---
title: "Snowflake vs BigQuery: Pricing, Architecture, Marketing Fit"
canonical: "https://www.owox.com/blog/articles/snowflake-vs-bigquery-comparison"
updated: "2026-10-13"
---

# Snowflake vs BigQuery: A detailed comparison

Snowflake bills for the time a warehouse runs; BigQuery bills for the bytes a query reads, or for slots. That one difference explains most of the others: who has to manage compute, how dashboards behave under load, and how you model your tables. For a marketing team on GA4 and Google Ads, BigQuery is the shorter path. Snowflake fits when marketing shares a warehouse with finance and product on AWS or Azure.

[Google BigQuery](/blog/topics/bigquery) · Updated October 13, 2026 · [Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) · 15 min read

![Snowflake vs BigQuery: A detailed comparison](https://cdn.owox.com/www/content-marketing-article/snowflake-vs-bigquery-comparison/thumbnail/2-zzuy2p.png/public)

This is a detailed comparison of [Snowflake](/blog/articles/snowflake-data-warehouse-explained) and BigQuery: how each one is built, how it bills you, how it behaves under dashboards and batch jobs, how it controls access, and which one fits a marketing team that works with GA4 and ad-platform data.

Both are elastic cloud data warehouses that speak SQL, so on a feature list they overlap almost entirely. The differences show up in daily work: who has to manage compute, what a busy dashboard costs, and how much modeling stands between raw data and a report. The goal here is not to crown a winner but to show which trade-offs match your stack and your team.

**_Note:_** _Originally published in March 2026. Updated in October 2026 with a section on marketing analytics workloads; list prices and product limits were checked against Snowflake and Google Cloud documentation in October 2026._

## How to think about choosing between Snowflake and BigQuery

Most teams comparing the two already have data in the cloud, several reporting tools, and pressure to deliver trusted numbers without losing control of cost. Four dimensions decide the [warehouse choice](/blog/articles/choose-right-data-warehouse) more reliably than any feature matrix: **architecture, cost, performance and governance**. If terms like partitions or storage versus compute are new, start with the [basic warehousing concepts](/blog/articles/database-data-warehouse-data-lake).

| Dimension | Snowflake | BigQuery |
| --- | --- | --- |
| **Cloud strategy** | Runs on AWS, Azure and Google Cloud | A Google Cloud service |
| **Compute model** | Virtual warehouses you size and schedule | Serverless; slots allocated per query |
| **Compute billing** | Credits per second of warehouse runtime | Per TiB scanned, or per slot-hour |
| **Idle cost risk** | A warehouse left running | A query that scans a large table unfiltered |
| **Governance anchor** | Role hierarchy inside Snowflake | Google Cloud IAM |
| **GA4 raw data** | Arrives through BigQuery first | Native export |
| **Best fit** | Teams that want explicit control over compute | Teams that want no compute to manage |

## Architecture deep dive: comparing Snowflake and BigQuery

Both platforms separate storage from compute, but they hand you very different controls. That difference decides how you isolate workloads, absorb concurrency spikes and plug the warehouse into the rest of your cloud.

### Core architectural models: storage, compute, and separation

**Snowflake** stores data in compressed micro-partitions on cloud object storage and runs queries on **virtual warehouses**: compute clusters you create, size from X-Small to 6X-Large, and set to suspend when idle. Several warehouses can read the same tables, so finance can keep a small warehouse for reports while data engineering runs a large one for nightly builds, and neither slows the other. The price of that control is that someone has to design and maintain the warehouse layout.

**BigQuery** stores data in Google-managed columnar storage and runs queries on **slots**, units of compute that Google allocates. There are no clusters to size. On the on-demand model you simply send SQL; on the capacity model you buy slots through [BigQuery editions](https://docs.cloud.google.com/bigquery/docs/editions-intro) and assign them to reservations. You manage query design and reservations, not machines.

### Concurrency, isolation, and workload management

Concurrency is where the two philosophies become visible to end users.

In Snowflake you route workloads to **separate warehouses** (one for BI, one for ELT, one for data science) and scale each on its own. A [multi-cluster warehouse](https://docs.snowflake.com/en/user-guide/warehouses-multicluster) goes further: when queries start to queue, Snowflake starts additional clusters up to the maximum you set and shuts them down as load drops. Multi-cluster warehouses require Enterprise Edition or higher.

