Australia verdict (TL;DR)
Verified 2026-05-27Australia is a Databricks and Snowflake-led lakehouse market at ASX 200 and Tier 1 enterprise, with Snowflake particularly strong at Australian retail, telco, and financial services (CBA, NAB, Telstra, Coles, Woolworths) and Databricks dominant where ML and AI workloads sit alongside analytics. Canva, Atlassian, Afterpay (Block), and SafetyCulture set the Australian product-SaaS reference architecture; Canva is one of the largest Snowflake customers globally and Atlassian publishes substantial public detail on its Databricks lakehouse. Microsoft Fabric OneLake is growing fast in Australian Microsoft enterprise. AWS Sydney (ap-southeast-2) and Azure Australia East (Sydney) plus Australia Central (Canberra) are the residency defaults; both hold IRAP PROTECTED for Commonwealth and Defence scope. There is no Australian-headquartered lakehouse vendor of meaningful scale; the honest assessment is that this is a US-vendor-dominated category in Australia, mediated by strong Australian Macquarie Cloud Services and AC3 SI partner ecosystem.
Picks for Australia
- ASX 200 enterprise SQL lakehouse: snowflake-lakehouse Default at CBA, NAB, Telstra, Coles, Woolworths-tier Australian enterprise. Canva is one of the largest Snowflake customers globally. AWS ap-southeast-2 Sydney with Polaris and native Iceberg.
- Australian ML-heavy lakehouse: databricks-lakehouse Default where ML and AI workloads sit alongside analytics. Atlassian, REA Group, Xero adoption. Databricks Sydney engineering presence. Unity Catalog for APP and APP 8 residency lineage on Azure Australia East or AWS Sydney.
- Australian Microsoft enterprise lakehouse: microsoft-fabric-onelake Fastest-growing in Australian M365 E5 enterprise (state governments, utilities, mid-market). OneLake on Azure Australia East Sydney and Australia Central Canberra; bundle economics inside M365.
- Australian AWS-anchored product company: aws-lake-formation S3 Tables plus Glue Catalog on AWS ap-southeast-2 Sydney. IRAP PROTECTED-assessed; lowest friction for AWS-native Australian SaaS and scale-ups.
- Australian GCP lakehouse: biglake BigQuery plus Iceberg on GCP australia-southeast1 Sydney. Common at Australian adtech, B2B SaaS, and select retail on GCP.
- Commonwealth and Defence-classified lakehouse: apache-iceberg Self-hosted Iceberg on AWS Sydney or Azure Australia Central Canberra with IRAP PROTECTED assessment, where federation across multiple engines and full control of data plane matters more than vendor convenience.
How the data lakehouse market looks in Australia
Australia's lakehouse market in 2026 is led by Snowflake and Databricks at ASX 200 and Tier 1 enterprise, with strong product-SaaS reference architectures from Australian-origin technology companies setting market expectations. Canva (one of the largest Snowflake customers globally), Atlassian (substantial public detail on its Databricks lakehouse architecture), Afterpay (now Block), REA Group, SEEK, Xero (Wellington-headquartered but heavy Australian engineering presence), and SafetyCulture have been visible reference architectures that anchor Australian buyer expectations. The Atlassian heritage in particular has cultural influence on Australian tech procurement: buyers expect transparent pricing, fast time-to-value, and self-service evaluation, which Snowflake and Databricks both deliver more naturally than legacy DWH incumbents.
ASX 200 enterprise deployment is split: Snowflake is dominant at Australian retail (Coles, Woolworths, Wesfarmers), telco (Telstra, Optus, TPG), and financial services (CBA, NAB, Westpac, Macquarie analytics teams) where the SQL-first cloud DWH-to-lakehouse path matches existing skills; Databricks is dominant where ML and AI workloads sit alongside analytics (financial crime detection, fraud, supply chain optimisation). Microsoft Fabric OneLake is the fastest-growing platform in Australian Microsoft enterprise, particularly at state governments (NSW, Victoria, Queensland, WA agencies on M365 E5), utilities, and Mittelstand-equivalent ASX 300 mid-market.
The Australian SI partner ecosystem mediates a large fraction of lakehouse procurement: Deloitte Australia, EY Australia, PwC Australia, KPMG Australia, Accenture Australia at large enterprise; Mantel Group, Servian (now Cognizant), Eliiza, Versent, AC3, Macquarie Cloud Services, and Boab AI at the Australian-native specialist tier. Macquarie Cloud Services in particular provides Australian sovereign hosting for AWS-, Azure-, and self-managed workloads with Australian data centre residency, which matters for federal and state scope. Atlassian-origin influence on Australian tech procurement biases toward Snowflake and Databricks over Oracle Analytics, Teradata, or SAP Datasphere.
