Connectors

Available data connectors.

Browse every registered connector, then open a connector to inspect its available datasets, report schemas, provider fields, Meridian mappings, and data types.

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Destinations

Export destinations for business-ready data.

Send governed Meridian datasets into warehouses, lakehouses, object storage, spreadsheets, BI tools, and built-in Meridian analysis surfaces.

14Shown
6Categories

BigQuery

Export semantic datasets and replicated source tables into Google BigQuery with backend-owned table and schema planning.

Data warehouseOAuth

Snowflake

Load governed exports into Snowflake databases and schemas using key-pair credentials and validated write modes.

Data warehouseKey pair

Amazon Redshift

Send period-safe semantic exports to Redshift tables through staged Parquet loading and customer IAM role access.

Data warehouseIAM role

Databricks

Stage semantic exports into Databricks SQL and Delta workflows with service-principal authentication.

LakehouseOAuth M2M

Microsoft Fabric Lakehouse

Export modeled datasets into OneLake and Fabric Lakehouse tables through service-principal credentials.

LakehouseService principal

Google Sheets

Replace selected spreadsheet ranges with semantic exports for recurring team reporting and lightweight analysis.

SpreadsheetOAuth

Excel

Use spreadsheet-ready exported datasets in Excel workflows when teams need offline review or workbook handoff.

SpreadsheetWorkbook

Looker Studio

Expose saved Meridian semantic exports to Looker Studio through the Meridian connector surface.

VisualizationConnector

Power BI

Push backend-compiled semantic exports into Power BI datasets with service-principal authentication.

VisualizationService principal

Amazon S3

Write JSON Lines exports to customer-owned S3 prefixes using cross-account IAM role access.

Object storageIAM role

Google Cloud Storage

Deliver JSON Lines exports to GCS buckets with destination credentials stored outside export configs.

Object storageService account

Azure Blob Storage / ADLS Gen2

Export JSON Lines datasets to Azure Blob Storage or ADLS Gen2 with Entra ID service-principal auth.

Object storageService principal

Meridian Dashboards

Use built-in dashboards powered by the same governed semantic layer used by exports.

MeridianIncluded

Data Explorer

Inspect fields, validate data, and explore modeled datasets inside Meridian before sending them downstream.

MeridianIncluded
Marketing Data Hub

Marketing data that arrives ready for decisions.

Metric Hive's Marketing Data Hub is the operating layer between source APIs and the places teams work. It collects provider data, preserves lineage, normalizes fields into governed semantic datasets, monitors trust signals, and exports modeled data to dashboards, spreadsheets, BI, and warehouses.

1

Collect source data

Connectors fetch source reports through governed credential, ingestion, interval, and source-state flows, then land the data with tenant, account, source, and date boundaries intact.

2

Normalize meaning

The hub maps provider fields into approved semantic datasets, including ad performance, web analytics, CRM, payments, ecommerce, product-ad performance, and source metadata.

3

Ship governed outputs

Explore, dashboards, transformations, Data Health, spreadsheets, BI connectors, and warehouse exports all use the same backend-owned contracts and compatibility guardrails.

What the hub owns

The Marketing Data Hub is not another reporting template. It is the shared data foundation that keeps connector ingestion, lake tables, semantic query datasets, transformations, monitoring, and exports aligned.

Cross-platform marketing contextPaid media, web analytics, CRM, ecommerce, payment, and product-ad data can stay in their own approved datasets instead of collapsing into one unsafe blended table.
Transformations with guardrailsBusiness dimensions and metrics can be shaped for reporting while backend services still own table names, SQL, tenant scope, grain, and export policy.
Data Health beside the pipelineFreshness, source failures, missing intervals, semantic queryability, and export health can be surfaced before a stale number reaches a budget meeting.
Exports that keep their shapeBigQuery, Snowflake, spreadsheets, Looker Studio, Power BI, object storage, and other destinations receive modeled datasets instead of raw provider dumps.
Semantic Layer

The semantic layer is the core of Metric Hive.