BigQuery decides concurrency itself. It [works out how many queries can run at once](https://docs.cloud.google.com/bigquery/docs/query-queues) from the compute available to the project or reservation and queues the rest, up to 1,000 interactive queries per project per region. On-demand projects generally have access to up to 2,000 concurrent slots. To protect one workload from another, you put them in different projects or reservations.

So a slow dashboard is fixed differently on each side. In Snowflake you resize the BI warehouse or let it scale out. In BigQuery you tune the queries, the table design or the reservation.

### Ecosystem alignment and integration with cloud services

BigQuery is part of Google Cloud. Access runs through Google Cloud IAM, and the [BigQuery Data Transfer Service](https://docs.cloud.google.com/bigquery/docs/dts-introduction) loads Google Ads, Campaign Manager, Display & Video 360, Search Ads 360, Google Ad Manager and YouTube data on a schedule. GA4 exports its raw events to BigQuery natively. If your company already runs on Google Cloud, there is little left to integrate.

Snowflake is **cloud-agnostic**: you choose AWS, Azure or Google Cloud and a region, and the SQL layer stays the same. [Secure Data Sharing](https://docs.snowflake.com/en/user-guide/data-sharing-intro) lets another Snowflake account query your data without copying it; a direct share works within one region, and sharing across regions or clouds goes through a listing with auto-fulfillment. If the company is multi-cloud, or its identity and BI tools live in Azure or AWS, Snowflake fits that strategy better.

## The Turning Point

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## Pricing and cost management in Snowflake and BigQuery

Either warehouse can be cheap or surprisingly expensive. They bill for different things, so they reward different habits.

### How Snowflake pricing works: warehouses, credits, and storage

A virtual warehouse consumes **credits** while it runs. [Each size up doubles the rate](https://docs.snowflake.com/en/user-guide/warehouses-overview): X-Small uses 1 credit per hour, Small 2, Medium 4, Large 8, up to 512 for 6X-Large. Billing is per second, with a 60\-second minimum each time the warehouse starts. The price of a credit depends on your edition, cloud and region. A suspended warehouse consumes no credits, and auto-suspend and auto-resume are on by default.

Two consequences follow. A small warehouse that never suspends can cost more than a large one that runs briefly. And every wake-up bills at least a minute, so many scattered queries cost more than the same work done in one batch.

Storage is billed on the compressed size of your tables, [including history kept for Time Travel and Fail-safe](https://docs.snowflake.com/en/user-guide/cost-understanding-data-storage). Time Travel retention is up to 1 day on Standard Edition and up to 90 days on Enterprise; Fail-safe keeps permanent tables for a further 7 days. Our guide to [Snowflake pricing and cost control](/blog/articles/snowflake-pricing-cost-control) goes deeper.

### How BigQuery pricing works: on-demand, editions, and storage

BigQuery offers [two compute models](https://cloud.google.com/bigquery/pricing):

*   **On-demand** bills the bytes each query processes: $6.25 per TiB, with the first 1 TiB each month free. Runtime does not matter; the amount of data read does.
*   **Capacity** bills slot-hours through three editions: Standard, Enterprise and Enterprise Plus. Slots autoscale, and Enterprise and Enterprise Plus offer one-year and three-year commitments at a discount. The older flat-rate model has not been offered since July 5, 2023.

A query answered from the [result cache](https://docs.cloud.google.com/bigquery/docs/cached-results) is not billed. Storage is billed separately: a table or partition that has not changed for 90 consecutive days drops to the long-term rate, roughly half the active one, and the first 10 GiB each month is free. See our [BigQuery pricing guide](/blog/articles/bigquery-pricing) for the full breakdown.

### Cost control strategies and typical cost pitfalls

**In Snowflake**, cost control is warehouse discipline:

*   Keep auto-suspend short, and start with a small warehouse; scale up only for workloads that prove they need it.
*   Separate ELT, BI and ad hoc work so light users do not run on a large warehouse.
*   Set a [resource monitor](https://docs.snowflake.com/en/user-guide/resource-monitors) with a credit quota. It can notify you, or suspend the warehouses it watches, when the quota is reached.

The usual pitfalls are a warehouse left running for sporadic work, analysts exploring raw tables on the warehouse sized for transformations, and long Time Travel retention nobody reviewed.

