In this article
Key takeaways
- Choose by workload mix, not features. All three now do warehousing, engineering, AI and even operational Postgres; they differ in what they do best and how much tuning each needs.
- The billing models are different, not just the prices. Databricks bills usage per workload, Snowflake bills warehouse time, and Fabric sells capacity. A cheap list price on the wrong model can still cost more.
- Databricks leads for data engineering, streaming, machine learning and AI built on open formats across clouds. Snowflake leads for SQL-first analytics with little administration. Fabric leads for Microsoft-first organizations living in Power BI.
- For regulated data, all three can sign a BAA, but each has conditions: a compliance profile, a specific edition, or a region. In the Gulf, region availability differs sharply.
- Using two of them together is common and workable, especially Databricks with Power BI on Fabric. Just make it a deliberate design.
Databricks vs Snowflake vs Fabric: the short answer by workload
Choose Databricks if data engineering, streaming, machine learning or AI products dominate your roadmap and you want open formats across clouds. Choose Snowflake if most of the work is SQL analytics and BI and you want a managed platform that needs little tuning. Choose Microsoft Fabric if you are standardized on Microsoft 365, Azure and Power BI and want one capacity-priced SaaS suite.
Engineering and AI first
Large-scale pipelines, streaming, ML and agents, open Delta and Iceberg tables in your own storage, and the same platform on AWS, Azure and Google Cloud.
Best for data and AI buildersSQL analytics first
A mature SQL engine that mostly tunes itself, strong data sharing and marketplace, and a fast path for analysts. Iceberg support has reduced the lock-in argument.
Best for SQL-heavy analytics teamsMicrosoft estate first
Data Factory, Spark, a warehouse, real-time analytics and Power BI on one lake (OneLake), bought as a capacity. Azure only.
Best for Power BI-centric organizationsWhat is Databricks, what is Snowflake, and what is Microsoft Fabric?
Databricks is a data and AI platform built by the original creators of Apache Spark. It popularized the data lakehouse: data stays in open table formats (Delta Lake, and now Apache Iceberg) in your own cloud object storage, while the platform adds transactions, governance through Unity Catalog, SQL warehouses running the Photon engine, pipelines, machine learning and AI agent tooling. It runs on AWS, Azure and Google Cloud.
Snowflake is a managed cloud data platform that began as a SQL data warehouse with storage separated from compute. You create virtual warehouses, run SQL, and Snowflake handles storage layout, tuning and most administration. It has since added Python through Snowpark, Iceberg tables, Cortex AI, Openflow ingestion and a managed Postgres service, and it also runs on all three major clouds.
Microsoft Fabric is Microsoft’s software-as-a-service analytics platform. It combines Data Factory, Spark data engineering, a SQL data warehouse, real-time intelligence, data science and Power BI in one product over OneLake, a single tenant-wide data lake that stores tables in Delta Parquet. You buy capacity rather than paying per query. Fabric is available only on Azure.
Databricks revenue run-rate, growing more than 80% a year, announced August 2026
Databricks press releaseSnowflake customers, with product revenue up 37% year over year in Q2 of fiscal 2027
Snowflake results, Sep 2026paid Microsoft Fabric customers, up more than 60% year over year
Microsoft earnings, Jul 2026All three are growing fast and none is going away, so “which vendor will win” is the wrong question. The useful question is which one fits the work you will do over the next three to five years.
Side-by-side comparison: architecture, cost, workloads and governance
Filter the rows by topic. On a phone, pick a platform to see its column.
