totally normal when you're moving from spreadsheets into this world.

To put it simply for your day-to-day, "query performance" is just how long you stare at a spinning wheel after you click a button. "Data modeling" is the boring but crucial upfront work of telling the tool how your sales, customers, and ads link together, so it doesn't have to sift through every single row every time.

Everyone here is steering you right about looking at your data setup first. The biggest gotcha with millions of rows isn't really X or Y, it's trying to make either of them do the heavy lifting directly from Shopify. That's where you'll get the crashes and the hour-long refreshes. Getting your data into a separate warehouse first, then connecting your BI tool to that, is the move that makes either option feel fast and stable for you and your marketing person.

You've received some good foundational advice about warehouses. But to answer your specific question, the debate is a bit of a red herring.

You ask about which handles large datasets better. On a proper warehouse, they'll both be fine, and the difference in raw speed won't matter for your use case. The "gotcha" isn't the tool's engine, it's the vendor's marketing. Both will sell you on benchmarks you'll never replicate, because your real bottleneck will be the marketing person learning to build a coherent report without creating a query that scans every row.

Look at which tool your marketing person can't break as easily. That's the one that "handles" your data better.

Your "dumb question" is the right one. The answer is no, you don't write SQL to get data in. Tools like Fivetran or Stitch do that. But that's just the first trap.

The bigger mistake is thinking a connector solves your problem. It just automates loading raw, poorly-structured API data into a warehouse. Then you absolutely do need to write SQL, or pay someone who can, to model it into something a BI tool can use efficiently. Without that modeling step, you'll just be waiting for refreshes from BigQuery instead of Shopify. The connector is the easy part. The modeling is the real work everyone tries to skip.

Welcome! It's a great question, and that feeling of being out of your depth is totally normal. You've hit on the exact pain point most of us run into.

Honestly, when you're talking about millions of rows, the difference between X and Y's raw speed on a proper setup is likely marginal for your use. The real "gotcha" you should look for isn't which one is faster in a benchmark, but which one helps you *avoid* slow queries in the first place. Some tools are better at guiding non-technical users (like your marketing person) toward building efficient reports without accidentally creating something that scans every single row. That's often more about the UI and guardrails than the underlying engine.

On the setup front, Y has historically been a bit more opinionated about how you structure your data, which can be a blessing and a curse. It forces a bit of modeling upfront, which can feel like extra work but pays off in stability later. X tends to be more flexible, which is great until you paint yourself into a performance corner. Since you're not a full-time data person, that initial learning curve might be your biggest deciding factor. Have you tried the free trials for both? The day-to-day feel of building a simple report will tell you a lot.