**In BigQuery**, cost control is query and table discipline:

*   Select only the columns you need and filter on the partition column. On a table that is not clustered, [`LIMIT` does not reduce the data read](https://docs.cloud.google.com/bigquery/docs/best-practices-costs).
*   [Partition](/blog/articles/bigquery-partitioned-tables) and cluster tables on the fields people filter by.
*   Preview a query’s bytes with a dry run, cap a query with the maximum bytes billed setting, and set custom daily quotas per project or per user.

The usual pitfalls are self-service users querying large unpartitioned tables, and buying slot capacity before measuring the baseline.

### How the pricing model shapes your data model

Because each warehouse charges for something different, the same reporting need leads to different modeling decisions.

**On BigQuery you model to reduce bytes read.** On-demand cost follows the data a query touches, so the model favours date-partitioned fact tables, clustering on the fields used in filters, and compact pre-aggregated tables for dashboards, so that a refresh reads megabytes instead of the full event history.

**On Snowflake you model to reduce warehouse time.** Cost follows how long a warehouse is awake, so the model favours incremental builds over full rebuilds, heavy joins done once in a scheduled batch, and refreshes grouped together so the warehouse wakes a few times a day rather than once per query.

The two meet in the same place: a small set of curated, business-ready tables that reports read, with raw data kept behind them. That is cheaper on both platforms, because the expensive work happens once.

## Performance behavior and workload patterns

Both warehouses are fast at scale. What differs is which lever you pull when something is slow.

### Query execution models, caching, and scaling

In Snowflake every query runs on a specific warehouse. Repeated queries over the same data benefit from that warehouse’s cache, and an identical query can be answered from [persisted results](https://docs.snowflake.com/en/user-guide/querying-persisted-results), which are kept for 24 hours as long as the underlying data has not changed. You scale **up** (a larger warehouse) for heavier queries or **out** (multi-cluster) for more users.

In BigQuery a query is spread across slots automatically. Cached results are also kept for about 24 hours, and performance depends mostly on **schema design and query shape**: partitioning, clustering and pruning decide how much data the engine has to read.

### Performance for ad hoc analytics, dashboards, and batch workloads

*   **High-concurrency BI dashboards.** When many business users open dashboards at the same hour, Snowflake’s answer is a dedicated BI warehouse, multi-cluster if the edition allows it, so the surge never touches ELT. BigQuery absorbs the surge without provisioning; on capacity pricing you protect BI with its own reservation.
*   **Occasional very large ad hoc queries.** BigQuery on-demand suits a team that rarely scans years of events: nothing to provision, and you pay for that scan only. In Snowflake you resize a warehouse or keep a larger one for such jobs.
*   **Predictable nightly batch.** Snowflake runs it on a large warehouse that suspends when the job ends. BigQuery runs it on-demand or on a reservation sized for the job.
*   **Self-service access to raw tables.** This hurts on both. In Snowflake inefficient queries saturate a shared warehouse; in BigQuery they multiply bytes scanned. The fix is the same: point people at curated tables, not raw ones.

## Snowflake vs BigQuery for marketing analytics

Marketing workloads have a recognisable shape: one large event source (GA4), a handful of ad-platform feeds that are small by comparison, and dashboards that many people refresh during the working day. Here is how the two warehouses handle each part.

### GA4 export: native in BigQuery, a second hop to Snowflake

GA4 exports raw events to BigQuery directly. The [export is available to standard and 360 properties](https://support.google.com/analytics/answer/9358801); standard properties have a daily export limit of 1 million events. A streaming export is also available and adds a BigQuery charge of $0.05 per gigabyte. Events arrive in daily `events_YYYYMMDD` tables with nested fields such as `event_params`. Our [GA4 to BigQuery guide](/blog/articles/google-analytics-4-to-bigquery-guide) covers the setup.

Snowflake cannot receive that export directly. Snowflake’s own [Connector for Google Analytics Raw Data](https://docs.snowflake.com/en/connectors/google/gard/gard-connector-about) reads the export from BigQuery and copies it into your Snowflake account, so a GA4-to-BigQuery link and a Google Cloud project are prerequisites either way. You are then running two systems for GA4 data, and the hop between them is yours to monitor.

