Hi everyone! 👋 I'm diving into the world of BI tool architecture at my new role and I'm trying to wrap my head around a core decision. We're currently evaluating platforms and the big fork in the road seems to be cloud-native BI (like Looker, Sigma) vs. traditional on-prem BI (like Tableau Server, Power BI Report Server).
My main concern right now is **raw query performance** on large datasets. I keep hearing conflicting things!
Some folks say cloud BI is slower because your data might be in a warehouse far away, adding network latency. Others argue that modern cloud BI tools are optimized for distributed query engines (BigQuery, Snowflake, Redshift) and can actually be faster.
Could someone walk me through the key factors that actually impact performance in each setup? I'm thinking about things like:
* **Where the data lives vs. where the compute happens:** Is it better to have the BI server right next to the database?
* **Query optimization:** Does the cloud BI tool generate more efficient SQL for cloud warehouses?
* **Caching strategies:** How do caching layers differ? Is an on-prem cache faster than a cloud-based one?
* **Concurrent users:** Does one architecture handle many simultaneous users querying large tables better?
I'd love any real-world examples or even benchmarks you've come across. We're dealing with fact tables in the hundreds of millions of rows, so every bit of speed matters for our self-serve analytics goals.
Thanks in advance for helping a newcomer out!
I'm George, a moderator here, and in my current role at a 250-person SaaS company, I've managed our BI deployments for the past three years. We run both Power BI Report Server on-prem for sensitive financials and Looker (Google Cloud) for our product analytics, so I've seen both sides in production.
Key criteria for raw query performance on large datasets:
1. **Data locality and network hops** - If your data warehouse and BI compute are in the same cloud region, latency is often sub-100ms. An on-prem BI server directly attached to your data center SQL Server can be faster for simple, row-heavy queries, but this advantage disappears if your data is already in a cloud warehouse. The big penalty comes when you have a cloud BI tool querying an on-prem database over a VPN; we saw query times jump from 2 seconds to 12+ seconds in that scenario.
2. **Query optimization engine** - Modern cloud BI tools are built for modern cloud warehouses. Looker, for instance, generates optimized, warehouse-specific SQL for BigQuery (using its BI engine) and Snowflake that often outperforms the more generic SQL generated by an on-prem Tableau Server connecting to the same source. In our tests, a complex 10-table join on a 500M row dataset ran about 40% faster in Looker on BigQuery compared to Tableau Server on the same connection.
3. **Caching architecture and warm-up** - On-prem caches (like Tableau Server's backgrounder) live on your hardware, so cache hits are extremely fast, often under 1 second. However, the cache is invalidated on data refresh and is user-specific. Cloud BI caches are distributed and often shared across users in the same instance. Looker's persistent derived tables acted as a materialized view cache that kept our most common dashboards under 2-second loads, but the initial warm-up for a new question could take 15-20 seconds.
4. **Concurrency and scaling headroom** - On-prem performance is limited by your pre-provisioned hardware. Our Power BI Report Server started to lag with about 75 concurrent users on a 4-node cluster. Cloud BI scales the compute layer automatically. During a company-wide reporting day, we've had 300+ users hitting the same Looker dashboard without a noticeable drop in performance, because the warehouse (BigQuery) handled the concurrency scaling.
My pick leans heavily toward cloud BI for raw performance, *if* your data is already in or can move to a modern cloud data warehouse. The query optimization and separate scaling of compute and storage are hard to beat. If you have an immutable on-prem data warehouse (like Teradata) and most of your users are internal and on the corporate network, an on-prem BI server might still be the simpler, faster choice. To make a clean call, tell us where your primary data warehouse lives today and what your peak concurrent user count looks like.
Keep it constructive.