AWS Sydney (ap-southeast-2) and Azure Australia East (Sydney) plus Australia Central (Canberra) are the residency defaults; both hold IRAP PROTECTED-level assessment for Commonwealth and Defence scope. GCP australia-southeast1 Sydney and australia-southeast2 Melbourne hold OFFICIAL: Sensitive IRAP assessment. There is no Australian-headquartered lakehouse vendor of meaningful scale comparable to Databricks or Snowflake; the honest assessment is that this is a US-vendor-dominated category in Australia, mediated by strong Australian channel and SI partners.
Privacy Act 1988 and the Australian Privacy Principles (APPs), administered by the OAIC: personal data ingested into a lakehouse is in scope; APP 8 (cross-border disclosure) requires the disclosing entity to take reasonable steps to ensure overseas recipients comply with the APPs or remain accountable for breaches, which drives Australian preference for AWS Sydney, Azure Australia East and Central, and GCP Sydney residency. Notifiable Data Breaches scheme (NDB, in force since 2018): eligible breaches must be notified to the OAIC and affected individuals as soon as practicable; the 2024-2025 Privacy Act reform trajectory has shifted practical expectations toward 72-hour notification windows for serious breaches, which makes lineage and audit logging features (Unity Catalog, Polaris, Glue Catalog audit logs) procurement-relevant. IRAP (Information Security Registered Assessors Program) at the PROTECTED classification: lakehouse used on Commonwealth-classified scope must run on IRAP PROTECTED-assessed infrastructure; AWS Sydney and Azure Australia Central Canberra hold PROTECTED-level IRAP; Databricks, Snowflake, AWS Lake Formation and S3 Tables, Microsoft Fabric OneLake, and BigLake inherit underlying cloud IRAP status but project teams must validate scope at procurement, particularly the data plane and any control plane components that may sit outside the Australian region. ACSC Essential Eight: the ACSC cyber baseline expected of Commonwealth and most state agency suppliers; SSO via SAML or OIDC, MFA, application control, and patching maturity are procurement checks. Security of Critical Infrastructure (SOCI) Act amendments 2022 and 2023: where the lakehouse touches designated critical infrastructure (energy, water, transport, communications, financial services, food and grocery, defence industry, higher education and research, health care and medical, space technology), the asset owner has Risk Management Program (CIRMP) obligations that propagate to lakehouse supplier contracts. Consumer Data Right (CDR) administered by ACCC and OAIC: open banking, open energy, and emerging open finance data flows into and out of the lakehouse must comply with CDR data minimisation, consent, and accredited data recipient rules; relevant for Australian banks, fintechs, energy retailers, and aggregators. APRA CPS 234 Information Security and CPS 230 Operational Risk Management: regulated APRA entities (banks, insurers, super funds) must meet specific information security and operational resilience standards for cloud arrangements; APRA expects board-level oversight and material service provider notification. ASIC market integrity rules for capital markets data. ATO Single Touch Payroll (STP) and Standard Business Reporting integration for finance lakehouse use cases. Modern Slavery Act 2018: applies to vendor procurement disclosure for entities over AUD 100m revenue; all major lakehouse vendors publish Modern Slavery statements.
Quick comparison, ranked for Australia
| Product | Best for | Starts at | 10-emp/mo* | Pricing | G2 | Geo |
|---|---|---|---|---|---|---|
| 2 Snowflake + Polaris Catalog | Mid-market through global enterprise | $0 | $0 | 4.5 | Global | |
| 1 Databricks Lakehouse Platform | Mid-market through global enterprise | $0 | $0 | 4.5 | Global | |
| 5 Microsoft Fabric OneLake | Microsoft-anchored mid-enterprise through global enterprise | $263 | $263 | 4.4 | Global | |
| 3 AWS Lake Formation + Iceberg | AWS-anchored teams of any size | $0 | $0 | 4.2 | Global | |
| 4 Google BigLake | GCP-anchored teams of any size | $0 | $0 | 4.4 | Global | |
| 6 Apache Iceberg | Engineering-led teams of any size | $0 | $0 | 4.6 | Global | |
| 7 Delta Lake | Engineering-led teams, Databricks-anchored | $0 | $0 | 4.5 | Global | |
| 9 Dremio | Engineering-led lakehouse teams | Quote | - | 4.4 | Global | |
| 10 Starburst | Engineering-led federated query teams | $0 | $0 | 4.4 | Global | |
| 8 Apache Hudi + Onehouse | Streaming-first engineering teams | $0 | $0 | 4.3 | Global |
*10-employee monthly cost = base fee + (per-employee × 10) using the lowest published tier. For opaque-pricing vendors, no value is shown.
