TLDR: Snowflake wins on multi-cloud flexibility and workload isolation. BigQuery wins on serverless simplicity and GCP-native analytics. Azure Synapse wins when your organization is already on Microsoft and needs a unified analytics workspace. The right call comes down to your existing cloud, your team’s skills, and whether you need portability across cloud providers.
Key Takeaways Snowflake is best for enterprises running multi-cloud environments, or those needing clean compute-storage separation with fine-grained workload control across teams. Google BigQuery suits teams on GCP that want zero infrastructure overhead, pay-per-query pricing at $6.25/TB, and built-in ML capabilities via BigQuery ML. Azure Synapse Analytics fits organizations deep in the Microsoft stack — Power BI, Azure Data Factory , Azure Machine Learning — where a unified analytics workspace reduces tool sprawl. Choosing the wrong platform costs real money — not just in licensing, but in migration, re-engineering, and lost engineering bandwidth. Why This Decision Is Harder Than It Looks Snowflake, Google BigQuery, and Azure Synapse are all genuinely capable platforms. They’re also commonly mismatched to the organizations that buy them — because benchmark results and vendor documentation don’t tell you which one fits your existing infrastructure, your team’s actual skills, or the tools you’re already using downstream.
Picking the wrong platform doesn’t mean the implementation fails on day one. It usually means a slow accumulation of friction: re-engineering pipelines, fighting integration gaps, watching query costs climb because the billing model doesn’t suit your workload. Getting it right upfront is significantly cheaper than getting it right after 12 months of production use.
This comparison covers the things that actually determine the decision: architecture, how each platform bills, performance behavior at scale, ecosystem fit, security posture , and the operational realities vendor docs leave out.
One note on scope: this article covers Snowflake, BigQuery, and Azure Synapse. If your organization is AWS-native, Amazon Redshift belongs in your evaluation — that’s a different comparison. For AWS-first teams, the more relevant question is Redshift vs Snowflake, and the tradeoffs look quite different from what’s covered here.
Platform Overviews: What Each One Actually Is Snowflake: Cloud-Agnostic, Workload-Isolated Snowflake is a cloud-native data platform that runs on AWS, Azure, and GCP. Its defining architectural feature is a three-layer model — independent storage, compute, and cloud services layers — that scale separately. You pay for compute via credits billed per second, and storage at a flat rate per terabyte.
What sets Snowflake apart in enterprise environments isn’t raw query speed. It’s workload isolation: multiple teams or applications can run simultaneously on separate virtual warehouses without contention. A marketing analytics job doesn’t slow down a finance query. For large organizations with many internal consumers of data, that isolation matters a lot in practice.
Snowflake’s Data Cloud also lets organizations share live data across company boundaries without copying or moving it — a supplier gives a retailer direct access to inventory data without building an ETL pipeline. Useful for firms that regularly exchange data with partners, vendors, or subsidiaries.
Google BigQuery: Serverless and GCP-Native BigQuery is Google’s fully managed, serverless data warehouse. It runs entirely on GCP. You don’t provision clusters, size nodes, or manage infrastructure. Write a query, BigQuery allocates what it needs, and you pay for what you used — or a flat rate if your volume is predictable.
It runs on Google’s Dremel engine, the same technology Google uses internally for large-scale analytics. Structured and semi-structured data are both supported natively. Real-time streaming ingestion is built in. BigQuery ML lets teams train and run machine learning models in standard SQL without exporting data anywhere.
The tradeoff is control. Workload isolation is limited compared to Snowflake. And on-demand pricing at $6.25/TB means one poorly optimized query against a large table can cost more than expected.
Azure Synapse Analytics: Unified Microsoft Workspace Azure Synapse Analytics Microsoft’s unified analytics platform. It combines traditional data warehousing via dedicated SQL pools (MPP architecture) with Apache Spark for big data processing, plus built-in data integration pipelines that overlap with Azure Data Factory — all in one workspace.
It’s not a pure data warehouse. It’s more of a full analytics platform : one environment covering ingestion, transformation, warehousing, and serving. For organizations already on Azure, using Power BI, with teams that know T-SQL, Synapse reduces the number of separate tools to manage.
The downside is complexity. Synapse does many things, which means more surface area to set up correctly. Teams that just need a fast, focused SQL analytics engine often find it overbuilt.
Architecture Deep Dive The table below summarizes the core architectural differences. The commentary after each row explains what it means in practice.
