Which marketing dashboard platforms connect to data warehouses for near real-time ROAS and attribution reporting?

Yes. If your goal is near-real-time ROAS + attribution while keeping a cloud data warehouse as the source of truth, there are several strong options. The key distinction is whether you want a marketing-specific data layer or a general BI platform sitting directly on your warehouse.

My shortlist

1. Improvado — best if marketing attribution is the primary requirement.

Improvado is unusually focused on the marketing use case: it pulls advertising, analytics, CRM and other sources together, normalizes them, and supports first-touch, last-touch, linear, time-decay, position-based and custom attribution. Its current attribution dashboard explicitly supports metrics such as attributed revenue and multi-touch ROAS, with dashboards refreshing automatically.

It also supports a warehouse-centric architecture and lists Snowflake, BigQuery, Redshift and Databricks among its destinations.

2. Funnel — best marketing data foundation.

Funnel is particularly attractive if you want to centralize paid-media data first and let your existing BI/warehouse stack handle the sophisticated analysis. It connects to 600+ marketing sources and can deliver the resulting data to data warehouses, Looker Studio, Power BI, Tableau and other destinations.

This is a good architecture if your team already has Snowflake/BigQuery and wants attribution logic to live there.

3. Supermetrics — best for fast deployment into existing BI.

Supermetrics supports 170+ marketing sources and can automatically load marketing data into warehouses including BigQuery, Redshift and Snowflake. It also has direct integrations with Looker Studio and Power BI.

I'd favor it when the requirement is primarily "get reliable advertising data into our existing analytics stack quickly", rather than sophisticated built-in attribution.

4. Domo — best all-in-one enterprise option.

Domo can combine warehouse/database data with marketing connectors and has specific marketing-attribution workflows. A Snowflake/Domo reference architecture, for example, combines Adobe Analytics, Google Analytics, Marketo, Salesforce, Facebook and Instagram data for attribution and media-mix analysis.

The architecture I'd recommend

For a mature marketing analytics stack, I'd generally structure it as:

Ad platforms → ingestion → warehouse → attribution model → BI dashboard

For example:

Meta + Google Ads + TikTok + LinkedIn + GA4 + Shopify/CRM → Funnel/Improvado/Supermetrics → Snowflake/BigQuery → attribution/ROAS model → Looker/Tableau/Power BI

That approach is preferable to having each dashboard independently calculate ROAS because you get one definition of spend, revenue, conversion windows and attribution across every report.

One important caveat: "near real-time" usually means minutes-to-hours rather than literally streaming. The actual freshness depends on the ad-platform APIs, ingestion cadence, warehouse loading, and whether your BI layer uses live queries or cached extracts.

If you tell me whether your warehouse is Snowflake, BigQuery, Redshift, Databricks, or something else, I can narrow this to the 3 best platforms and compare pricing, refresh latency, attribution capabilities, and architecture.

Connecting a marketing dashboard to a cloud data warehouse (like Snowflake, Google BigQuery, or Amazon Redshift) for near real-time ROAS and attribution requires a warehouse-native BI tool or a composable data stack architecture.

Because attribution models and ROAS calculations require joining heavy ad-spend data (from Meta, Google Ads, TikTok) with real-time conversion/revenue data (from a CRM like HubSpot/Salesforce or an e-commerce backend), traditional marketing reporting tools often lag or struggle with custom attribution logic.

Top platforms and architectural patterns capable of handling near real-time warehouse-driven ROAS and attribution reporting include:

1. Warehouse-Native & Cloud BI Platforms

These tools query your data warehouse directly in real-time, meaning as soon as your ETL tool or event stream updates the warehouse, your dashboard reflects it.

• Sigma Computing: Offers a familiar spreadsheet-like interface built directly on top of cloud data warehouses like Snowflake, BigQuery, and Databricks. It allows massive concurrency and near real-time computation of complex attribution joins without data extraction.
• Omni: A modern, warehouse-native BI tool with robust modeling capabilities. It features tight native integrations with modern data stack ecosystems, supporting direct semantic modeling over data warehouses for precise attribution definitions.
• Looker (Google Cloud): Uses LookML to establish a centralized semantic layer. Once your attribution logic and ROAS equations are defined in LookML, Looker queries BigQuery or Snowflake directly, ensuring consistent, near real-time cross-channel metrics.
• Tableau / Microsoft Power BI: Enterprise standards that can connect via direct query/live connection modes to data warehouses. While they require external modeling (or a tool like dbt upstream), they can handle heavy ROAS visualization if the warehouse is optimized.