| Dimension | DatabricksData Intelligence Platform | SnowflakeAI Data Cloud | Microsoft FabricSaaS on Azure |
|---|---|---|---|
| Core modelWhat you are buying | LakehouseSpark and Photon over open Delta and Iceberg tables in your cloud storage | Managed data platformSnowflake-managed storage and virtual warehouses; Iceberg tables optional | SaaS suiteIntegration, engineering, warehouse, real-time and Power BI over OneLake |
| Clouds | AWS · Azure · GCPSame platform and governance on each | AWS · Azure · GCPCross-cloud replication available | Azure onlyShortcuts can read data held in other clouds |
| Table formats | Delta + IcebergManaged Iceberg and Iceberg v3 GA in 2026 | Native + IcebergIceberg v3 GA May 2026; external engines read and write via Horizon | Delta ParquetShortcuts and mirroring bring in other sources |
| Billing unitCompute | DBUsPer workload type; classic compute also pays the cloud VM bill | CreditsPer second of warehouse time, plus storage and serverless features | Capacity unitsAn F-SKU billed per second, or reserved for about 41% less |
| Cost behaviour | Usage-variableServerless, budgets and auto-termination keep it in check | Usage-variableOne-minute resume minimum; default auto-suspend is 10 minutes | Capacity-fixedThrottles instead of surprise bills, unless overage is switched on |
| SQL and BI | StrongSQL warehouses with Photon; Genie One for natural-language questions | Very strongMature engine, minimal tuning, broad BI tool support | Very strongFabric warehouse plus Power BI Direct Lake on OneLake |
| Engineering and streaming | Very strongLakeflow pipelines and connectors, Structured Streaming | StrongDynamic tables, Snowpipe Streaming, Openflow | StrongData Factory, Spark notebooks, Real-Time Intelligence |
| ML and AI | Very strongMLflow, model serving, vector search, Agent Bricks | StrongCortex AI functions and agents, Snowpark ML, CoWork | ModerateCopilot and Fabric IQ; heavy ML usually runs in Azure AI services |
| Operational databasePostgres for apps | LakebaseManaged Postgres, GA on AWS and Azure in 2026 | Snowflake PostgresGA February 2026, from the Crunchy Data acquisition | SQL databaseSQL database in Fabric, GA November 2025 |
| GovernanceCatalog and access | Unity CatalogOpen-sourced in 2024; tables, files, models and agents | Horizon CatalogAccess, lineage and policies, now including Iceberg | OneLake securityPlus Microsoft Purview across the Microsoft estate |
| PHI and HIPAAUS healthcare | BAA + profileCompliance security profile required on PHI workspaces | BAA + editionBusiness Critical edition required for PHI | Microsoft BAAFabric and Power BI covered by the default Microsoft BAA |
| Gulf regionsUAE and KSA | UAE, Qatar, KSAAzure UAE North and Qatar Central; GCP Dammam; no AWS Middle East region | UAE and KSAAWS and Azure in the UAE; GCP Dammam in Saudi Arabia | UAE NorthAll workloads in UAE North; no Saudi region listed |
Feature status checked against vendor documentation and announcements on October 23, 2026. All three ship monthly; check anything your decision depends on.
Databricks pricing vs Snowflake pricing vs Fabric pricing: how each one bills
Comparing list prices directly is misleading, because each platform meters something different. Databricks meters compute by workload type, Snowflake meters warehouse running time, and Fabric sells a capacity you share across every workload. These are the mechanics, with US list prices as of October 2026:
DBUs per workload
+ cloud VMs (classic only)
Each workload type has its own DBU rate. Classic compute also runs VMs billed by your cloud provider; serverless bundles the compute into one higher rate.
- Jobs compute $0.15/DBU, serverless jobs $0.35
- SQL Pro $0.55/DBU, SQL Serverless $0.70
- All-purpose (interactive) $0.55/DBU
- AWS us-east-1, Premium tier. The Standard tier was retired in 2025 on AWS and GCP
Credits per warehouse-second
+ storage + serverless
An X-Small warehouse uses 1 credit an hour and each size up doubles it. Billing is per second after a one-minute minimum each time a warehouse resumes.
- $2 Standard, $3 Enterprise, $4 Business Critical per credit
- Storage $23 per TB per month on demand
- Gen2 warehouses use 1.35× the credits on AWS and are now the default
- AWS US East, on demand. Capacity contracts are discounted
Capacity units
+ OneLake storage
You buy a capacity, F2 to F8192, and every workload draws on it. Usage is smoothed over time, and over-use is throttled rather than billed.
- $0.18 per CU-hour: F2 about $263 a month, F64 about $8,410
- One-year reservation is about 41% cheaper
- OneLake hot storage $0.026 per GB per month
- East US, pay-as-you-go. F64+ lets free users view Power BI content
Why the cheapest list price can still cost moreUsage billing rewards workloads that run briefly and then stop: a nightly batch, a dashboard used in office hours. Capacity billing rewards steady, predictable use, and a reserved Fabric F64 can be good value for a Power BI estate with hundreds of viewers who would otherwise need Pro licences. The costliest mistake is usually a mismatch: an always-on warehouse on a usage-billed platform, or an oversized capacity bought for a workload that runs two hours a day.
Cost pressure is real across the industry. In dbt Labs’ 2026 State of Analytics Engineering survey, 57% of respondents reported increased warehouse and compute spend, while only 36% reported increased team budgets. Whichever platform you choose, tagging, budgets and per-workload compute policies matter more than the rate card.