Dimension Snowflake Google BigQuery Azure Synapse Architecture Multi-cluster shared data Serverless, fully managed MPP (dedicated) + serverless pools Compute/storage separation Yes — fully independent Yes — auto-managed Yes — manual or serverless Cloud availability AWS, Azure, GCP (native) GCP only Azure only Workload isolation Virtual warehouses (strong) Limited Workload management pools Multi-cloud portability Native No (BigQuery Omni extends queries) No Open table format support Apache Iceberg (Open Catalog) Iceberg, BigLake Delta Lake , Parquet Streaming ingestion Snowpipe (additional cost) Native, included Event Hubs / Spark Streaming ML integration Snowflake Cortex, Snowpark BigQuery ML, Vertex AI Azure Machine Learning
What this means in practice: If you want zero infrastructure management and your team is already on GCP, BigQuery removes the most friction. If you’re consolidating from a fragmented Azure toolset, Synapse’s unified workspace means fewer tools to learn and maintain. Microsoft Fabric Vs Databricks: A Comparison Guide Explore key differences between Microsoft Fabric and Databricks in pricing, features, and capabilities.
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Pricing: How the Bill Actually Works This is where enterprises consistently get surprised. The three platforms bill in fundamentally different ways, and the right choice depends on your workload shape.
Snowflake Pricing Snowflake charges for compute via credits and storage separately, billed per second with a 60-second minimum per warehouse session.
Standard Edition: ~$2/credit on-demand (US AWS regions) Enterprise Edition: ~$3/credit on-demand (the most common tier for mid-to-large enterprises) Storage: ~$23/TB/month (US regions, on-demand) Warehouse sizes: X-Small (1 credit/hour) through 6X-Large (512 credits/hour) — each size doubles the credit consumption and compute capacity The key cost risk: warehouses that aren’t auto-suspended keep burning credits even when idle. A Medium warehouse left running idle costs 4 credits/hour — at $3/credit Enterprise pricing, that’s $12/hour for nothing. Auto-suspend configuration is not optional; it’s the single biggest cost control lever Snowflake gives you.
Snowflake pricing rewards deliberate warehouse architecture. Organizations that right-size virtual warehouses and configure auto-suspend properly see predictable bills. Those that don’t see bills that grow much faster than their actual workload justifies.
Google BigQuery Pricing BigQuery uses two fundamentally different billing models:
On-demand pricing: $6.25/TB of data scanned per query (first 1TB/month free). The cost is based on data processed , not time or compute. Capacity pricing (BigQuery Editions): Reserved slots (virtual CPUs) billed per slot-hour. Standard, Enterprise, and Enterprise Plus tiers with autoscaling options and optional 1- or 3-year commitments for discounted rates. Storage: $0.02/GB/month (active, modified in last 90 days), $0.01/GB/month (long-term, unchanged 90+ days) The on-demand model rewards query optimization. A full-table scan on a 20TB dataset costs $125. The same query with proper partitioning and clustering might scan 500GB and cost $3.13. That gap is entirely in your control — but requires your data engineers and analysts to understand BigQuery’s cost model and write queries accordingly.
For steady, high-volume workloads, capacity pricing usually becomes more economical. For bursty, ad-hoc, or exploratory workloads, on-demand is often cheaper.
Azure Synapse Pricing Synapse has two distinct resource models with different pricing:
Dedicated SQL pools: Billed via Data Warehouse Units (DWUs). Pricing ranges from approximately $1.20–$1.51/DWU/hour depending on tier. Storage is billed separately. These pools must be paused when not in use, or they continue billing — similar to Snowflake’s idle warehouse problem. Serverless SQL pools: $5/TB of data processed — similar mechanics to BigQuery’s on-demand model. Synapse’s pricing complexity increases when you use multiple pool types simultaneously. Organizations running Spark pools for transformation, dedicated pools for BI serving, and serverless pools for ad-hoc exploration have three separate cost streams to monitor and optimize.
Pricing Model Comparison This table shows how each platform’s pricing behavior maps to common workload patterns. Use it as a starting point — model your actual top queries and ingestion volumes before committing.