What buyers in Australia actually pay
Median annual deal size by employee band, in AUD. Crowdsourced from anonymized buyer disclosures.
| Product | Employee band | Median annual (AUD) | Sample | Notes |
|---|---|---|---|---|
| Snowflake + Polaris Catalog | ASX 200 enterprise 500-5,000 employees | A$280,000 | 38 | Enterprise tier with Iceberg via Polaris; AWS Sydney; AUD billing via Snowflake Australia; multi-year capacity commitment common |
| Databricks Lakehouse Platform | ASX 200 enterprise 500-5,000 employees | A$320,000 | 31 | Azure Australia East or AWS Sydney; DBU consumption; AUD via Databricks Australia or Azure EA; Unity Catalog standard |
| Microsoft Fabric OneLake | AU M365 E5 enterprise 500-5,000 employees | A$180,000 | 42 | F64 Fabric capacity inside M365 E5 bundle; Azure Australia East Sydney; AUD via Microsoft Australia |
| AWS Lake Formation + Iceberg | AU SaaS or scale-up 100-1,000 employees | A$110,000 | 26 | Combined Glue plus Lake Formation plus S3 Tables plus Athena; AWS Sydney; AUD billed |
| Google BigLake | AU GCP-native scale-up 100-1,000 employees | A$95,000 | 22 | BigQuery editions plus Cloud Storage; GCP australia-southeast1 Sydney; AUD billed via GCP Australia |
Australia-built or Australia-strong vendors worth knowing
Not yet ranked in our global top 10, but credible options for Australia buyers and worth a shortlist.
No Australian-headquartered lakehouse vendor of meaningful scale
The Australian lakehouse market is dominated by US-headquartered vendors (Databricks, Snowflake, AWS, Microsoft, Google) consumed via Australian-region deployments. Honest assessment for the category.
Canva (Sydney) as Snowflake reference architecture
Visit ↗Canva is one of the largest Snowflake customers globally; published Snowflake reference architecture has influenced Australian product-SaaS lakehouse design. Not a vendor, a reference customer that shapes market expectations.
Atlassian (Sydney) as Databricks reference architecture
Visit ↗Atlassian publishes substantial detail on its Databricks lakehouse architecture; the Australian-origin Atlassian engineering culture has cultural influence on Australian tech procurement biasing toward modern cloud platforms over legacy DWH incumbents.
Macquarie Cloud Services (Sydney)
Visit ↗Sydney-headquartered Australian cloud hosting provider with sovereign Australian data centre residency. Common substrate for federal and state government cloud workloads and self-managed Iceberg or Delta deployments where Commonwealth-only residency matters.
Mantel Group, Eliiza, Versent, AC3, Boab AI (Australian data SI partners)
Australian-native data and AI consulting partners that mediate a large fraction of Databricks, Snowflake, and Fabric lakehouse implementations at ASX 200 and state government scope. Not lakehouse vendors but the practical Australian implementation channel.
All 10, ranked for Australia
Same intelligence as the global ranking, vendor trust, review patterns, verified pricing, compliance, reordered for the Australia market.
Snowflake + Polaris Catalog
Cloud-neutral managed lakehouse with native Iceberg and open-sourced Polaris Catalog.
Snowflake (NYSE:SNOW) made a genuine strategic shift toward open lakehouse architecture in 2024: native Iceberg tables reached read/write parity with internal tables, and the Polaris Catalog was open-sourced in Jun 2024 as an Apache Iceberg REST catalog implementation. The honest reading is that this is a real bet on Iceberg interop, partly defensive against Databricks-on-Delta and partly offensive into the open-format buyer segment. The trade-off: whether enterprise customers actually benefit depends on which catalog they pick, and Snowflake credit-based pricing remains easy to overspend without governance. Best fit for SQL-first enterprises wanting open format with managed SaaS.
Cloud-neutral enterprises (500+ employees) wanting lakehouse semantics in Iceberg without operating a separate engine, with a strong preference for managed SaaS and SQL workloads.
Heavy AI/ML training shops (Databricks better), single-cloud teams that could just use BigLake or Lake Formation, or buyers who reject credit-based pricing.
