In January 2026, most teams face the same squeeze. Data keeps spreading across clouds, apps, and old systems, users want answers faster, and the cloud bill never stays put.
That’s why the Data Lakehouse idea keeps coming up in board meetings and architecture reviews. In plain terms, it’s lake-style storage (cheap, scalable files in object storage) with warehouse-like query speed and governance so teams can use it day to day.
This guide compares Dremio, Starburst, and Snowflake across deployment, how they work with data, performance and concurrency, and cost. It also covers the option many mature teams choose, mixing platforms on purpose instead of forcing a single winner.
Start with your “must-haves”, cloud only, hybrid, or on-prem
If you want to narrow the field fast, start with deployment and how much operations work you’re willing to own. The same tool can feel “simple” or “painful” depending on your team’s skills and your compliance boundaries.
Some orgs can run everything in a single public cloud account with few constraints. Others have to keep parts of the data behind a firewall, in a specific region, or split across multiple clouds after an acquisition. Those constraints tend to decide the shortlist before you ever run a benchmark.
Just as important, be honest about operations. Do you want a platform you tune and scale yourself, or a service where you focus on data models and user access while the vendor runs the engine? Both are valid, but they lead to different choices.
If you need on-prem or hybrid, Snowflake is usually not an option
Snowflake is a managed SaaS platform, it runs in public cloud regions and doesn’t support a self-hosted on-prem deployment. If your “must-have” includes on-prem compute, strict data residency behind your firewall, or a hybrid setup where queries must run inside a private network, Snowflake usually drops off the list quickly.
Dremio and Starburst can run on-prem and in major clouds, which fits regulated environments like banks, healthcare, and government, and also fits teams that need hybrid for practical reasons (latency to on-prem databases, contract limits, or slow migrations). That flexibility can matter more than any single feature.
If you want “it just works” with the least ops work, Snowflake leads
Snowflake is hard to beat on day-one simplicity. You spin up an account, load data, and start querying. Compute and storage are separated, and you scale compute with virtual warehouses without managing clusters or tuning JVM settings.
The tradeoff is that Snowflake works best when data is centralized inside Snowflake. It supports options like external tables for data in cloud storage, but most teams still move core datasets in so they get consistent performance, caching, and the smoothest user experience.
How each platform fits your Data Lakehouse, federate in place or load into a warehouse
A helpful mental model is to picture where your data “lives” and where queries “run.”
Some platforms are great at querying data in place across many systems, like a universal adapter that lets you join lake files with database tables without copying everything first. Others shine when you load data into one managed place, then run lots of queries with predictable behavior and easy sharing.
This choice affects more than performance. It changes how many pipelines you maintain, how many copies of the same dataset exist, and how quickly teams can experiment without waiting for ingestion work.
Starburst is built for querying many systems without moving all the data
Starburst is built on Trino, a distributed SQL engine known for federation. The big idea is simple: run one SQL query that can read from object storage and also from other systems (databases, warehouses, and more), then join the results.
Connector coverage is a major reason teams pick it. Starburst is known for a broad library that includes many legacy and niche sources, which matters when your reality includes more than S3 and Postgres.
Starburst also has strong proof points for large-scale use. Trino’s roots include very large deployments where users ran tens of thousands of queries per day and scanned massive volumes. That history doesn’t guarantee your workload will be fast, but it does show the engine can operate at serious scale when it’s sized and managed well.
Dremio focuses on a lake-first experience with a friendly UI and built-in acceleration
Dremio is often chosen by teams that want a lake-first platform, not just a query engine. You connect to your lake and other sources, then use its catalog, virtual datasets, and semantic-style layers to publish data in a way BI teams can understand.
A standout feature is reflections, which act like smart accelerations for repeat queries. When dashboards hit the same joins and aggregates over and over, reflections can reduce the work dramatically, sometimes turning slow scans into interactive responses without analysts changing their SQL.
Under the hood, Dremio’s engine is built around Apache Arrow for columnar in-memory processing, and it aligns closely with open table formats, especially Apache Iceberg. In many deployments, teams see good performance without the same level of manual tuning they’d expect in older big data stacks.
Snowflake shines when you centralize data for consistent performance and simple sharing
Snowflake’s model is straightforward: load data into Snowflake, then query it with strong defaults. It’s designed for broad adoption, meaning lots of users, lots of tools, and strong security controls without needing to run your own clusters.
Concurrency is where Snowflake feels effortless. Virtual warehouses make it simple to isolate workloads, and multi-cluster setups can scale out when many users hit the system at once.
Snowflake “connectivity” is mostly about getting data into Snowflake and connecting apps and BI tools to Snowflake. It’s not meant to be a live federated query layer across dozens of operational systems. If your plan is “don’t copy data, query it where it sits,” Snowflake may not match that approach.
Performance, concurrency, and cost, what matters in real life
If you’re hoping for a single scoreboard that settles the debate, you’ll be disappointed. The right question isn’t “Which is fastest?” It’s “Which is fastest for our workload, with our data layout, under our cost rules?”