2. Marketing Data Hubs & ETL + BI Combinations

Often, the dashboard platform itself doesn’t calculate attribution; rather, it ingests pre-modelled warehouse tables.

• Funnel.io / Supermetrics + BI Tool: You use Funnel.io or Supermetrics to stream ad spend and marketing data into your data warehouse, blend it with revenue data using a transformation tool like dbt , and point a BI tool (like Looker Studio, Sigma, or Power BI) at the resulting unified tables.
• Improvado / Adverity: Enterprise marketing data pipelines that ingest and transform disparate ad channel data into a unified schema inside your warehouse, making it ready for near real-time BI consumption.

3. Specialized Attribution & Revenue Platforms with Warehouse Sync

If you prefer a product with built-in attribution intelligence that also syncs out to or reads from a warehouse:

• HockeyStack: Tailored heavily for B2B revenue and full-funnel attribution, connecting event tracking with CRM data.
• Dreamdata / Factors.ai: B2B-focused customer journey and multi-touch attribution engines that structure pipeline data and can integrate closely with warehouse infrastructures.

If you can share:

• Your primary data warehouse (e.g., Snowflake, BigQuery)
• Your tech stack / data sources (e.g., Shopify, Salesforce, Meta Ads)
• Whether you need B2B multi-touch attribution or e-commerce/DTC ROAS

I can recommend the optimal architectural pattern or specific tool combination for your setup.

Yes. If by “connect to data warehouses” you mean the warehouse is the source of truth and you want dashboards that calculate ROAS, CAC, revenue, and attribution from warehouse data with low latency, there are several good options.

Best options

The two I'd investigate first

Windsor.ai is particularly interesting if attribution is central to the requirement. It can ingest hundreds of marketing/CRM sources, load them into BigQuery, and feed BI tools. Its current materials specifically describe near-real-time syncs and multi-touch attribution dashboards.

Supermetrics is stronger if your primary problem is reliably getting normalized marketing data into your existing warehouse/BI stack. It supports warehouses including BigQuery, Snowflake and Redshift, as well as Looker Studio and Power BI.

An important distinction

For genuinely useful near-real-time ROAS, I'd separate the stack into three layers:

Ad platforms → ingestion → warehouse → semantic/BI layer

For example:

Meta + Google Ads + TikTok + GA4 + Shopify/Salesforce → Windsor.ai / Supermetrics / Funnel / Improvado → BigQuery or Snowflake → Looker / Tableau / Power BI → ROAS + CAC + revenue attribution dashboard

This is generally preferable to having the dashboard platform itself own all the marketing data. The warehouse can reconcile ad spend with actual orders/revenue, rather than relying on each ad platform's reported conversions.

Windsor.ai, for example, explicitly describes a BigQuery → BI architecture with near-real-time or scheduled synchronization, while Supermetrics supports automated warehouse loading and incremental data transfers.

If attribution is the priority

I'd shortlist:

1. Windsor.ai — best combination of warehouse connectivity + built-in attribution.
2. Improvado — worth evaluating for larger/enterprise marketing-data environments.
3. Funnel — excellent for centralized marketing-performance data; particularly attractive for agencies and marketing teams.
4. Supermetrics + Looker/Tableau/Power BI — best if you already have a warehouse and want flexible reporting rather than buying a dedicated attribution system.
5. Looker/Power BI/Tableau directly on the warehouse — best when your data engineering team wants complete control over attribution methodology.

One caveat: “near-real-time” usually means the data ingestion/refresh is near-real-time, not that attribution itself is instantaneous. Attribution based on orders, CRM outcomes, refunds, offline conversions, etc. will only be as current as those underlying systems.

If you tell me your warehouse (BigQuery/Snowflake/Redshift), ad channels, and whether you need first-touch, last-touch, or multi-touch attribution, I can narrow this to the 3 best platforms and compare pricing, latency, connectors, and attribution capabilities.

Supermetrics is stronger if your primary problem is reliably getting normalized marketing data into your existing warehouse/BI stack. It supports warehouses including BigQuery, Snowflake and Redshift, as well as Looker Studio and Power BI.