Where Databricks, Snowflake and Fabric bills surprise teams
Each of these comes from the vendor’s own documentation. None is a flaw, but each regularly catches teams that assume the platform behaves like the one they used before.
Idle interactive clusters keep billingDatabricks
On Databricks, idle compute keeps accumulating DBU and cloud instance charges until it auto-terminates. Auto-termination can be set from 10 to 10,000 minutes, or switched off entirely.
Use cluster policies that enforce auto-termination, prefer serverless or jobs compute for scheduled work, and tag every cluster to an owner and budget.
Classic compute is two bills, not oneDatabricks
Databricks classic compute is billed in DBUs by Databricks and in VMs, storage and networking by your cloud provider. Teams often forecast only the first.
Model both halves, or use serverless where the bundled rate makes cost easier to predict. Watch data transfer charges on serverless too.
Many short queries, many minimum chargesSnowflake
Snowflake bills at least 60 seconds each time a warehouse resumes, and the default auto-suspend of 10 minutes bills idle time after the last query. Interactive warehouses bill a 60-minute minimum.
Tune auto-suspend per warehouse, consolidate small sporadic workloads, and check which warehouse type each workload actually needs.
New warehouses cost more per hour by defaultSnowflake
Gen2 standard warehouses are now the default for new warehouses and use 1.35 times the credits per hour of Gen1 on AWS. They are faster, so they often finish sooner, but not always enough to offset the rate.
Benchmark representative workloads on both generations before standardizing, and right-size after switching.
Overnight jobs eat daytime headroomFabric
Fabric smooths background operations over 24 hours. A heavy overnight load is spread across the following day, leaving less capacity for interactive reports. Sustained overuse first delays, then rejects, interactive requests.
Watch the Capacity Metrics app, separate heavy engineering from Power BI capacity if needed, and schedule loads with smoothing in mind.
Pausing is not free, and overage costs 3×Fabric
Pausing a Fabric capacity immediately bills the smoothed usage it was still carrying. Optional capacity overage billing is charged at three times the normal rate, with no reservation discount.
Pause deliberately, not as a nightly cost saver, and switch on overage only with an alert and a budget owner.
Small Fabric capacities still need Pro licencesFabric
Below F64, every Power BI viewer needs a Pro or Premium Per User licence. Organizations sometimes buy an F8 expecting to share reports freely.
Count viewers before choosing a SKU. For broad internal distribution, compare F64 against per-user licensing.
Workload fit: BI, engineering, streaming, ML and AI
This is our judgement from delivery work and from each platform’s documented capabilities in October 2026. It is not a benchmark. Three dots means a natural fit, two means capable with some trade-offs, and one means possible, but most teams would use something else.
- Natural fit
- Capable, with trade-offs
- Possible, usually done elsewhere
Read across a row, not down a column. If your roadmap is mostly the first and last rows, Snowflake or Fabric will serve you with less effort. If it is mostly rows two to five, the case for Databricks gets stronger with every row.
Databricks vs Fabric and Snowflake vs Fabric: when Microsoft Fabric wins
Fabric is often evaluated as a third option, but it is really a different purchase. You are not choosing a data engine so much as deciding how much of your analytics estate should live inside Microsoft 365 and Azure. When the answer is “most of it”, Fabric has real advantages:
Power BI is already the front end
Direct Lake reads Delta tables in OneLake without import or DirectQuery trade-offs, and an F64 or larger capacity lets free users view reports. For a large reader base that can change the licence maths.
One tenant, one identity, one bill
Entra ID, Purview, Teams and Excel integration come with it, and a capacity is easy to budget. For a small data team inside a Microsoft-first organization, that simplicity is worth a lot.
Mirroring instead of pipelines
Mirroring replicates databases into OneLake with little engineering, with GA sources including Oracle, SAP Datasphere and BigQuery in 2026, and mirrored storage is free up to the capacity size in terabytes.
Gulf buyers on Azure
Search interest in Microsoft Fabric is steady in the UAE and rising in Saudi Arabia. All Fabric workloads run in Azure UAE North, though no Saudi region is listed yet, which matters for data that must stay in the Kingdom.