Raw connector fields only become useful when the system understands what they mean. Metric Hive maps platform data into canonical entities, grains, dimensions, metrics, date rules, currency behavior, and table contracts so every dashboard, export, and analysis starts from the same decision-ready model.

1

Connect sources

Connectors read provider reports with their source scope, timestamps, currencies, identifiers, and coverage decisions, then preserve enough lineage for audit and troubleshooting.

2

Interpret meaning

The semantic contract decides each field's canonical entity, base grain, role, aggregation behavior, additivity, visibility, and export policy before users can query it.

3

Ship governed data

Dashboards, Explore, spreadsheets, BI, and data-warehouse exports all use the same contracts, so decisions are made from the same model wherever the data goes.

Why it matters

Metric Hive is built with the semantic layer at its core. It keeps connector ingestion, lake tables, query datasets, exports, and app surfaces aligned around the same business meaning.

Right decisions, fewer argumentsTeams compare fields that mean the same thing and can see when a metric is not safe to combine with another grain.
Not raw connector plumbingProvider fields are normalized into business concepts without losing source lineage, provider context, or controlled advanced detail.
Grain-aware metricsCounts, rates, snapshots, revenue, orders, and customer metrics carry aggregation rules so totals are not multiplied by unsafe joins.
Warehouse exports modeled and readyExports to destinations like BigQuery, Snowflake, spreadsheets, and BI tools use canonical tables instead of forcing your team to rebuild the model downstream.
Pricing Optimization

Pricing Optimization for margin-safe growth.

Metric Hive turns Commerce Intelligence profit data into a pricing and promotion review system. It monitors product prices, realized selling price, discounts, returns, inventory, cost rules, and contribution margin so ecommerce teams can find where to protect margin before they change prices, launch promotions, or increase campaign spend.

1

Check pricing readiness

The system verifies product catalog, variant or SKU coverage, order lines, discount evidence, COGS or cost rules, refunds, returns, inventory, freshness, and currency policy before showing recommendations.

2

Model the economics

Commerce Intelligence evaluates realized selling price, gross revenue, net revenue, discount rate, COGS, gross margin, contribution margin, return impact, and ad spend context where available.

3

Stage review decisions

Recommendation queues can suggest price review, promo review, discount suppression, margin guardrails, campaign exclusions, or clearance review, but decisions remain manual and do not publish price changes.

What teams can review

Pricing Optimization is a Commerce Intelligence add-on. It helps operators, finance, merchandising, and performance marketing teams inspect the economics behind product prices and promotions without rebuilding margin spreadsheets.

Product price and margin monitoringReview current price, realized selling price, discount amount, gross margin, contribution margin, return rate, inventory status, and recommendation status by product or SKU.
Promotion performance analysisCompare owned-data discount and promotion periods by orders, units, revenue, discount amount, contribution margin, return impact, and result label.
Margin leak queuesSurface low-margin products, margin-negative products, high-discount products, high-return products, and other leak signals that need review before the next promotion.
Evidence-backed exportsSend modeled pricing price-margin, promotion performance, margin leak, and recommendation datasets into BigQuery and planning workflows with grain, currency, and provenance preserved.
Measurement

Measure marketing with modeled evidence, not platform scorecards.

Metric Hive Measurement brings MMM, MTA, and triangulation into the same semantic layer that powers your modeled marketing and commerce data. Every result starts with backend-owned readiness checks for scope, outcome trust, media or touchpoint coverage, currency behavior, diagnostics, and publishing status.

1

Define the measurement scope

Measurement uses an account or default market scope with timezone, currency, week start, date range, outcome source, and versioned assumptions attached to durable outputs.

2

Check inputs before methods

Readiness verifies outcome history, media spend, touchpoint events, conversion provenance, privacy-safe bridge quality, history depth, and currency safety before results are produced.

3

Publish only guarded outputs

Results can be blocked, directional, or publishable. Trusted surfaces require diagnostics, safe metadata, and publish/sign-off state before outputs are treated as customer-visible evidence.

What Measurement includes

The product family is organized around method modules and a shared readiness contract, so MMM and MTA do not invent separate data assumptions for the same business question.