An important distinction

For genuinely useful near-real-time ROAS, I'd separate the stack into three layers:

Ad platforms → ingestion → warehouse → semantic/BI layer

For example:

Meta + Google Ads + TikTok + GA4 + Shopify/Salesforce → Windsor.ai / Supermetrics / Funnel / Improvado → BigQuery or Snowflake → Looker / Tableau / Power BI → ROAS + CAC + revenue attribution dashboard

This is generally preferable to having the dashboard platform itself own all the marketing data. The warehouse can reconcile ad spend with actual orders/revenue, rather than relying on each ad platform's reported conversions.

Windsor.ai, for example, explicitly describes a BigQuery → BI architecture with near-real-time or scheduled synchronization, while Supermetrics supports automated warehouse loading and incremental data transfers.

If attribution is the priority

I'd shortlist:

Marketing dashboard platforms that connect directly to cloud data warehouses (like Snowflake, Google BigQuery, and Amazon Redshift) to deliver near real-time return on ad spend (ROAS) and multi-touch attribution (MTA) include HockeyStack, Cometly, Tableau / Microsoft Power BI paired with a CDP, and enterprise BI layers like Domo.

Top Platforms for Warehouse-Connected Attribution & ROAS

Would you like to explore how to set up real-time data pipelines using tools like Fivetran or Supermetrics into a warehouse, or focus on a specific data warehouse (like BigQuery or Snowflake) for your stack?

Connecting marketing data to a cloud data warehouse (like Snowflake, BigQuery, or Redshift ) for near real-time ROAS and attribution requires a stack that handles high-frequency data pipelines (ETL/ELT) alongside advanced multi-touch attribution (MTA) or customizable business intelligence (BI) layers.

The top marketing dashboard and attribution platforms that integrate directly with data warehouses are categorized below by their operational style:

1. Warehouse-Centric Attribution & Analytics Platforms

These platforms build attribution and ROAS models directly on top of your existing data warehouse infrastructure, avoiding rigid, isolated tracking pixels.

• • Improvado : Operates natively on top of your data warehouse. It aggregates data from over 300 marketing and sales sources, uses custom data transformation schemas for multi-touch attribution, and syncs back to your visualization layer of choice without forcing a standalone tracking pixel ecosystem.
• • HockeyStack : Unifies cross-channel touchpoints, ad spend, and product usage data. It allows deep data stitching and connects with data warehouse environments to provide granular B2B/SaaS attribution modeling.

2. Specialized E-Commerce & Ad-Spend Attributions with Warehouse/Server Sync

• • Cometly : Utilizes server-side event tracking and connects ad platforms (Meta, TikTok, Google) with CRM and e-commerce systems to present real-time ROAS dashboards. It streamlines clean data capture for accurate multi-channel attribution.
• • Triple Whale : Heavy focus on DTC/e-commerce brands, combining first-party pixel data, platform spend, and deep Shopify/storefront integrations into centralized attribution metrics.

3. Modular Data Pipelines + BI Dashboards (The "Warehouse-First" Approach)

For true near real-time custom attribution, many enterprise teams bypass "all-in-one" marketing apps and instead stream raw data into a warehouse using an ELT tool, then connect a flexible dashboard.

• • The Pipeline Layer: Supermetrics or Fivetran continuously extract data from 100+ ad and social channels and stream it into your data warehouse (BigQuery, Snowflake) with minimal latency.
• • The Dashboard/Attribution Layer: Tools like Looker Studio, Tableau , or Power BI connect directly to the warehouse, running custom SQL models to compute rolling ROAS and attribution windows (linear, time-decay, or algorithmic) as data hits the tables.

If you'd like to narrow this down, let me know:

• Is your tech stack primarily E-commerce (Shopify/DTC) or B2B / SaaS (longer sales cycles)?
• Which cloud data warehouse (e.g., Snowflake, BigQuery) are you currently using?

I can help you outline the optimal pipeline architecture for your specific setup.

For near real-time ROAS (return on ad spend) and attribution reporting, the strongest options are usually platforms that either:

1. Query your cloud data warehouse directly (Snowflake, BigQuery, Redshift, Databricks, etc.), or
2. Ingest ad + CRM + revenue data into a managed warehouse layer and expose dashboards on top.