Databricks and Fabric togetherThe most common architecture we see in Microsoft-first enterprises is not either-or. Azure Databricks handles ingestion, engineering, machine learning and AI on Delta tables governed by Unity Catalog; Power BI on Fabric serves the business. Microsoft announced Fabric read access to Unity Catalog in preview in March 2026, which makes the join cleaner. Fabric falls short when you need multiple clouds, heavy machine learning, or very large engineering workloads that would need an oversized capacity.
Open formats and lock-in: Delta Lake vs Iceberg, catalogs and exit costs
Three years ago “open versus proprietary” separated these platforms clearly. In 2026 it mostly does not, because all three now meet at open table formats. Databricks supports managed Iceberg tables and Iceberg v3 alongside Delta Lake. Snowflake made Iceberg v3 generally available and lets external engines read and write Iceberg tables through its Horizon Catalog. Fabric stores tables in Delta Parquet in OneLake and offers bi-directional access with Snowflake.
Lock-in has moved up a layer, to the catalog (who governs permissions and lineage), the compute features you build on (procedures, AI functions, pipelines), and the BI and semantic layer. Unity Catalog was open-sourced in 2024 and Apache Polaris, the project behind Snowflake Open Catalog, became an Apache top-level project in February 2026. Our advice: keep core tables in an open format whichever platform you choose, and know which proprietary features you are adopting and why.
| Layer | Where lock-in now lives | How to keep options open |
|---|---|---|
| Storage and tables | Low on all three, if you use Delta or Iceberg | Keep core tables in an open format in storage you control |
| Catalog and governance | Medium: permissions, tags and lineage are platform-specific | Define policies as code; prefer catalogs with open APIs |
| Transform logic | Medium to high: procedures, proprietary functions | Keep transformations in SQL or dbt-style models where practical |
| AI and app features | High: AI functions, agents, apps | Adopt them deliberately, where the speed-up is worth the tie-in |
| BI and semantic layer | High in Fabric, where Power BI is integral | Keep metric definitions documented and portable |
Regulated data: HIPAA, PHI and Gulf data residency
For healthcare, financial services and public sector work, two questions often decide the shortlist before features do: will the vendor sign the right agreement, and can the data stay in the required region?
| Platform | US PHI (HIPAA) | UAE | Saudi Arabia |
|---|---|---|---|
| Databricks | Signed BAA, plus the compliance security profile on every workspace that processes PHI (billed as the Enhanced Security and Compliance add-on). Preview features are not allowed for PHI. | Azure Databricks in UAE North | Databricks on Google Cloud in Dammam (me-central2) |
| Snowflake | Signed BAA, and the Business Critical edition for PHI. | AWS me-central-1 and Azure UAE North | Google Cloud Dammam |
| Microsoft Fabric | Covered by Microsoft’s default BAA, which lists Fabric and Power BI in scope. | UAE North (all workloads); UAE Central is Power BI only | No Saudi region listed |
Region and compliance availability checked October 2026. Confirm the specific services and features you need in your chosen region before committing; regional prices are higher than US list prices.
A BAA covers the vendor’s side. Access control, encryption, audit logging, de-identification for analytics and data-sharing agreements are still yours to design. For UAE healthcare data, local law also restricts processing health data outside the country, which is covered in our guide to EHR data migration and archiving.
Migration effort either way
If you already run one of these, the cost of switching belongs in the decision. Rough effort, assuming core tables can move in an open format:
| Move | Effort | What drives it |
|---|---|---|
| Legacy warehouse to any of the three | Highest | Procedural code, ETL, BI repointing and the parallel run. See our warehouse to Databricks migration guide. |
| Snowflake to Databricks | Medium | Iceberg can keep one copy of data during cutover; Snowflake SQL converts with Lakebridge; streams, tasks and sharing need mapping. |
| Databricks to Snowflake | Medium | SQL moves readily; Spark, Python pipelines and ML workloads need re-platforming onto Snowpark or tools outside Snowflake. |
| Synapse or SQL Server to Fabric | Lower | Familiar T-SQL and tooling; Power BI reports move with little change. Still a migration, with reconciliation and testing. |
| Adding Power BI on Fabric to Databricks | Lower | An integration, not a migration: Delta tables surface through OneLake shortcuts or mirroring. |
Which data platform should you choose? A three-question decision guide
This gives a first steer, not a final answer. A proof of concept on your own data is the real test.
Three questions
What will most of the work be over the next three years?
Think about where the engineering hours will go, not just today’s reports.
Is your organization standardized on Microsoft 365, Azure and Power BI?
Including identity, collaboration and most existing reports.