Strengths
- Native Iceberg tables GA with read/write parity to internal tables
- Polaris Catalog open-sourced Jun 2024 as Apache Iceberg REST catalog
- Cloud-neutral: native on AWS, Azure, GCP
- Snowpark for Python/Java/Scala in-lakehouse processing
- Strong governance, masking, and row-level security
Weaknesses
- Credit-based pricing easy to overspend without strict governance
- External Iceberg catalogs require careful planning; performance trade-offs vs internal tables
- May 2024 customer credential incident still discussed in deals
Pricing tiers
partial- StandardOn-demand $2/credit; storage $23/TB/month compressed$0 /mo
- EnterpriseOn-demand $3/credit; multi-cluster warehouses, masking$0 /mo
- Business CriticalOn-demand $4/credit; HIPAA, PCI, customer-managed keys$0 /mo
- Virtual Private Snowflake (VPS)Dedicated metadata service for regulated industriesQuote
- · Compute credit overruns from un-suspended warehouses
- · External Iceberg query has different perf characteristics than internal tables
- · Cross-region data egress
Key features
- +Native Iceberg tables (managed and external)
- +Polaris Catalog (open-source Apache Iceberg REST catalog)
- +Snowpark for Python/Java/Scala
- +Time Travel and Zero-Copy Cloning
- +Snowpipe streaming ingestion
- +Secure Data Sharing and Marketplace
Databricks Lakehouse Platform
Delta Lake-native lakehouse with Unity Catalog and Mosaic AI; Iceberg-aware after Tabular acquisition.
Databricks is the enterprise lakehouse leader, unifying data engineering, analytics, and ML/AI training on Delta Lake + Unity Catalog. The Jun 2024 acquisition of Tabular (the Iceberg-creator-led startup) for a reported $1B+ creates obvious tension because Databricks is the lead maintainer of Delta Lake, the rival format to Iceberg; the public position is that Databricks will support both via Delta UniForm and through ongoing Iceberg contribution. Last private valuation was $43B in Sept 2023 (reported $62B in subsequent rounds), with a 2026 IPO widely expected but not confirmed. Trade-offs: DBU pricing complexity, and SQL-only buyers often find Snowflake simpler.
Mid-market and enterprise data teams (200-50,000 employees) running serious ML training plus analytics, where lakehouse governance and AI workflow integration matter more than pure SQL simplicity.
SQL-only BI shops (Snowflake or BigQuery simpler), Iceberg-purist buyers wary of Databricks owning Delta Lake, or small teams without dedicated data engineering.
Strengths
- Delta Lake as the open default plus Delta UniForm Iceberg interop
- Unity Catalog unifies governance across analytics, ML, and lakehouse tables
- Best-in-class for AI/ML training and feature engineering via Mosaic AI
- Tabular acquisition brought Iceberg-creator engineering talent in-house
- Photon vectorized engine narrows SQL gap to dedicated warehouses
Weaknesses
- DBU pricing complexity, plus separate cloud infra costs charged by hyperscaler
- Delta vs Iceberg neutrality is contested given Databricks owns Delta Lake project
- Unity Catalog migration painful for legacy Hive metastore customers
Pricing tiers
partial- Standard (Jobs)From $0.15/DBU; basic Spark workloads$0 /mo
- PremiumFrom $0.40/DBU; SQL warehouses, Unity Catalog, audit logs$0 /mo
- EnterpriseFrom $0.65/DBU; HIPAA, PCI, customer-managed keys$0 /mo
- Mosaic AI Model TrainingFoundation model training and serving; custom quoteQuote
- · Cloud infra (EC2/Azure VMs/GCE) billed by hyperscaler, not Databricks
- · Photon premium DBU multiplier on SQL warehouses
- · Mosaic AI inference and training billed separately
Key features
- +Delta Lake (open table format)
- +Delta UniForm (Iceberg metadata interop)
- +Unity Catalog governance
- +Photon vectorized SQL engine
- +Mosaic AI (training, fine-tuning, serving)
- +Lakehouse Federation across S3/ADLS/GCS
- +Delta Sharing (open data sharing protocol)
Microsoft Fabric OneLake
Microsoft unified lakehouse store: Delta-native, with Iceberg via shortcuts and Power BI bundle economics.
OneLake is the unified data lake layer underneath Microsoft Fabric, announced in May 2023 as part of Microsoft Fabric and using Delta Lake as the native open format. The 2024-2025 additions of OneLake shortcuts to Iceberg tables (in S3, ADLS, and elsewhere) and the broader Fabric Iceberg interop make OneLake the closest thing to a multi-format lakehouse store from Microsoft. The honest framing: OneLake wins deals through Power BI Premium bundle pricing and Microsoft 365 procurement leverage, not because the underlying engine is best-in-class. Capacity Unit (CU) pricing complexity remains the main cost-forecasting issue.