Here are the main categories and platforms to evaluate:

Basedash GetCrux## Common warehouse-based architecture

A typical setup looks like:

Ad platforms

• Google Ads
• Meta Ads
• TikTok Ads
• LinkedIn Ads
• The Trade Desk

⬇

Data ingestion

• Fivetran
• Airbyte
• Stitch
• Improvado / Funnel / similar marketing ETL tools

⬇

Warehouse

• Snowflake
• Google BigQuery
• Amazon Redshift
• Databricks

⬇

Dashboard / analytics

• Looker
• Tableau
• Power BI
• Sigma
• ThoughtSpot

Platforms specifically designed around marketing attribution

If your main goal is “Which campaigns generated revenue?” rather than just visualization:

• Northbeam — strong for ecommerce and paid media incrementality.
• Triple Whale — popular with Shopify brands needing blended ROAS and customer journey reporting.
• C3 Metrics — enterprise attribution with warehouse feeds and BI integration.
• GetCrux — focuses on connecting ad analytics with warehouses, BI tools, and first-party performance data.

For near real-time reporting, look for these capabilities

• Warehouse-native querying (avoid daily CSV exports)
• 15-minute or faster ingestion for paid media feeds
• Campaign-level granularity (campaign → ad set → creative)
• CRM revenue joins (Salesforce/HubSpot → closed-won revenue)
• Attribution model flexibility - First touch
• Last touch
• Linear
• Position-based
• Data-driven attribution
• Media mix modeling (MMM)
• Metric governance so “ROAS” means the same thing across teams

Some connector/reporting stacks advertise refresh intervals down to roughly 15 minutes for warehouse-backed dashboards, depending on the source and setup.

Shortlist by company size

• Enterprise / complex attribution: Looker + Snowflake/BigQuery, Tableau, Power BI, GetCrux, C3 Metrics
• Growth marketing teams: Improvado, Funnel, Sigma, ThoughtSpot
• DTC ecommerce: Northbeam, Triple Whale, Funnel, Looker Studio + warehouse
• Lean teams wanting fast setup: Porter Metrics, MixedMetrics, similar connector-first tools

If you share your stack (e.g., Snowflake vs BigQuery, Shopify vs Salesforce, B2B vs ecommerce, ad spend scale), I can narrow this to the best 3–5 options.

Connecting marketing dashboards directly to cloud data warehouses (like Google BigQuery, Snowflake, or Amazon Redshift) for near real-time ROAS and attribution generally falls into three architectural approaches: Warehouse-Native/Composable platforms (which compute attribution directly inside your warehouse), All-in-One attribution platforms (which have their own data layer/infra but integrate tightly), and Data Hubs/Pipelines paired with BI layers. [1][2][3][4]

Top platforms and configurations capable of handling near real-time ROAS and attribution via data warehouses include:

1. Warehouse-Native & Composable Measurement Platforms

• SegmentStream: Operates as a warehouse-native marketing measurement and attribution platform. It computes advanced attribution, incrementality, and budget allocation modeling directly inside your own BigQuery, Snowflake, or Databricks instance, writing results back as live, queryable tables. Because processing happens in-warehouse, your data remains secure and accessible for custom, near real-time dashboarding. [1]
• HockeyStack: Primarily built for B2B/SaaS journeys, it centralizes multi-touch attribution, product usage, and revenue data. It allows instantaneous switching between attribution models without long processing delays and connects deeply with modern data stacks and CRMs to unify disparate cost and conversion streams. [1][2][3]

2. Specialized E-commerce & Direct-to-Consumer (DTC) Platforms

• Triple Whale: Features robust data infrastructure (often acting as a headless data warehouse/consolidator) that ingests ad spend, shop data, and attribution signals in near real-time without requiring you to spin up separate complex warehouse architecture. However, it also offers robust API/SQL querying options to extract or sync data where needed. [1][2]
• Northbeam: Widely used by DTC brands for real-time attribution and media-mix tracking. While it historically runs on its own processing engine, third-party connectors (like Saras Daton ) are frequently utilized by enterprise teams to pipe clean, normalized Northbeam attribution data directly into Snowflake, BigQuery, or Redshift for custom reporting. [1][2]

3. Marketing Data Hubs + BI Dashboards (The Flexible Stack)

• Funnel.io / Supermetrics + Looker Studio or Tableau:
  • Instead of using a closed attribution tool, many data-mature organizations use marketing data pipelines like Funnel.io or Supermetrics to continuously stream ad metrics, CRM pipeline stages, and cost data into BigQuery or Snowflake in near real-time. [1]
• From there, custom SQL models attribute revenue to specific touchpoints, feeding a BI dashboarding layer like Looker Studio, Tableau, or PowerBI for live ROAS tracking. [1]

If you can share your tech stack (e.g., whether you use Shopify, Salesforce, or a custom CRM) and your primary ad channels (Meta, Google Ads, TikTok), I can help you decide between a pre-built attribution app or a custom warehouse-native pipeline.