### Ad-platform data

On BigQuery, Google’s own sources come through the Data Transfer Service, which also lists Facebook Ads. On Snowflake, the [Openflow connectors](https://docs.snowflake.com/en/user-guide/data-integration/openflow/connectors/about-openflow-connectors) include Google Ads, Meta Ads, LinkedIn Ads and Amazon Ads. Neither vendor covers every platform, so most teams add a connector tool for the rest.

OWOX Data Marts includes [data connectors](/product/data-connectors) for ad platforms, among them Google Ads, Facebook Ads, LinkedIn Ads, Microsoft Ads, TikTok Ads, Reddit Ads and X Ads, that load into the warehouse you already use. See the walkthroughs for [Google Ads to BigQuery](/blog/articles/google-ads-google-bigquery) and [Google Ads to Snowflake](/blog/articles/google-ads-data-snowflake-owox).

Wherever the data lands, pulling it is the easy part. The work is giving campaigns, channels and dates one naming and one grain across platforms, so that spend can be joined to sessions and revenue.

### Marketing attribution with Snowflake or BigQuery

The layering is the same on both: **raw events, sessions, conversions, attribution**. What differs is the first step.

On BigQuery you start from the GA4 export as it is. Event parameters are a repeated record, so you [flatten them with `UNNEST`](/blog/articles/unnest-ga4-event-parameters-in-bigquery), build sessions with window functions over `event_timestamp` per `user_pseudo_id`, and store a sessions table partitioned by date. An unscoped `UNNEST` or a missing date filter is what turns a cheap query into an expensive one.

On Snowflake the same events sit in a [`VARIANT` column](https://docs.snowflake.com/en/user-guide/querying-semistructured). You read nested fields with path syntax, expand arrays with `LATERAL FLATTEN`, and usually materialise a clean events table first. That is more modeling up front and simpler SQL afterwards. The heavy attribution job can then run on its own warehouse, away from dashboards.

Past that point the tables look the same on both: ad cost by campaign and day, sessions by traffic source, conversions, and attribution credit per touchpoint. Our [marketing analytics data model](/blog/articles/marketing-analytics-data-model) article walks through that structure.

### What a marketing-size workload costs

List prices make the two bills easy to reason about. The figures below are arithmetic on the vendors’ published rates, not measurements of a real account.

**BigQuery on-demand.** At $6.25 per TiB, a dashboard query that scans 5 GiB costs about three cents, and the free 1 TiB a month covers roughly 200 such scans. The same dashboard built on a pre-aggregated table that scans 50 MiB per refresh costs about a hundredth of that. The risk is the opposite case: a report pointed at raw GA4 tables with no date filter reads the whole history on every refresh.

**Snowflake.** An X-Small warehouse uses 1 credit per hour. If dashboard traffic keeps it awake for six hours of a working day, that is 6 credits a day whether it served ten queries or ten thousand. If it wakes for a single query and suspends, the 60\-second minimum applies each time. The risk here is auto-suspend set too long, or dashboards sharing a warehouse sized for nightly jobs.

For a team whose main cost is many light dashboard queries, bytes-scanned billing with well-built tables is hard to beat. For a team whose main cost is a few heavy scheduled jobs, a warehouse that runs briefly and suspends is predictable and easy to cap.

### Who runs it

This often decides the question before price does.

*   **A marketing team with one analyst and no data engineer** is better served by BigQuery on-demand: there is no compute to size, GA4 arrives on its own, and the main skill needed is writing filtered SQL.
*   **A company where finance, product and marketing share one warehouse** on AWS or Azure, with engineers who already run Snowflake, should keep marketing there. One more warehouse and one more role is less work than a second platform.
*   **A team on Snowflake that depends on GA4** should budget for the BigQuery hop: someone owns the export, the connector and the cost on both sides.

## Governance, security, and self-service analytics

A warehouse also has to decide who can see what, and leave a record of what was done. Both do, anchored in different places.

### Roles, permissions, and access controls in Snowflake and BigQuery

**Snowflake is role-centric, inside the platform.** Users get roles; roles own objects and hold privileges on databases, schemas, tables, views and warehouses. Grants are explicit, which is good for audits and takes disciplined role design to keep tidy.