Do you need more than one cloud, or full control of data in open formats?
For example, data residency in several countries, or partners on other clouds.
Shortlist Databricks
Your work is engineering- and AI-heavy, or needs open formats across clouds. Plan Unity Catalog governance and cost guardrails from day one, and use serverless where it simplifies operations. Run a proof of concept on your hardest pipeline, not your easiest.
Shortlist Snowflake
SQL analytics with the least administration is Snowflake’s strength. Choose the edition by compliance needs (Business Critical for PHI), set auto-suspend per warehouse, and keep core tables in Iceberg if you want to keep options open.
Shortlist Microsoft Fabric
When Power BI is the front end and Microsoft is the estate, Fabric’s single capacity and OneLake are hard to beat. Size the SKU by both workload and viewer count, and watch smoothing and throttling in the first months.
Consider Databricks with Power BI on Fabric
Use Azure Databricks for engineering, ML and AI on Delta tables in Unity Catalog, and Fabric and Power BI for business reporting. It is a common enterprise pattern; define clearly where each workload lives so the two do not grow into duplicates.
Our disclosure and how we wrote this comparison
Bonami Software is a Databricks Select Partner. We have tried to make this comparison useful whichever platform you pick, and where Snowflake or Fabric is the better fit, we have said so.
- Prices are US list prices checked on October 23, 2026: Databricks’ published pricing data (AWS us-east-1, Premium), Snowflake’s service consumption table effective October 15, 2026 (AWS US East, on demand), and Azure retail prices for Fabric (East US, pay-as-you-go). Negotiated contracts differ.
- Feature and region status comes from vendor documentation and announcements on the same date.
- Workload-fit ratings are our judgement from delivery work. They are not benchmark results, and we have not run a cross-platform benchmark for this article.
- Company figures come from each vendor’s own announcements and are measured differently: a revenue run-rate, product revenue, and a paid customer count.
If you are weighing these platforms for a real project, our data engineering team runs a short platform-fit assessment. We profile your workloads, model cost on each billing model, and test the hardest use case on the shortlisted platforms. On Databricks we have built governed, agent-based platforms such as member identity resolution, where five specialised agents resolve every incoming member event into one auditable identity.
Frequently asked questions: Databricks vs Snowflake vs Fabric
Is Databricks better than Snowflake?
Neither is better in general. Databricks is usually the stronger fit when data engineering, streaming, machine learning and AI products dominate and you want data in open formats in your own cloud storage. Snowflake is usually the stronger fit for SQL-first analytics and BI with minimal tuning and administration. For SQL warehousing alone the two are closer than ever, so workload mix, team skills and cost model should decide.
What is the difference between Databricks and Snowflake?
Databricks is a lakehouse platform that grew out of Apache Spark. It keeps data in open Delta Lake or Apache Iceberg tables in your cloud storage, governed by Unity Catalog, and bills in Databricks Units plus cloud compute on classic clusters. Snowflake is a managed cloud data platform that grew out of the SQL data warehouse. It manages storage for you, now also supports Iceberg tables, and bills in credits per second of warehouse time plus storage.
What is the difference between Microsoft Fabric and Databricks?
Microsoft Fabric is a software-as-a-service analytics suite on Azure that bundles data integration, data engineering, warehousing, real-time analytics and Power BI over one data lake called OneLake, billed as a reserved or pay-as-you-go capacity. Databricks is a data and AI platform that runs on AWS, Azure and Google Cloud, billed by usage, with deeper engineering, machine learning and AI tooling. Many Microsoft-first organizations use both: Databricks for engineering and AI, Fabric and Power BI for business intelligence.
Which is cheaper, Databricks, Snowflake or Microsoft Fabric?
It depends on the workload, not the list price. In October 2026 US list prices, Snowflake credits cost 2 to 4 dollars depending on edition, Databricks Premium compute ranges from about 0.15 dollars per DBU for jobs to 0.70 dollars per DBU for serverless SQL, and Fabric capacity costs 0.18 dollars per capacity unit hour pay-as-you-go. Usage-billed platforms are cheaper for spiky or small workloads; a reserved Fabric capacity can be cheaper for steady, Power BI-heavy use. Model your own workloads before deciding.
Can you use Databricks and Snowflake together?
Yes. Both read and write Apache Iceberg tables, Databricks can federate Snowflake into Unity Catalog, and Delta Sharing and Snowflake data sharing can exchange data with partners. Running both is common after mergers or when engineering and analytics teams chose differently, but it doubles governance and cost management work, so make it a deliberate architecture rather than an accident.