Marketing Mix ModelingEstimate aggregate contribution, contribution share, ROI, uncertainty, and diagnostics from weekly media and outcome patterns.
Multi-Touch AttributionReport attributed conversions, revenue, contribution margin, model comparison, unattributed share, and path-quality diagnostics from eligible observed paths.
TriangulationCompare already published MMM and MTA summaries for the same scope, outcome mapping, and channel taxonomy to find alignment, conflict, or blockers.
Release-safe guardrailsLocked future modules such as incrementality and budget optimization stay separate until their readiness, billing, outputs, and audit paths exist.
Product Catalog Optimization

Make product feeds safe enough to spend behind.

Metric Hive turns Shopify catalog data, inventory evidence, Google Merchant Center diagnostics, and semantic product tables into feed readiness checks, priority issue groups, preview rows, and safe exports. The first version is built for Google Shopping diagnostics and export-ready product data without mutating Merchant Center.

1

Connect catalog evidence

Shopify provides product, variant, price, image, and inventory context. Google Merchant Center issue data adds provider-side diagnostics when it is connected.

2

Score feed readiness

The backend checks destination requirements, product identity, freshness, missing fields, provider issues, inventory risk, and export policy before a row is considered usable.

3

Stage safe outputs

Teams get issue groups, deterministic recommendations, trust receipts, feed previews, CSV downloads, and modeled exports while external platform publishing stays disabled in v1.

What teams can decide

Product Catalog Optimization is a feed intelligence layer, not a PIM replacement. It helps teams understand which products are ready for paid shopping channels, which products are blocked, and what evidence explains the decision.

Fix the highest-impact problems firstPriority issue groups combine severity, affected products, blockers, warnings, and available business evidence instead of mirroring raw diagnostics.
Stop advertising unsafe productsDisapprovals, missing required fields, stale catalog records, out-of-stock products, and low-margin products can be flagged before they reach a feed export.
Preview before anything changesBefore-and-after row previews show source values, normalized export values, blocked rows, warnings, and missing fields without changing Shopify or Merchant Center.
Give data teams modeled feed evidenceAllowlisted Product Feed exports expose readiness snapshots, issue history, variant scores, and destination export evidence for warehouse and BI workflows.
Commerce Intelligence

Know the true profit behind every order, product, customer, and channel.

Commerce Intelligence turns ecommerce orders, order lines, product catalog, COGS, payment fees, refunds, returns, shipping, fulfillment, inventory, marketing context, and currency policy into governed profit datasets. The result is not another revenue dashboard. It is a traceable commerce model that shows where margin is created, where it leaks, and which outputs are safe to use.

1

Bring commerce inputs together

Metric Hive keeps orders, products, payments, costs, refunds, returns, inventory, web events, marketing touchpoints, and source provenance scoped to the correct account, shop, currency, and date grain.

2

Derive explainable profit

The modeled layer applies cost rules, product identity, payment bridges, validation checks, and currency policy so gross revenue can become net revenue, gross profit, and contribution margin without unsafe joins.

3

Unlock governed decisions

Dashboards, Explore, exports, BI, and other intelligence modules use the same approved datasets, and later modules remain gated when evidence is stale, missing, zero-row, or not launch-ready.

What teams can decide

Commerce Intelligence is the profit truth layer for Metric Hive. It gives marketing, finance, ecommerce, and data teams a shared model for decisions that should not be made from revenue or platform ROAS alone.

Which products actually make moneyContribution margin can be reviewed by product, SKU, channel, country, currency, and date where the underlying profit rows and product identity are ready.
Where margin leaksCost waterfalls keep discounts, refunds, COGS, payment fees, merchant shipping cost, fulfillment, duties, customs, returns, and other costs visible instead of blending them away.
Which customers are valuableLifecycle and LTV views can use modeled revenue, refunds, returns, costs, and acquisition context when backend readiness proves the dataset for that scope.
Which channels deserve spendMarketing attribution and creative performance can read from modeled commerce outcomes so growth decisions can consider contribution profit, not just attributed revenue.
What inventory and demand signals implyInventory health, forecasts, and recommendations stay tied to product identity, sales behavior, margin, freshness, and source coverage instead of isolated stock snapshots.
What can be exported safelyApproved modeled exports expose governed Commerce Intelligence datasets without letting frontend inputs choose raw table names, SQL fragments, tenant scope, or unsafe joins.
Product

Product

Product description placeholder.