Microsoft 365 + Power BI Premium-anchored enterprises (500-100,000+ employees) where Fabric capacity comes effectively-free with existing M365 E5 commitments.
Non-Microsoft-anchored teams, organizations rejecting Capacity Unit pricing, or buyers wanting best-in-class engine performance over bundle economics.
Strengths
- OneLake as Delta Lake-native unified analytics store
- OneLake shortcuts allow read of Iceberg tables in S3, ADLS, elsewhere
- Power BI Premium bundle, often effectively-free with E5 commitments
- Copilot integrated across the Fabric suite
- One SKU covers lakehouse + warehouse + BI + ETL + real-time
Weaknesses
- Wins on bundle economics, not core engine quality
- Capacity Unit (CU) pricing complexity
- Iceberg support via shortcuts is read-mostly vs full lakehouse semantics
Pricing tiers
partial- F2 (smallest)2 CU; pay-as-you-go$263 /mo
- F6464 CU; common mid-size enterprise capacity$8400 /mo
- F20482,048 CU; very large enterprise capacity$269000 /mo
- Bundled with Power BI PremiumF64 effectively included with P1 commitments at many enterprisesQuote
- · OneLake storage billed separately at ADLS rates
- · Cross-region data egress
- · Mirroring usage can spike CU consumption
Key features
- +OneLake (Delta Lake-native unified store)
- +OneLake shortcuts (Iceberg read in S3/ADLS)
- +Fabric Lakehouse (Spark + SQL endpoint)
- +Fabric Warehouse (T-SQL warehouse)
- +Power BI native integration
- +Copilot in Fabric
- +Mirroring (Snowflake, Cosmos, Azure SQL)
AWS Lake Formation + Iceberg
AWS-native lakehouse: Glue Catalog, Lake Formation governance, and S3 Tables for Iceberg.
AWS Lake Formation is the AWS-native lakehouse governance layer over S3, with AWS Glue Data Catalog as the metadata store and Lake Formation managing fine-grained access controls. The 2024 Re:Invent S3 Tables announcement made Iceberg a first-class S3 bucket type, removing the need for a separate Iceberg metastore for many AWS-native pipelines. The lakehouse engines on top are Athena, EMR, Redshift Spectrum, and Glue ETL. Strengths: deep AWS integration, IAM-native access, and Iceberg-native S3. Trade-offs: best-fit narrows sharply when not AWS-anchored, governance UX is more workmanlike than Unity Catalog, and pricing fragments across Glue, Lake Formation, S3 Tables, and the chosen query engine.
AWS-anchored organizations (any size) where S3 is already the data plane and the team wants to add Iceberg + governance without leaving AWS.
Multi-cloud or non-AWS teams, organizations wanting a single integrated lakehouse vendor (Databricks or Snowflake), or buyers wanting opinionated governance UX.
Strengths
- Iceberg-native S3 Tables (2024 GA) removes need for separate metastore
- AWS Glue Data Catalog as the metadata layer with broad AWS integration
- Lake Formation fine-grained access on rows, columns, and tags
- IAM-native authentication and tag-based access control
- Query engine flexibility: Athena, EMR, Redshift Spectrum, Glue ETL
Weaknesses
- Best-fit narrows sharply when not AWS-anchored
- Governance UX more workmanlike than Unity Catalog or Polaris
- Pricing fragments across Glue, Lake Formation, S3 Tables, query engine
Pricing tiers
public- Glue Data Catalog$1/100k objects/month; first 1M free$0 /mo
- Lake FormationNo additional charge; underlying services billed separately$0 /mo
- S3 TablesStorage at S3 standard rates; per-request fees$0 /mo
- Athena (query)$5/TB scanned; or capacity reservation$0 /mo
- · S3 Tables compaction and maintenance request fees
- · Glue ETL DPU consumption
- · Cross-region data egress
- · Athena/EMR/Redshift Spectrum billed separately as compute
Key features
- +AWS S3 Tables (Iceberg-native S3 buckets)
- +AWS Glue Data Catalog
- +Lake Formation fine-grained access controls
- +Tag-based access control
- +Cross-account data sharing
- +Native Apache Iceberg support
- +Integration with Athena, EMR, Redshift Spectrum
Google BigLake
BigQuery engine over open table formats: Iceberg, Hudi, and Delta on Cloud Storage.
BigLake is Google Cloud lakehouse layer that lets BigQuery (and other GCP engines including Dataproc Spark and Dataflow) query Apache Iceberg, Apache Hudi, and Delta Lake tables on Cloud Storage with the same governance model as native BigQuery tables. The fit: GCP-anchored teams who already use BigQuery as the analytics engine and want to add lakehouse semantics over open formats without operating a separate platform. Strengths: tightest integration with BigQuery, Looker, and Vertex AI; native Iceberg, Hudi, and Delta support; and serverless query economics. Trade-offs: best-fit narrows sharply when not GCP-anchored, and cross-cloud egress economics favor staying inside GCP.