Workload Pattern Snowflake BigQuery Azure Synapse Sporadic, unpredictable queries Medium (credits idle if not suspended) Low (pay per query at $6.25/TB) Medium–High (DWUs idle if not paused) High-volume, predictable BI reporting Low–Medium (capacity pricing or Enterprise) Low (Editions capacity pricing) Low (committed DWUs, predictable) Multi-team concurrent analytics Low (isolated virtual warehouses) Medium (slot contention possible) Medium (workload management pools required) Storage-heavy, compute-light archiving Low–Medium Low ($0.01/GB long-term storage) Low Real-time streaming ingestion Additional (Snowpipe) Included (native streaming) Additional (Event Hubs / Spark) ML model training on warehouse data Requires Snowpark / Cortex Included (BigQuery ML) Azure ML (separate service)
Note: Actual costs vary by region, contract tier, and workload specifics. Always model real query and ingestion patterns before comparing platform costs.
Real Cost Example: A Mid-Size Analytics Team at 50TB To make the pricing models concrete, here’s how costs play out for a representative workload: a 50TB data warehouse, 30 analysts running queries throughout the business day, roughly 500 queries/day of mixed complexity, and 5TB of new data ingested monthly.
Cost Component Snowflake (Enterprise) Google BigQuery Azure Synapse (Dedicated) Compute ~$4,320/month (Medium warehouse, 8hr/day, auto-suspend) ~$1,875/month (on-demand, avg 300GB scanned/query × 500 queries) ~$2,880/month (DW500c, 8hr/day paused nights/weekends) Storage (50TB) ~$1,150/month ~$1,000/month (active) ~$1,100/month Streaming ingestion (5TB/month) ~$125/month (Snowpipe) Included ~$200/month (Event Hubs) Estimated monthly total ~$5,600 ~$3,875 ~$4,180
A few things this table illustrates that the per-unit pricing numbers hide:
BigQuery looks cheapest here — but only if analysts write efficient queries with partitioning and clustering. If 10 analysts run poorly-optimized full-table scans against the full 50TB daily, that $1,875 compute line becomes $18,750. The on-demand model is the most cost-sensitive to query quality of the three.
Snowflake’s estimate assumes proper auto-suspend configuration. Without it — warehouse left running 24/7 — that $4,320 compute line roughly triples to $12,960. Auto-suspend is Snowflake’s equivalent of turning off the lights.
Azure Synapse’s estimate assumes DWUs are paused outside business hours. For teams with 24/7 reporting requirements or global users across time zones, that assumption breaks and costs increase significantly.
The honest framing: run your actual top 20 queries against each platform’s cost calculator before making a final decision. Synthetic estimates are a starting point, not a verdict.
Performance: Where Each Platform Actually Shines All three use columnar storage and massively parallel processing. For most analytical workloads on reasonably sized data, they perform comparably. The differences emerge at the edges — and in how the platform behaves as scale and concurrency increase.
Snowflake delivers consistent performance across concurrent workloads. Because each virtual warehouse is isolated, 20 teams can run jobs simultaneously without query contention. Auto-scaling within warehouses handles burst demand. Performance is predictable and tunable.
BigQuery excels at large-scale ad-hoc queries over very large datasets. Its serverless architecture means a query over 50TB runs the same way as one over 500GB — you don’t resize clusters or estimate slots upfront. It’s the strongest of the three for streaming analytics when paired with GCP services like Pub/Sub or Dataflow.
Azure Synapse dedicated pools perform well for structured warehousing workloads — traditional star schema, dimension/fact table queries, and predictable BI reporting traffic. Synapse serverless pools are better suited for exploratory queries over data lake files rather than production BI serving.
The Performance Factor Most Benchmarks Miss Query optimization effort. BigQuery’s on-demand pricing makes query costs immediately visible in billing — which forces engineering discipline around partitioning, clustering, and avoiding full-table scans. Snowflake’s credit model can mask expensive queries behind aggregate billing. Synapse’s DWU model can make poorly designed schemas costly in ways that are harder to trace.
The platform that generates the lowest bill for a given workload is partly a function of your team’s willingness and ability to optimize queries for that platform’s billing model.
What Independent Benchmarks Show Benchmarks should be taken with a grain of salt — configuration choices, query complexity, and cluster sizing all affect results, and vendors have been known to sponsor benchmarks that favor their platforms. That said, a few independent tests provide useful directional data.
Fivetran’s 2022 TPC-DS benchmark (99 complex queries, 1TB scale, run by Brooklyn Data Co.) found that all five major platforms — Snowflake, BigQuery, Redshift, Synapse, and Databricks — delivered “excellent execution speed, suitable for ad hoc, interactive querying.” The headline finding: the performance gap between platforms has narrowed significantly over the past few years. Choosing based purely on benchmark speed is no longer a strong basis for the decision.