Yes. If your goal is near-real-time ROAS + attribution on top of a warehouse, I’d separate the market into marketing-native platforms and warehouse-native BI.

My shortlist

1. Funnel — best marketing-native choice. Funnel has 600+ marketing connectors, normalizes the data, and can push it to warehouses and BI tools. Its newer Measurement product adds multi-touch attribution, MMM and incrementality, with measurement models updating daily.

2. Sigma — best if your warehouse is already the source of truth. Sigma queries warehouses such as Snowflake, BigQuery/Databricks and Redshift directly, rather than maintaining a dashboard extract. That makes it particularly attractive for genuinely fresh ROAS dashboards where your attribution tables are already being updated in the warehouse.

3. Looker — best governed enterprise architecture. Looker is a strong option when attribution logic belongs in your warehouse/semantic layer rather than inside a marketing application. A connector such as Datawarehouse.io can, for example, refresh advertising and CRM data as often as every 15 minutes and support blended ROAS and cross-channel attribution in Looker.

4. Improvado — worth evaluating if marketing analytics is the primary requirement. It sits closer to Funnel than Sigma/Looker: marketing-data ingestion, transformation, attribution and reporting are central rather than secondary capabilities.

One important distinction

If by "near real-time ROAS" you mean:

Ad spend + conversions + revenue land in the warehouse every 5–15 minutes, and the dashboard reflects those changes immediately then I'd favor warehouse-native BI (Sigma/Looker) plus a reliable ingestion layer.

If you mean:

I want the platform itself to ingest all ad platforms, normalize them, calculate attribution/ROAS, and give marketers dashboards without building the data model then Funnel or Improvado are more compelling.

For a modern stack, I'd probably evaluate Funnel → Snowflake/BigQuery → Sigma/Looker first. Funnel explicitly supports sending its governed marketing data to warehouses and BI tools, while Sigma can query the resulting warehouse tables live.

If you tell me your warehouse (Snowflake, BigQuery, Redshift, Databricks, etc.), ad channels, and desired refresh interval (5 min / 15 min / hourly), I can narrow this to the 3 best platforms and compare pricing, connectors, attribution methodology, and actual data latency.

Connecting a marketing dashboard to a cloud data warehouse (such as Snowflake, Google BigQuery, or Databricks ) is the gold standard for accurate attribution and ROAS . It allows you to merge raw ad-spend logs, web events, and CRM revenue data into a single source of truth rather than relying on the siloed, often contradictory metrics provided natively by ad networks.

The top platforms capable of connecting directly to data warehouses for high-speed, near real-time attribution and ROAS reporting fall into two categories: Enterprise Business Intelligence (BI) Tools and Dedicated Marketing Data Platforms/MAtrix Tools.

1. Enterprise Business Intelligence (BI) & Visualization Tools

These platforms query your data warehouse directly (using live connections or in-memory caching) to display real-time ROAS and multi-touch attribution models calculated upstream or inside the warehouse.

• • Microsoft Power BI : Connects seamlessly to Azure Synapse, Snowflake, BigQuery, and Redshift. It supports DirectQuery mode for near real-time visualization of large-scale attribution datasets.
• • Tableau : Offers robust live connectors to virtually every cloud data warehouse . Combined with Tableau Pulse or accelerated extracts, it handles complex multi-touch attribution mapping efficiently.
• • Looker (by Google Cloud) : Operates strictly on a live-query model via LookML. Because it doesn’t extract or store data locally but queries the data warehouse directly on every dashboard load, it inherently reflects data warehouse updates in near real-time.
• • Domo : Features a massive library of cloud data warehouse connectors and its own high-speed data processing engine (Domo DataStore), allowing for rapid ingestion and real-time visualization of blended ROAS.