**BigQuery is IAM-driven.** Permissions are granted through [Google Cloud IAM roles](/blog/articles/bigquery-iam-roles-and-permissions) at the organisation, project, dataset or table level, so data access follows the same policies as the rest of your cloud.

Both are secure. The question is whether your security team would rather drive policy from inside the warehouse or from the cloud provider’s IAM.

### Data quality, lineage, and auditability considerations

| Dimension | Snowflake | BigQuery |
| --- | --- | --- |
| **Governance anchor** | Role hierarchy inside the warehouse | Cloud IAM at organisation and project level |
| **Fine-grained security** | Masking policies and row access policies (Enterprise Edition) | Column-level access controls, row-level access controls and data masking |
| **Audit** | Account usage views | Cloud audit logs |
| **Self-service risk** | Role sprawl | IAM complexity |

The feature lists are in each vendor’s documentation: [Snowflake editions](https://docs.snowflake.com/en/user-guide/intro-editions) and [BigQuery data governance](https://docs.cloud.google.com/bigquery/docs/data-governance).

Neither warehouse governs **meaning**. Access control stops the wrong person from reading a table; it does not stop two teams from computing “revenue” or “active user” in two ways. [Data quality](/product/data-quality) and shared definitions are handled above the raw tables, in the layer where metrics are written down once.

## Decision scenarios: mapping platforms to your context

The better choice depends less on benchmarks than on your size, skills and cloud strategy.

### Scenarios by company size, team skills, and cloud commitments

*   **Smaller teams.** With few analysts and little engineering capacity, BigQuery is usually the smoother path if you already use GA4 and Google Ads. Snowflake makes sense early if your customers or your infrastructure require another cloud.
*   **Mid-sized organizations.** With analytics engineers and several BI tools, Snowflake is attractive for explicit compute control and workload isolation. BigQuery works well when the data platform is already on Google Cloud.
*   **Enterprises and regulated environments.** A multi-cloud mandate favours Snowflake’s cloud-agnostic model and data sharing. A company standardised on Google Cloud gets consistent policy from BigQuery’s IAM model.

### Checklist of questions to narrow down your choice objectively

*   Which cloud is strategic for the next few years, and where do identity and BI tools live?
*   Is GA4 your main behavioural source?
*   Is the workload mostly dashboards and light ad hoc queries, or heavy scheduled processing?
*   Is query volume steady enough for committed capacity, or spiky?
*   Who will size, schedule and monitor compute, and do you want that job to exist?
*   How many tools will read from the warehouse, and where will metric definitions live?

## Where OWOX Data Marts fit in

Neither warehouse is a self-service reporting tool on its own. On both, teams run into the same problems: metric definitions that differ between dashboards, analysts rebuilding similar queries, and business users running expensive queries on raw tables.

OWOX Data Marts runs on your warehouse; Snowflake and BigQuery are both [supported Data Storages](https://docs.owox.com/docs/storages/supported-storages/snowflake/). An analyst defines each Data Mart with SQL, or from an existing table or view, and connects Data Marts into a [data model](/product/data-modeling): the entities of the business and how they join. Reports then deliver those Data Marts to where people work, such as [Google Sheets](/product/spreadsheets) or Data Studio (formerly Looker Studio). An AI assistant such as Claude or ChatGPT can query the same published Data Marts through [OWOX MCP](/features/mcp); OWOX builds the SQL over the joins the analyst defined and runs it in your warehouse, and the result rows go to the assistant that asked.

If you were looking for a semantic layer on top of Snowflake or BigQuery, this is a different approach: the definitions live in a data model that exists before anyone asks a question. Our comparison of a [semantic layer and Data Marts](/blog/articles/semantic-layer-vs-data-marts) explains the difference.

It also makes a later change of warehouse less painful. Your SQL still runs in one warehouse’s dialect, but reports and spreadsheets read Data Marts, not raw tables, so the logic sits in one place you can change, not in dozens of dashboards.

## Your New Normal

Turn your data into decisions.

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## FAQ

## Frequently Asked Questions

What factors should teams consider when choosing between Snowflake and BigQuery?

Teams should evaluate cloud strategy (single vs multi-cloud), workload types (BI dashboards vs heavy batch), cost predictability vs flexibility, governance model preferences (warehouse role model vs cloud IAM), team skills, and integration with existing tools. Snowflake offers more explicit compute control and is suited for multi-cloud or complex governance, while BigQuery provides a hands-off serverless experience tightly integrated with Google Cloud services.