Detailed product copy will be added here.
Clear pricing for business-ready data

Marketing data without enterprise-plan friction.

Meridian connects marketing, CRM, payment, and ecommerce data, stores it, maps it into common business fields, and sends it where your team works. No-code for marketers. Programmable workflows for technical teams.

$1.25per data source each month
From $25for spreadsheet exports
Unlimitedusers in every subscription
Profit waterfallshipping, gateway fees, and late returns stay modeled separately
Pricing

Clear pricing for every source, export, and team.

Pay monthly for what you use: 4 data sources + Google Sheets export one month, 100 sources + BigQuery the next. For commerce teams, the modeled layer separates customer-paid shipping revenue, merchant shipping costs, gateway fees, and retroactive return adjustments so profit reporting does not collapse into generic dashboard math.

Pricing Calculator
Data sources
Exports
Product

From scattered platform data to one trusted reporting layer.

Meridian stores your source data, shapes it into shared business definitions, and keeps it ready for dashboards, exports, and automation. Commerce models preserve the difference between what customers paid for shipping, what the merchant paid to fulfill it, transaction fees, and late return adjustments.

1

Connect

Bring in marketing, CRM, payment, ecommerce, and analytics data. Keep source data stored so reporting is fast and repeatable.

2

Transform

Map platform-specific fields into common business fields. Create custom dimensions and metrics for your own reporting logic.

3

Activate

Explore data in Meridian, export it to your tools, and automate supported workflows through API and MCP.

Transformation

Not just extracted. Made usable.

Meridian keeps source detail available while giving your team common fields for reporting. Build shared definitions once, then use them in dashboards, exports, and automated workflows.

Native source fieldsBefore
SourceNative fieldValue
Meta Adsspend420.13
Google Adscost_micros391200000
LinkedIn AdsamountSpent82.44
Meridian common fieldsAfter
Common fieldValue
cost893.77
shipping_amount12.00
shipping_cost_amount10.00
payment_fee_amount11.00
refund_amount50.00
return_cost_amount4.00
currencyUSD
date2026-05-01
channelPaid social / Paid search
No-code and programmable

Point and click when you want speed. Use code when you want control.

The same data model powers the UI, exports, and programmable workflows, so marketers and technical teams can work from one shared foundation.

For marketers

  • Connect sources without writing code
  • Choose common fields and custom metrics
  • Explore performance in dashboards
  • Send reports to spreadsheets and BI tools

For technical teams

  • Automate supported setup and operations
  • Export warehouse-ready datasets
  • Build repeatable workflows with API coverage
  • Connect AI agents through Meridian MCP as coverage expands
Destinations

Send clean data where your team already works.

Use Dashboards and Data Explorer, export to spreadsheets and BI tools, and sync clean datasets to your warehouse destinations.

Spreadsheets

Google Sheets
Excel

BI and visualization

Looker Studio
Power BI

Warehouses

BigQuery
Snowflake
Redshift

Meridian

Dashboards
Explore
Field validation
Access

Every subscription gets the whole platform.

Your bill changes when you connect more data or add exports, not when someone needs another seat or a team wants to use a product feature.

Transformations includedNormalize fields and create business-ready dimensions and metrics.
Dashboards includedView performance and validate data before sending it downstream.
Explore includedInspect source freshness, fields, and reporting tables in Meridian.
Data Health includedTrack source freshness, export health, semantic trust, and product readiness without buying a separate module.
Users includedAdd the team members who need access without seat-count planning.
Scale includedMove from a few sources to many sources without switching plans.

Start with one source. Scale when it makes sense.

Connect a source, normalize the fields that matter, and send business-ready data to the tools your team already uses.

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