GCP-anchored organizations (any size) wanting lakehouse semantics on Iceberg/Hudi/Delta with BigQuery as the primary engine, plus tight Looker and Vertex AI integration.
Multi-cloud or AWS/Azure-anchored organizations, teams that need a single integrated lakehouse vendor across clouds, or buyers without existing BigQuery investment.
Strengths
- Native Iceberg, Hudi, and Delta Lake support on Cloud Storage
- Same governance model as BigQuery (Policy Tags, BigQuery IAM)
- BigQuery serverless query economics extend to open tables
- BigQuery Omni for cross-cloud query against AWS S3 and Azure
- Tight integration with Vertex AI and Looker
Weaknesses
- Best-fit narrows sharply when not GCP-anchored
- Cross-cloud egress economics favor staying inside GCP
- External table query has different perf characteristics than native BigQuery
Pricing tiers
public- On-demand$6.25/TB scanned on BigQuery; Cloud Storage at standard rates$0 /mo
- BigQuery Editions Standard$0.04/slot-hour; capacity reservations$0 /mo
- BigQuery Editions Enterprise$0.06/slot-hour; CMEK, VPC-SC$0 /mo
- BigQuery Editions Enterprise Plus$0.10/slot-hour; cross-region replication$0 /mo
- · Cloud Storage class tiering
- · BI Engine memory reservation
- · Cross-region or cross-cloud egress
Key features
- +Native Apache Iceberg, Hudi, and Delta Lake support
- +BigQuery engine over Cloud Storage tables
- +BigLake Metastore (Iceberg-compatible)
- +BigQuery Omni cross-cloud query
- +Policy Tags for column-level access
- +Vertex AI integration
Apache Iceberg
The winning open table format of 2025-2026 by hyperscaler buy-in.
Apache Iceberg is the open table format originated at Netflix in 2017, donated to the Apache Software Foundation, and now the de facto winner of the open-table-format war in 2025-2026 on the strength of hyperscaler buy-in. AWS (S3 Tables, Athena, EMR, Redshift), Google (BigLake, BigQuery), Microsoft (Fabric via shortcuts), and Snowflake all support Iceberg as a first-class format. Databricks acquired Tabular (the company founded by Iceberg creators Ryan Blue and Daniel Weeks) in Jun 2024 for a reported $1B+, which brought core Iceberg engineering talent into the Delta Lake-stewarding company; the public position is dual-format support. The honest read: pick Iceberg unless you are deep on Databricks.
Engineering-led organizations of any size committing to open-format lakehouse architecture, particularly multi-engine or multi-cloud teams who want to avoid table-format lock-in.
Teams deep on Databricks where Delta Lake is the path of least resistance, or shops that prefer fully managed lakehouse SKUs over assembling components.
Strengths
- De facto open-table-format winner by hyperscaler buy-in
- ACID transactions, time travel, schema evolution, hidden partitioning
- Iceberg REST catalog spec standardized (Polaris, Nessie, Glue support it)
- Vendor-neutral by design and Apache-governed
- Strong contributor diversity across AWS, Apple, Netflix, Stripe, Tabular
Weaknesses
- Catalog choice (Polaris, Unity, Glue, Nessie) is the real lock-in decision
- Maintenance operations (compaction, snapshot expiry) require operational discipline
- Tabular acquisition by Databricks creates uncertainty about long-term neutrality
Pricing tiers
public- Apache IcebergApache 2.0; unlimited use; community support$0 /mo
- Commercial managed offeringsSnowflake Polaris, AWS S3 Tables, Tabular (Databricks), Dremio, Cloudera, OnehouseQuote
Key features
- +ACID transactions on object storage
- +Time travel and snapshot isolation
- +Schema evolution (add, drop, rename columns)
- +Hidden partitioning and partition evolution
- +Iceberg REST catalog spec
- +Multi-engine read/write (Spark, Trino, Flink, Presto, Snowflake, BigQuery)
Delta Lake
Databricks-led open table format with Iceberg interop via Delta UniForm.