Where Fivetran’s results are more useful is in cost-per-query efficiency. BigQuery’s on-demand model creates high variability — efficient queries are very cheap, unoptimized ones are expensive. Snowflake’s per-second credit billing rewards queries that finish quickly. Synapse dedicated pools are most cost-efficient when utilization is consistently high (billing continues whether you query or not).
GigaOm’s TPC-DS test on 30TB (an earlier, Microsoft-sponsored benchmark) found Azure SQL Data Warehouse (Synapse’s predecessor) delivering competitive price-performance on structured star-schema queries — the workload type it’s explicitly built for. Snowflake outperformed BigQuery significantly on complex join-heavy queries in that dataset. BigQuery’s serverless model, however, means cluster sizing decisions don’t affect it the way they do the others.
The practical takeaway from the benchmark literature: workload type matters more than platform identity. Snowflake handles high-concurrency mixed workloads well. BigQuery handles massive ad-hoc scans efficiently when queries are optimized. Synapse dedicated pools perform predictably for consistent, structured BI traffic. Run your own representative queries on each platform’s free tier before drawing conclusions.
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Ecosystem and Integration For most enterprises, this is the deciding factor — not the platform’s raw capabilities, but how it connects with everything already in use.
Snowflake Ecosystem Snowflake integrates with nearly every major data tool in the modern stack. Informatica, Talend, dbt, Fivetran, Airbyte, Tableau, Power BI, Looker, and hundreds of others have native Snowflake connectors. Because Snowflake is cloud-agnostic, it functions as a neutral hub in multi-cloud environments where other platforms are locked to a specific provider.
The Snowflake Marketplace allows organizations to access third-party datasets — financial market data, location intelligence, demographic benchmarks — directly within Snowflake without ETL pipelines . For firms augmenting internal analytics with external data, this reduces integration complexity.
BigQuery Ecosystem BigQuery’s integration story is deepest within GCP. Vertex AI for model training and deployment, Looker for BI, Google Analytics 4 for web analytics, Pub/Sub for event streaming, Cloud Composer for orchestration — all connect to BigQuery with minimal configuration. For teams already standardized on GCP, this coherent stack reduces the number of integration handoffs and reduces data egress costs.
BigQuery Omni extends BigQuery’s query engine to data stored on AWS S3 and Azure Blob Storage — a useful bridge for organizations with multi-cloud data storage who want to centralize analytics without moving all data to GCP.
Azure Synapse Ecosystem Synapse’s strongest integration story is within the Microsoft ecosystem. Power BI connects natively. Azure Data Factory pipelines run inside Synapse itself. Azure Machine Learning and Azure Purview for data governance operate in the same environment. For organizations using Microsoft 365, Dynamics 365, or running significant Azure workloads, Synapse reduces integration overhead and simplifies licensing.
This is also where Kanerika’s Microsoft Solutions Partner credential for Data & AI becomes relevant for clients — deep Synapse implementations are often paired with Microsoft Fabric modernization roadmaps, which build on Synapse’s foundations while moving toward OneLake’s unified storage model.
If your BI layer is Power BI, your cloud is Azure , and your team knows T-SQL — Synapse is almost certainly your lowest-friction path.
Security and Compliance All three platforms are enterprise-grade on security. The differences are in default configurations and compliance breadth.
Feature Snowflake BigQuery Azure Synapse Encryption at rest Yes (AES-256, default) Yes (AES-256, default) Yes (TDE, default) Encryption in transit TLS (default) TLS (default) TLS (default) Row/column-level security Yes (native) Yes (column-level, IAM integration) Yes (T-SQL GRANT, row-level security) Customer-managed encryption keys Yes (Business Critical+) Yes Yes Network isolation Private Link, VPC Service Controls VPC Service Controls, Private Service Connect Azure Private Link, VNet integration Compliance certifications SOC 2, HIPAA, GDPR, PCI DSS SOC 2, HIPAA, GDPR, FedRAMP High SOC 2, HIPAA, GDPR, FedRAMP High, ISO 27001, DoD IL2 Data governance tooling Native + Informatica, Collibra Google Dataplex, BigQuery data catalog Microsoft Purview (native, deep integration) Multi-region failover Business Critical+ tier Multi-region configuration Built-in geo-redundancy on Azure
Azure Synapse’s compliance certification list is broader, partly because Microsoft has made deep investments in government and defense sector compliance. For FedRAMP High, DoD, or ITAR-regulated workloads, Synapse has the most mature compliance posture of the three. BigQuery’s FedRAMP High authorization covers it for most federal workloads. Snowflake’s government availability varies by cloud region and requires confirming specific certifications for the region you deploy in.