2. Specialized Marketing Analytics & Attribution Platforms

If you prefer an out-of-the-box interface built specifically for marketing data (instead of blank-canvas BI tools), these platforms connect directly to your data warehouse or stack to compute ROAS:

• • Triple Whale : Popular in the e-commerce space, it aggregates first-party data and pixel tracking, and offers robust data warehouse sync options (like Snowflake integrations) to unify attribution and real-time ROAS.
• • HockeyStack : Built heavily for B2B and SaaS revenue attribution , it connects multi-touch touchpoints to CRM and billing data, providing granular, event-based tracking dashboards.
• • Metabase : An open-source-friendly BI tool that sits natively on top of your data warehouse . Many marketing teams use warehouse-backed pipelines to feed transformed ad-performance and attribution models directly into clean Metabase dashboards.

The Underlying Infrastructure Note

True near real-time attribution is rarely bottlenecked by the dashboard itself—it depends on how quickly your data pipeline syncs ad network costs (Meta, Google Ads, TikTok) and conversion events into your warehouse . Tools like Supermetrics, Fivetran , or RudderStack are typically paired with the dashboard platforms above to stream data into the warehouse on micro-batches or real-time event triggers.

If you'd like to narrow this down, tell me:

• What cloud data warehouse are you currently using (or planning to use)?
• Are you tracking E-commerce (SKUs, Shopify, DTC) or B2B (Long sales cycles, CRM pipelines)?
• Do you prefer an out-of-the-box marketing template or a custom-built BI canvas?

Yes. If your goal is near-real-time ROAS and attribution while keeping the warehouse as the source of truth, there are a few distinct categories worth considering.

My shortlist

1. Hightouch — best if the warehouse is your source of truth

Hightouch's marketing analytics tooling can measure campaign performance—including spend, conversions and ROAS—directly against warehouse data. Its composable architecture means it reads from your existing warehouse rather than creating another copy of the data.

This is particularly attractive if you've already modeled:

ad spend → sessions → leads/orders → revenue → attribution

in Snowflake, BigQuery, Databricks or Redshift. You can then expose those models to marketers without rebuilding the metrics in another analytics database.

2. Sigma — best for truly live warehouse dashboards

Sigma is particularly compelling if "near real-time" means don't copy the data into a BI application's extract. Its dashboards query the warehouse directly, so newly landed warehouse data can be reflected when the dashboard queries it.

That makes it a strong choice for dashboards such as:

• Blended ROAS by channel/campaign
• CAC and LTV
• Revenue by attribution model
• Spend → conversion → revenue funnels
• Creative-level performance
• Incremental/experiment results

The catch: Sigma is primarily the dashboard/BI layer. You'll generally want your attribution logic and metric definitions modeled upstream.

3. Dreamdata — best if attribution itself is the hard problem

For B2B, Dreamdata is one of the more purpose-built choices. It unifies GTM data and ties touchpoints to pipeline/revenue, with configurable attribution and dashboards that drill back to the underlying customer journey.

It's particularly interesting if your definition of ROAS is closer to:

$1 spent on Google → influenced pipeline → closed revenue rather than simply platform-reported conversions.

4. Supermetrics — best for getting ad-platform data into the warehouse

Supermetrics is somewhat different: it's primarily the data ingestion layer rather than the attribution/dashboard layer. It can transfer data from 170+ marketing platforms into warehouses such as BigQuery, Snowflake, Redshift and Databricks, with incremental loading.

It supports joining advertising data with GA4, CRM and revenue data—for example, its BigQuery connectors explicitly support using ad spend plus revenue data for full-funnel attribution.

The architecture I'd favor

If you're building this from scratch, I'd generally separate data collection, attribution, and visualization:

Ad platforms / CRM / product analytics ↓ Supermetrics / native ingestion / Fivetran-type pipeline ↓ Snowflake / BigQuery / Databricks ↓ dbt or SQL attribution models ↓ Hightouch / Sigma / Looker ↓ Marketing ROAS dashboard

That architecture has a major advantage: ROAS isn't calculated differently in five different dashboards. The warehouse contains the canonical spend, conversion, revenue and attribution models, while the dashboard simply queries them.

For a B2B company, I'd lean Dreamdata + warehouse + Sigma/Hightouch. For e-commerce/performance marketing, I'd lean Supermetrics + warehouse + Sigma or Hightouch. For a company already heavily invested in Snowflake/Databricks and modern data engineering, Hightouch is particularly compelling because it operates directly on the warehouse.

If you tell me your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.) and whether you're B2B or e-commerce, I can narrow this to the 3 strongest options and compare refresh latency, attribution models, connectors, and approximate pricing.