Delta Lake is the open table format created at Databricks, open-sourced under the Linux Foundation in 2019, and the native format for the Databricks Lakehouse Platform. It remains strong inside Databricks (Unity Catalog assumes Delta as the default) and has hedged for the Iceberg-dominant 2025-2026 landscape via Delta UniForm (2024), which writes Iceberg metadata in parallel so external engines can read Delta tables as if they were Iceberg. The honest framing: if Databricks is your primary engine, Delta is the right format; if you want format neutrality across hyperscalers, Iceberg is winning. Microsoft Fabric OneLake also uses Delta natively, which keeps Delta relevant outside Databricks.
Organizations standardized on Databricks or Microsoft Fabric where Delta is the path of least resistance, with Delta UniForm available for occasional Iceberg interop.
Multi-engine shops choosing one format, or organizations on AWS/GCP-native lakehouse stacks where Iceberg has stronger first-party support.
Strengths
- Native format for Databricks Lakehouse and Microsoft Fabric OneLake
- Delta UniForm writes Iceberg metadata for cross-engine read
- Mature ecosystem inside Databricks and Spark
- ACID transactions, time travel, schema evolution
- Delta Sharing as open data sharing protocol
Weaknesses
- Hyperscaler buy-in (AWS, GCP) is weaker than for Iceberg
- Databricks-led project governance raises neutrality questions for non-Databricks shops
- Delta UniForm Iceberg interop is one-way (write Delta, read Iceberg) at most engines
Pricing tiers
public- Delta LakeApache 2.0; unlimited use; community support$0 /mo
- Commercial managedDatabricks, Microsoft Fabric, Onehouse all offer managed DeltaQuote
Key features
- +ACID transactions on object storage
- +Time travel and version control
- +Schema evolution and enforcement
- +Delta UniForm (Iceberg metadata interop)
- +Delta Sharing (open data sharing protocol)
- +Native Databricks and Microsoft Fabric integration
Dremio
Lakehouse-native query engine on Iceberg with Project Nessie Git-for-data catalog.
Dremio is the lakehouse-native query engine purpose-built for SQL on Apache Iceberg tables in S3/ADLS/GCS, with Project Nessie as the Git-for-data catalog. The fit: teams that want to separate storage from compute vendor, run their data in Iceberg in their own object store, and use Dremio as the engine without committing to Databricks or Snowflake compute. Series E $410M raised in Jan 2022 at $2B+ valuation; no significant funding rounds publicly disclosed since. Strengths: Iceberg-first engineering, Nessie data versioning, and reflections (acceleration layer) for sub-second BI. Trade-offs: smaller market presence than Databricks/Snowflake, narrower ecosystem.
Engineering-led teams (100-5,000 employees) committing to Iceberg lakehouse architecture who want to separate storage from compute vendor and use a query engine outside the Databricks/Snowflake duopoly.
Buyers wanting fully managed integrated lakehouse + ML platform (Databricks), heavy AI/ML training shops, or teams without dedicated data engineering capacity.
Strengths
- Iceberg-first lakehouse query engine
- Project Nessie for Git-for-data versioning and branching
- Reflections (materialized view acceleration) for sub-second BI
- Apache Arrow-based engine with strong query performance
- Bring-your-own-cloud and bring-your-own-object-store model
Weaknesses
- Smaller market presence than Databricks or Snowflake
- No significant funding round publicly disclosed since 2022
- Narrower BI and partner ecosystem than the leaders
Pricing tiers
partial- Cloud StandardManaged Dremio on AWS/Azure; usage-basedQuote
- Cloud EnterpriseAdvanced governance, SSO, dedicated supportQuote
- Software (self-hosted)On-prem or BYOC; subscription-basedQuote
Key features
- +Iceberg-native query engine
- +Project Nessie (Git-for-data catalog)
- +Reflections (materialized view acceleration)
- +Apache Arrow-based execution
- +Lakehouse semantics: ACID, time travel, branching
- +SQL over S3/ADLS/GCS
Starburst
Managed Trino with multi-format lakehouse support and Stargate federation.
Starburst is the commercial company behind Trino (the open-source distributed SQL query engine, formerly PrestoSQL), offering Starburst Galaxy (SaaS) and Starburst Enterprise (self-hosted) as managed Trino with multi-format lakehouse support (Iceberg, Delta, Hudi) and Stargate federation across data sources. Series D $250M raised in Feb 2022 at $3.35B valuation; no major funding round publicly disclosed since. Strengths: federated query across lakehouse plus operational data sources (Postgres, MySQL, Mongo, etc.), Trino community heritage, and multi-format support. Trade-offs: smaller than Databricks/Snowflake, primary value is federation rather than being a one-stop lakehouse.
Engineering-led teams (100-10,000 employees) with federation requirements across lakehouse plus operational data sources, who value Trino open-source heritage and multi-format support.