For data governance, Microsoft Purview’s native, deep integration with Synapse is a meaningful advantage for organizations that take data lineage, sensitivity labels, and unified governance seriously — particularly in regulated industries like financial services , healthcare, and insurance. Kanerika implements Microsoft Purview alongside Synapse for clients in these verticals.
1. Choose Snowflake when: Your organization runs workloads across multiple clouds (AWS + Azure, or AWS + GCP) and needs a single data platform that spans them Multiple business units or teams need isolated compute — different SLAs, different performance budgets, no contention You exchange data with external partners, subsidiaries, or data vendors and want live sharing without ETL pipelines Your ETL stack includes Informatica , Talend , or other enterprise integration tools that need a cloud-neutral landing zone You’re evaluating or building on dbt and want strong compatibility across transformation layers 2. Choose Google BigQuery when: Your primary cloud is GCP, or you’re willing to standardize on it Workloads are bursty and unpredictable — on-demand pay-per-query pricing works in your favor Your team wants zero infrastructure management and your engineers prefer SQL over cluster configuration 3. Choose Azure Synapse when: You’re deeply invested in the Microsoft Azure ecosystem and want to reduce tool sprawl Power BI is your primary BI layer and you want native, one-click connectivity You’re already using Azure Data Factory, Azure ML, or Microsoft Purview and want one workspace for all of it Industry Fit: Where Each Platform Tends to Land The “right” platform often correlates with industry as much as it does with technical requirements. Here’s how the decision typically breaks down, based on implementation patterns across enterprise clients:
Industry Common Choice Why Watch Out For Financial services Snowflake or Synapse Multi-entity data sharing (Snowflake) or existing Microsoft stack + compliance (Synapse) BigQuery’s FedRAMP High covers federal; Synapse has strongest FINRA/SEC audit trail depth Healthcare / Life Sciences Synapse or Snowflake HIPAA BAAs available from all three; Synapse wins when Epic or Cerner integration is needed via Azure BigQuery works for research orgs on GCP; Synapse best for hospital systems on Microsoft Retail / E-commerce BigQuery or Snowflake BigQuery for Google Analytics + GA4 + real-time ad analytics; Snowflake for multi-brand data sharing Synapse is viable but Power BI-centric retailers often find Fabric a better next step Manufacturing Synapse or Snowflake Synapse when Microsoft ERP (Dynamics) is central; Snowflake for cross-plant, multi-cloud consolidation SAP-centric manufacturers should assess SAP BW → Synapse compatibility early Technology / SaaS BigQuery or Snowflake BigQuery for event-driven product analytics on GCP; Snowflake for multi-cloud product data sharing Avoid over-engineering for scale you don’t yet have — BigQuery’s on-demand model penalizes early-stage misuse Public Sector / Government Synapse or BigQuery Synapse for FedRAMP High, DoD IL2, existing Microsoft agreements; BigQuery for FedRAMP-moderate Snowflake government cloud availability varies by region — verify before committing
These are patterns, not prescriptions. A healthcare organization running primarily on GCP with a data science-heavy team looks different from a hospital system on Azure standardized on Power BI. Use the industry column as a starting filter, then validate against your actual stack.
Decision Framework: Four Questions Before You Choose Before talking to any vendor, answer these four questions honestly. They’ll narrow the field faster than any benchmark.
1. What’s your primary cloud — and do you need portability? If your organization is all-in on Azure, Synapse is the path of least resistance. All-in on GCP? BigQuery. If you’re multi-cloud, or if cloud neutrality is a strategic priority, Snowflake is the only one of these three that operates natively across all three hyperscalers.
2. How predictable and consistent are your query workloads? Bursty, variable, exploratory → BigQuery on-demand pricing works in your favor. Steady, high-volume, predictable BI traffic → Synapse dedicated pools or Snowflake capacity pricing tend to cost less. Mixed, with multiple teams running different workloads simultaneously → Snowflake’s virtual warehouse model gives you the most control over cost and performance across that diversity.