How do Snowflake and BigQuery perform with different workload patterns like ad hoc queries, dashboards, and batch transformations?

Snowflake excels in repeated, similar queries with effective warehouse-level data caching and explicit scaling for heavy batch jobs. It is especially predictable for dashboard concurrency with multi-cluster warehouses. BigQuery performs strongly on massive, exploratory queries with on-demand parallel execution and cached results, and handles large batch jobs well on reserved slot capacity bought through [BigQuery editions](https://docs.cloud.google.com/bigquery/docs/editions-intro). However, query optimization and partitioning are critical to avoid high costs and latency.

Which platform is better suited for multi-cloud strategies or organizations needing cloud flexibility?

Snowflake is cloud-agnostic and runs on AWS, Azure, and Google Cloud, enabling multi-cloud strategies and migrations without changing analytical layers. It supports cross-cloud data sharing and collaboration regardless of the underlying cloud provider, making it a strong choice for organizations seeking cloud flexibility and avoiding vendor lock-in.

What role does a governed data mart layer play on top of Snowflake or BigQuery?

OWOX Data Marts provides a governed, reusable analytics layer above Snowflake or BigQuery, standardizing business metrics, publishing curated domain-specific data marts, and exposing controlled self-service interfaces. This reduces metric discrepancies, limits expensive raw queries, simplifies governance, and helps control costs by decoupling business logic from the underlying warehouse.

How do Snowflake and BigQuery support governance, security, and self-service analytics?

Snowflake uses a role-centric access control model within the platform, managing privileges explicitly on objects with hierarchical roles, facilitating least-privilege enforcement and auditability. BigQuery leverages Google Cloud IAM for permissions across projects, datasets, and tables, integrating with GCP’s centralized security. Both platforms support data lineage, audit logging, and fine-grained security features, but governance is often enhanced via semantic layers and curated data marts.

Which platform offers better concurrency and workload isolation for BI dashboards and analytics?

Snowflake provides multi-cluster virtual warehouses that auto-scale to handle concurrency spikes and allows dedicated warehouses per workload, giving fine-grained isolation and predictable performance. BigQuery handles concurrency automatically with slot-based resource allocation and fair scheduling, but can experience queuing or throttling without well-designed reservations, which may affect performance during heavy workloads.

How does pricing differ between Snowflake and BigQuery for typical workloads?

Snowflake pricing is based on compute credits consumed by running virtual warehouses and storage costs charged for compressed data at rest, plus time travel. You pay per second of warehouse runtime based on size. BigQuery pricing charges on-demand per byte processed by queries and storage charges for active and long-term data; for predictable workloads, capacity pricing bills slot-hours through the Standard, Enterprise and Enterprise Plus editions. The older flat-rate model has not been offered since July 5, 2023, according to [Google's BigQuery pricing page](https://cloud.google.com/bigquery/pricing).

What are the main architectural differences between Snowflake and BigQuery?

Snowflake uses virtual warehouses as independent compute clusters on top of shared cloud object storage, allowing explicit control over compute resources and workload isolation. BigQuery uses a serverless, slot-based execution model over fully managed Google distributed storage with automatic scaling and resource allocation managed by Google, focusing on query design and capacity reservations instead of cluster sizing.

## Who wrote this

![Ievgen Krasovytskyi](https://cdn.owox.com/www/webflow/68404586b341508a789a4aa5_.png/public)

[Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) Head of Marketing

Ievgen Krasovytskyi is the Head of Marketing at OWOX, leading strategy across content, SEO, product marketing, and AI-powered automation. With deep expertise in analytics infrastructure, data warehouses, and marketing technology, he builds systems that connect marketing performance to business outcomes. Ievgen writes about SaaS growth, analytics workflows, and the future of AI in marketing operations.