Buyers wanting fully managed integrated lakehouse + ML platform (Databricks), or teams that only need single-format Iceberg query (Dremio or BigLake fit).
Strengths
- Managed Trino with multi-format support (Iceberg, Delta, Hudi)
- Stargate federation across 50+ data sources (lakehouse plus operational)
- Strong open-source Trino heritage and community
- Bring-your-own-cloud and BYO-object-store model
- Galaxy SaaS plus self-hosted Enterprise options
Weaknesses
- Smaller than Databricks/Snowflake on managed enterprise share
- No major funding round disclosed since Feb 2022 ($3.35B valuation)
- Primary value is federation; not a one-stop lakehouse platform
Pricing tiers
partial- Galaxy FreeLimited cluster; community support$0 /mo
- Galaxy StandardPay-as-you-go cluster pricing; usage-basedQuote
- Galaxy EnterpriseAdvanced governance, SSO, dedicated supportQuote
- Starburst Enterprise (self-hosted)Subscription; on-prem or BYOCQuote
Key features
- +Managed Trino (SaaS Galaxy and self-hosted Enterprise)
- +Multi-format support: Iceberg, Delta, Hudi
- +Stargate federation across 50+ sources
- +Caching and acceleration layer
- +Role-based access control and data products
- +Bring-your-own-cloud model
Apache Hudi + Onehouse
Streaming-first open table format from Uber, with Onehouse as commercial managed offering.
Apache Hudi is the open table format originated at Uber in 2016-2017 and donated to the Apache Software Foundation, designed from day one for streaming-first and record-update-heavy workloads (CDC, real-time ingestion, frequent upserts). Onehouse is the commercial managed offering founded by Hudi creator Vinoth Chandar in 2021, with a multi-format strategy (Hudi, Iceberg, Delta via Apache XTable). The honest framing: Hudi has lost the broader open-table-format war to Iceberg on hyperscaler buy-in, but retains a defensible niche in streaming-first and CDC-heavy workloads where its incremental processing model is genuinely differentiating. Best fit for Uber-origin shops and streaming-heavy data engineering teams.
Streaming-first data engineering teams (50-50,000 employees) with heavy CDC, frequent upserts, or real-time ingestion requirements where Hudi incremental processing is differentiating.
Batch-heavy analytics shops (Iceberg or Delta fit better), or teams wanting broadest hyperscaler-native support without operational engineering work.
Strengths
- Streaming-first and CDC-heavy workload specialization
- Record-level updates and deletes natively supported
- Onehouse managed offering with Hudi creator on engineering team
- Apache XTable for cross-format (Hudi/Iceberg/Delta) interop
- Used in production at Uber, Walmart, Robinhood, Notion
Weaknesses
- Lost broader format war to Iceberg on hyperscaler buy-in
- Smaller ecosystem and contributor base than Iceberg or Delta
- Best-fit narrowed to streaming/CDC workloads
Pricing tiers
partial- Apache HudiApache 2.0; unlimited use; community support$0 /mo
- Onehouse FreeCommunity tier; limited capacity$0 /mo
- Onehouse CloudManaged Hudi + multi-format; usage-basedQuote
- Onehouse EnterpriseDedicated support, enterprise governanceQuote
Key features
- +Streaming-first incremental processing
- +Record-level updates (Copy-on-Write and Merge-on-Read)
- +Time travel and snapshot isolation
- +Apache XTable for cross-format interop
- +Native Spark, Flink, Presto, Trino support
- +Onehouse managed cloud
Frequently asked questions
The questions buyers actually ask before they sign.
Which lakehouse vendors hold IRAP PROTECTED assessment for Australian Commonwealth and Defence workloads?
How does APP 8 cross-border disclosure affect lakehouse vendor selection in Australia?
Should an Australian fintech evaluate Snowflake or Databricks as the primary lakehouse?
Lakehouse vs data warehouse: is there a real architectural difference in 2026?
Apache Iceberg vs Delta Lake vs Apache Hudi: which open table format should we pick?
Does the Databricks-Tabular acquisition (Jun 2024) hurt Iceberg neutrality?
Is Snowflake genuinely going open with the Polaris Catalog OSS pivot?
When should we choose a lakehouse over a warehouse-only architecture?
What is the cost reality of a lakehouse at petabyte scale?
Which Iceberg catalog should we choose: Polaris, Unity Catalog, AWS Glue, or Nessie?
How does query engine + storage separation work in practice?
Final word
Looking at a different market? See the global Data Lakehouse ranking, or pick another country at the top of this page.
Last updated 2026-05-27. Local pricing reverified quarterly. Found something inaccurate? Tell us.