3. How many teams or applications share the same data platform? Single team, sequential workloads → any of the three work. Multiple teams with different latency requirements and no tolerance for query contention → Snowflake’s isolated virtual warehouses are the right architecture. This is one of the most underweighted criteria in typical evaluations.
4. What does your BI, integration, and governance stack look like? Power BI-first organizations → Synapse or Snowflake (both integrate well). Looker-first → BigQuery. Informatica or Talend for ETL → Snowflake is the most natural landing zone. Microsoft Purview for governance → Synapse has the deepest native integration. If your answers point in different directions, weight the BI and governance tools more heavily — those are the downstream consumers that your users interact with daily.
Data Warehouse Migration: A Practical Guide for Enterprise Teams Learn key strategies , tools and best practices for successful data-warehouse migration.
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Common Migration Patterns We See : Snowflake vs Google BigQuery vs Azure Synapse: Understanding why organizations switch platforms — and what that transition actually involves — is often more useful than any feature comparison . Here are the migration patterns Kanerika sees most frequently.
Legacy on-premises → Snowflake. The most common pattern for organizations exiting Teradata, Netezza, or SQL Server environments who want cloud flexibility without committing to a single hyperscaler. The typical driver is multi-cloud strategy or the need to share data across business units on different clouds. Snowflake’s T-SQL compatibility reduces SQL rewrite effort, but ETL layer migration (typically from Informatica or SSIS) is where complexity concentrates. Realistic timeline: 6–12 months for mid-size environments.
Oracle / SQL Server → Azure Synapse. The natural path for Microsoft-stack organizations. T-SQL compatibility is high, Azure Data Factory can replace SSIS pipelines, and Power BI connections require minimal reconfiguration. The common pitfall: underestimating the effort to migrate proprietary Oracle PL/SQL logic or SQL Server stored procedures. Kanerika’s FLIP accelerator handles the Synapse-to-Fabric migration that often follows once Synapse is stable. Timeline: 4–9 months for well-scoped environments.
Hadoop / on-premises GCP → BigQuery. Organizations exiting aging Hadoop clusters or consolidating multi-system GCP environments. BigQuery Migration Service handles SQL conversion from HiveQL and other dialects. The Dremel engine’s performance advantage is most visible for teams coming from Hive — queries that took hours start taking seconds. Timeline: 3–8 months, shorter for GCP-native teams.
Redshift → Snowflake. A growing pattern as organizations adopt multi-cloud strategies or outgrow Redshift’s AWS lock-in. The workload isolation and Data Cloud sharing capabilities are typically the pull factors. SQL rewrite effort is moderate; ETL re-pointing to Snowflake-native connections is the primary time investment. Timeline: 4–9 months.
Synapse → Microsoft Fabric. Less a migration than an evolution — Fabric’s OneLake architecture subsumes Synapse’s storage model. Organizations on Synapse today are typically on a 12–24 month roadmap toward Fabric. The transition is incremental rather than a hard cutover. For teams evaluating Synapse now, Microsoft Fabric should be part of the roadmap conversation from day one.
In every migration, the technical work is only part of the effort. Gartner research has found that 60% of data warehouse migrations exceed their planned timelines, and the primary cause is almost always organizational rather than technical — downstream system re-pointing, stakeholder alignment, and data quality remediation that wasn’t scoped adequately upfront.
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How Kanerika Approaches Cloud Data Warehouse Selection Kanerika works with enterprise organizations across all three platforms. As a Microsoft Solutions Partner for Data & AI with active Snowflake and Databricks practices, the starting point for any platform selection is the actual state of a client’s infrastructure, team skills, and analytics roadmap — not a preferred vendor.
A few patterns show up consistently:
Organizations already on Azure with Power BI as the primary BI layer typically land on Synapse, usually paired with a Microsoft Fabric migration roadmap to unify storage on OneLake and cut data movement costs. Kanerika’s FLIP accelerator automates large portions of the Synapse-to-Fabric migration, compressing timelines that would otherwise stretch to a year or more.
Multi-cloud or cloud-neutral enterprises, particularly those sharing data across business units or with external partners, tend toward Snowflake for its workload isolation and Marketplace capabilities. These engagements often run alongside Informatica or Talend migration projects where the integration layer is being updated at the same time as the warehouse.