[LinkedIn](https://www.linkedin.com/in/ievgen-krasovytskyi-a38a1253/) · [All articles](/team/ievgen-krasovytskyi)

[Google BigQuery](/blog/topics/bigquery)

## Related articles

[Snowflake · How to Turn Snowflake into a Self-Service Marketing Analytics Engine · October 1, 2026](/blog/articles/snowflake-self-service-marketing-analytics)

[Snowflake · Snowflake for Data Analysts: How to Turn Your Warehouse into a Reusable Reporting Layer · October 1, 2026](/blog/articles/snowflake-reusable-reporting-layer)

[Snowflake · Automate Snowflake Reports in Google Sheets with OWOX Data Marts · October 1, 2026](/blog/articles/automate-snowflake-google-sheets)

[See all articles →](/blog/articles)

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## Links

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- [Google BigQuery](https://www.owox.com/blog/topics/bigquery) — /blog/topics/bigquery.md
- [Ievgen Krasovytskyi](https://www.owox.com/team/ievgen-krasovytskyi) — /team/ievgen-krasovytskyi.md
- [Snowflake](https://www.owox.com/blog/articles/snowflake-data-warehouse-explained) — /blog/articles/snowflake-data-warehouse-explained.md
- [warehouse choice](https://www.owox.com/blog/articles/choose-right-data-warehouse) — /blog/articles/choose-right-data-warehouse.md
- [basic warehousing concepts](https://www.owox.com/blog/articles/database-data-warehouse-data-lake) — /blog/articles/database-data-warehouse-data-lake.md
- [Book a Demo](https://www.owox.com/book-a-call) — /book-a-call.md
- [Snowflake pricing and cost control](https://www.owox.com/blog/articles/snowflake-pricing-cost-control) — /blog/articles/snowflake-pricing-cost-control.md
- [BigQuery pricing guide](https://www.owox.com/blog/articles/bigquery-pricing) — /blog/articles/bigquery-pricing.md
- [Partition](https://www.owox.com/blog/articles/bigquery-partitioned-tables) — /blog/articles/bigquery-partitioned-tables.md
- [GA4 to BigQuery guide](https://www.owox.com/blog/articles/google-analytics-4-to-bigquery-guide) — /blog/articles/google-analytics-4-to-bigquery-guide.md
- [data connectors](https://www.owox.com/product/data-connectors) — /product/data-connectors.md
- [Google Ads to BigQuery](https://www.owox.com/blog/articles/google-ads-google-bigquery) — /blog/articles/google-ads-google-bigquery.md
- [Google Ads to Snowflake](https://www.owox.com/blog/articles/google-ads-data-snowflake-owox) — /blog/articles/google-ads-data-snowflake-owox.md
- [flatten them with UNNEST](https://www.owox.com/blog/articles/unnest-ga4-event-parameters-in-bigquery) — /blog/articles/unnest-ga4-event-parameters-in-bigquery.md
- [marketing analytics data model](https://www.owox.com/blog/articles/marketing-analytics-data-model) — /blog/articles/marketing-analytics-data-model.md
- [Google Cloud IAM roles](https://www.owox.com/blog/articles/bigquery-iam-roles-and-permissions) — /blog/articles/bigquery-iam-roles-and-permissions.md
- [Data quality](https://www.owox.com/product/data-quality) — /product/data-quality.md
- [supported Data Storages](https://docs.owox.com/docs/storages/supported-storages/snowflake)
- [data model](https://www.owox.com/product/data-modeling) — /product/data-modeling.md
- [Google Sheets](https://www.owox.com/product/spreadsheets) — /product/spreadsheets.md
- [OWOX MCP](https://www.owox.com/features/mcp) — /features/mcp.md
- [semantic layer and Data Marts](https://www.owox.com/blog/articles/semantic-layer-vs-data-marts) — /blog/articles/semantic-layer-vs-data-marts.md
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- [Snowflake · How to Turn Snowflake into a Self-Service Marketing Analytics Engine · October 1, 2026](https://www.owox.com/blog/articles/snowflake-self-service-marketing-analytics) — /blog/articles/snowflake-self-service-marketing-analytics.md
- [Snowflake · Snowflake for Data Analysts: How to Turn Your Warehouse into a Reusable Reporting Layer…](https://www.owox.com/blog/articles/snowflake-reusable-reporting-layer) — /blog/articles/snowflake-reusable-reporting-layer.md
- [Snowflake · Automate Snowflake Reports in Google Sheets with OWOX Data Marts · October 1, 2026](https://www.owox.com/blog/articles/automate-snowflake-google-sheets) — /blog/articles/automate-snowflake-google-sheets.md
- [See all articles →](https://www.owox.com/blog/articles) — /blog/articles.md