File format and table design matter a lot. A well-managed Iceberg or Parquet layout with good partitioning can change outcomes more than a logo choice. The same is true for usage patterns. A platform that’s perfect for 30 analysts can feel expensive when 800 users start running ad-hoc queries at 9 a.m.
So treat performance, concurrency, and cost as a three-way balance, not a contest.
Big scans and complex joins can favor lake engines, but Snowflake scales concurrency with less effort
Starburst and Dremio are often strong on data lake workloads, big scans, and complex analytical SQL because they can push filters down to the source and run massively parallel plans across distributed compute.
Snowflake can also run big queries well, but its “superpower” is how simple it is to scale concurrency. When you need to serve hundreds to thousands of users, Snowflake’s ability to add more compute clusters behind a warehouse can feel like turning a dial.
Dremio and Starburst can isolate workloads too, just differently. Dremio can run separate engines so BI and ad-hoc don’t fight each other. Starburst can use separate clusters and resource groups to prioritize and control heavy queries. These models work, but they usually require more planning than Snowflake’s managed scaling.
Cost is not just price per hour, it is also storage, data copies, and surprise usage
Cost discussions go off the rails when teams only compare compute rates. The bigger drivers are (1) how many copies of data you keep, (2) how often compute is running, and (3) how quickly usage grows after success.
Snowflake charges for compute and storage inside the platform, and it’s easy to spend more when warehouses run longer than expected or scale out for concurrency. The flexibility is great, but without guardrails, the bill can jump.
Starburst and Dremio often keep data in low-cost object storage and charge mainly for compute. That can reduce duplicate storage and gives you more knobs to tune cost, though you may take on more management work if you self-host.
Public benchmark writeups also hint at the tradeoffs. In one Iceberg-based TPC-DS-style comparison, a managed Starburst setup ran the workload much faster than a single Snowflake warehouse, and Snowflake needed multiple warehouses to reach similar runtimes. The monthly compute cost in that scenario favored Starburst by a wide margin. That’s one test on one dataset, but it matches what many teams see: open lake engines can be very efficient on lake files when configured well.
Dremio adds another angle, you can start with a free edition for smaller use cases, then decide if enterprise features and support are worth it. At large scale, licensing and support choices can change the math for any platform.
A practical decision guide, pick one, or use a hybrid on purpose
Choosing a Data Lakehouse platform is a lot like choosing where to cook. You can build a great kitchen at home, or you can eat at a restaurant that always has staff on hand. The best answer depends on how often you cook, how many guests you serve, and how much control you want.
Use the guide below to make an initial call, then prove it with a short test using real queries and real data volumes.
Quick picks by scenario, fastest start, most connectors, best BI acceleration, lowest long-term lock-in
| Scenario | Best Fit | Why It Tends To Win |
| Fastest managed rollout, lots of users | Snowflake | Low ops burden, easy scaling for high concurrency |
| Broad federation across many sources | Starburst | Strong connector breadth, query across systems in place |
| Lake-first self-service with acceleration | Dremio | Catalog and virtual datasets, reflections for repeat Bl |
| Reduce lock-in and data copies | Dremio or Starburst | Open table formats and “query where it lives” patterns |
Lock-in deserves plain talk. Snowflake is proprietary and optimized around its managed service. Dremio and Starburst typically fit better when you want open formats in object storage and the option to move compute without moving data.
When a hybrid setup makes sense, keep curated data in Snowflake and explore the wider lake with Dremio or Starburst
Many teams don’t pick a single tool, they design a division of labor.
A common pattern is to keep trusted, curated reporting data in Snowflake for broad consumption, governed access, and steady performance. Then use Dremio or Starburst to query the wider lake, including very large raw zones, less-used history, or sources you don’t want to ingest yet.
This can also control Snowflake spend. If heavy exploration or long-tail queries move to a lake engine, Snowflake warehouses can stay focused on high-value reporting and shared datasets.
How Data Pilot helps teams implement a Data Lakehouse with Dremio and beyond
Data Pilot helps teams design and implement Data Lakehouse architectures that fit real constraints, not slide decks. That usually starts with an assessment of workloads, data locations, and security needs, then a clear plan for table formats (often Iceberg), governance, and rollout to BI and data science users.
As a Dremio partner, Data Pilot provides certified Dremio Engineers who can accelerate setup and reduce risk. That includes engine sizing, reflection strategy, catalog design, and practical cost controls from day one. If your roadmap includes Starburst or Snowflake alongside Dremio, the same team can help define the boundaries so each platform has a clear job.
Conclusion
The right Data Lakehouse choice comes down to a few checks: where it must run (cloud-only vs hybrid), how much data you’re willing to move, how many sources you need to query, how many users you must support at once, and what cost controls you can enforce.
Don’t rely on opinions or generic benchmarks. Run a short proof of value using 2 to 3 real workloads, with clear guardrails on runtime, concurrency, and spend. If you want a fast, low-risk start, contact Data Pilot for an architecture review or a Dremio-led lakehouse starter plan that gets you to working results quickly.