GCP-native or ML-heavy organizations find BigQuery’s native ML and Vertex AI integration reduces the number of separate tools they need. For these engagements, Kanerika focuses on query optimization frameworks and governance setup rather than the platform selection itself.
The decision always involves what sits upstream — ETL tools like Informatica or Azure Data Factory, transformation layers like dbt — and downstream: BI platforms, data science workbenches, operational systems. A warehouse that’s technically excellent in isolation but creates friction in the surrounding stack costs more over time than a slightly less capable one that fits cleanly.
FAQs What is the main difference between Snowflake, Google BigQuery, and Azure Synapse? Snowflake is a cloud-agnostic data warehouse that runs on AWS, Azure, and GCP, with strong workload isolation via virtual warehouses. Google BigQuery is a fully serverless, managed data warehouse exclusive to GCP, built for zero-infrastructure analytics. Azure Synapse Analytics is a unified analytics workspace that combines data warehousing, Apache Spark, and data integration within the Microsoft Azure ecosystem.
Which cloud data warehouse is cheapest for enterprise analytics? Cost depends entirely on your workload. BigQuery’s on-demand pricing at $6.25/TB scanned works well for bursty, sporadic workloads. Synapse dedicated pools tend to offer predictable costs for steady, high-volume workloads when DWU commitments are right-sized. Snowflake at $2–3/credit (Standard to Enterprise edition) offers the most cost control when virtual warehouses are properly configured with auto-suspend. Model your actual query volume, concurrency, and storage before comparing.
Can Snowflake run on Azure and Google Cloud? Yes. Snowflake runs natively on AWS, Azure, and GCP. You choose the cloud region during account setup. BigQuery is locked to GCP, though BigQuery Omni lets you query data stored on AWS S3 and Azure Blob Storage. Azure Synapse is locked to Azure.
Is Azure Synapse being replaced by Microsoft Fabric? Microsoft has positioned Microsoft Fabric as the next-generation evolution of Synapse, with OneLake as the unified storage layer replacing the fragmented storage model in Synapse. Azure Synapse Analytics remains supported and widely deployed. Organizations evaluating a new Microsoft data platform should determine whether to start on Synapse or go directly to Fabric, depending on their Microsoft contract, team readiness, and migration timeline.
Which platform is best for machine learning workloads? Google BigQuery, via BigQuery ML and Vertex AI integration, has the strongest native ML story for GCP-native teams. Snowflake has expanded into ML through Snowflake Cortex and Snowpark Python. Azure Synapse integrates with Azure Machine Learning for enterprise ML pipelines. For organizations where ML is a primary, large-scale workload, Databricks is often the stronger choice compared to any of the three — see Databricks vs Snowflake vs Microsoft Fabric for that comparison.
How long does it take to migrate to a new cloud data warehouse? It varies considerably. A well-scoped SQL Server-to-Synapse migration for a mid-size organization takes roughly 3–6 months. A more complex migration involving legacy ETL tools, downstream BI re-pointing, and data quality remediation can take 12–18 months. Kanerika’s FLIP accelerator significantly reduces the Synapse/ADF-to-Fabric portion of migrations. See enterprise cloud data migration best practices for a detailed breakdown.
What is Snowflake's Data Cloud? Snowflake’s Data Cloud is an ecosystem that lets organizations share live data across company boundaries without copying or moving it. A supplier can give a retailer direct access to inventory data in Snowflake without building a data integration pipeline. It supports data monetization, cross-organizational analytics, and partner data sharing — capabilities that are harder to replicate natively in BigQuery or Synapse.
Does Azure Synapse support Apache Spark? Yes. Azure Synapse includes native Apache Spark pools. Data engineers and data scientists can run Spark workloads in the same environment they use for SQL-based warehousing, which reduces the need for separate Databricks clusters in organizations with simpler ML and transformation requirements. For more complex ML and data science workloads, Databricks often provides more capability — see Azure Synapse vs Databricks for details.
How does BigQuery handle workload management and concurrency? On-demand BigQuery provides up to 2,000 concurrent slots per project, shared across all queries. When demand exceeds available slots, queries queue. Unlike Snowflake’s isolated virtual warehouses, there’s no per-team or per-application isolation — a large query from one analyst can consume slots that affect response times for another. BigQuery Editions with reserved slots and autoscaling improve this, but workload isolation remains stronger in Snowflake’s virtual warehouse model.