Who are Databricks’ main competitors?
Snowflake and Microsoft Fabric are the most common alternatives in enterprise evaluations, alongside Google BigQuery, Amazon Redshift and the native analytics services of each cloud. For machine learning and AI workloads, cloud services such as Azure Machine Learning, Amazon SageMaker and Google Vertex AI also compete.
Who is Snowflake’s biggest competitor?
Databricks is Snowflake’s most direct competitor, and the two now overlap in warehousing, data engineering, AI and operational Postgres databases. Microsoft Fabric competes strongly in Microsoft-first organizations, and Google BigQuery and Amazon Redshift compete within their own clouds.
Which platform is best for AI and machine learning?
Databricks has the broadest built-in machine learning and AI tooling, including MLflow, model serving, vector search and Agent Bricks, and is the usual choice when you build and train your own models or agents. Snowflake Cortex AI makes it easy to apply large language models to data already in Snowflake through SQL. Fabric relies on Copilot, Fabric IQ and the wider Azure AI services for most AI work.
Is Microsoft Fabric HIPAA compliant?
Microsoft includes Fabric and Power BI in the scope of its HIPAA business associate agreement, which Microsoft offers by default through its product terms. As with any platform, compliance still depends on how you configure access, encryption, auditing and data handling. Databricks requires a signed BAA and the compliance security profile on workspaces that process PHI, and Snowflake requires a BAA and the Business Critical edition for PHI.
Does Microsoft Fabric work with Databricks?
Yes. Both use Delta Lake tables, OneLake shortcuts can point at data in Azure storage that Databricks writes, and Microsoft announced read access to Databricks Unity Catalog from Fabric in preview in March 2026. A common pattern is Databricks for engineering and machine learning with Power BI on Fabric for reporting.
What is a data lakehouse?
A data lakehouse stores data in open table formats such as Delta Lake or Apache Iceberg on low-cost cloud object storage, and adds the transactions, schema enforcement, governance and fast SQL that used to require a separate data warehouse. Databricks popularized the term; Fabric’s OneLake and Snowflake’s Iceberg tables follow the same idea.
Will Databricks overtake Snowflake?
By reported scale it already has on some measures. Databricks announced a revenue run-rate above 7 billion dollars in August 2026, while Snowflake guided to about 6.07 billion dollars of product revenue for fiscal 2027. The two figures are measured differently, and both companies are growing quickly, so scale should not decide your platform. Fit to your workloads should.
The takeaway: pick the billing model and the workload, not the logo
Databricks, Snowflake and Microsoft Fabric have converged on features faster than most buyers expected. All three now offer a lakehouse-style architecture, open table formats, AI assistants and even managed Postgres. What has not converged is what each does best, how each charges, and how much of your organization each expects to sit inside.
Start from the work: list the workloads for the next three years, estimate how steady or spiky each is, and note your compliance and region constraints. Shortlist two platforms, run a proof of concept on your hardest real use case, and model cost on each platform’s own billing model. That will tell you more than any comparison table, including this one.
Choosing a data platform?
Tell us your main workloads, current stack, compliance needs and target regions. We will tell you which two platforms we would shortlist and why, and what a proof of concept should test.
Sources
- Databricks pricing (and published AWS pricing data, October 2026)
- Snowflake service consumption table, effective October 15, 2026
- Microsoft Fabric pricing
- Microsoft Learn: Fabric licenses and capacity SKUs
- Microsoft Learn: Fabric smoothing and throttling
- Databricks docs: managing compute and auto-termination
- Snowflake docs: CREATE WAREHOUSE (auto-suspend)
- dbt Labs: State of Analytics Engineering 2026
- Databricks: $7B revenue run-rate (August 2026)
- Snowflake: Q2 fiscal 2027 results
- Microsoft: FY2026 Q4 earnings (Fabric customers)
- Databricks docs: HIPAA compliance · Snowflake editions · Microsoft HIPAA/HITECH
- Fabric region availability · Azure Databricks regions · Snowflake regions
- Databricks: Unity Catalog and Apache Iceberg (2026) · Apache Polaris graduates
Prices are US list prices on October 23, 2026 and exclude taxes, discounts and regional differences. Product names and features change often; confirm details with each vendor. Bonami Software is a Databricks Select Partner. Databricks, Snowflake and Microsoft Fabric are trademarks of their respective owners.
