The analytics landscape offers two fundamentally different approaches: warehouse-first (using tools like dbt, Snowflake, and BI platforms) and product analytics (using tools like PostHog, Amplitude, or Mixpanel). Choosing the right approach—or the right combination—can dramatically impact your team's ability to generate insights.

This guide helps you understand when to use each approach, their trade-offs, and how to make the decision based on your specific context.

Understanding the Two Approaches

Warehouse-First Analytics

The warehouse-first approach treats your data warehouse as the single source of truth. All data flows into the warehouse, where analysts and engineers transform it using SQL and tools like dbt.

Key components:

  • Data warehouse: Snowflake, BigQuery, Redshift, or Databricks
  • Transformation: dbt, Dataform (Google Cloud), or custom SQL
  • BI layer: Looker, Metabase, Tableau, Mode, or Power BI
  • Orchestration: Airflow, Dagster, or Prefect
  • Data ingestion: Fivetran, Airbyte, or Stitch

Data flow:

Sources -> ETL/ELT -> Warehouse -> dbt Models -> BI Tools -> Dashboards/Reports

Product Analytics

Product analytics platforms provide an integrated experience for tracking user behavior and analyzing product usage patterns.

Key components:

  • Platform: PostHog, Amplitude, Mixpanel, or Heap
  • SDK integration: Client and server-side tracking
  • Built-in features: Funnels, retention, cohorts, session replay
  • Self-serve exploration: Point-and-click analysis

Data flow:

App Events -> SDK -> Product Analytics Platform -> Built-in Dashboards/Analysis

Understanding the Tools

Data Warehouse Comparison

Each major cloud data warehouse has distinct strengths:

  • Snowflake: Multi-cloud (AWS, Azure, GCP), separated storage and compute for flexible scaling, strong data sharing capabilities. Best for organizations wanting cloud-agnostic flexibility and workload isolation via virtual warehouses.
  • BigQuery: Fully serverless with automatic scaling, on-demand pricing based on bytes scanned, deep Google Cloud integration. Ideal for teams preferring minimal cluster management and bursty workloads.
  • Redshift: Tight AWS integration, PostgreSQL-compatible, offers both provisioned clusters and serverless options. Strong fit for AWS-centric organizations with predictable workloads.
  • Databricks: Unified analytics platform combining data warehousing with data science and ML. Best for organizations with significant machine learning requirements alongside analytics.

Product Analytics Platform Comparison

  • PostHog: Open-source, developer-first platform combining analytics, session replay, feature flags, and A/B testing. Free tier includes 1 million analytics events, 5,000 session replays, and 1 million feature flag requests monthly. Offers self-hosted option for data control.
  • Amplitude: Enterprise-focused with advanced behavioral cohorting and predictive analytics. Pricing based on monthly tracked users (MTUs). Strong for marketing and growth teams with sophisticated segmentation needs.
  • Mixpanel: Event-based analytics with intuitive UI for non-technical users. Free tier offers 20 million events monthly. Recently added session replay. Ideal for teams wanting quick insights without heavy technical setup.
  • Heap: Auto-capture approach that tracks all user interactions automatically. Reduces implementation overhead but can generate high event volumes. Best for teams wanting comprehensive tracking without manual instrumentation.

When to Choose Warehouse-First

The warehouse-first approach excels in specific scenarios:

You Need to Combine Multiple Data Sources

When analysis requires joining product data with:

  • CRM data (Salesforce, HubSpot)
  • Financial data (Stripe, billing systems)
  • Marketing data (ad platforms, email tools)
  • Operational data (support tickets, inventory)
  • Third-party enrichment data
-- Example: Warehouse query joining multiple sources
SELECT
    u.user_id,
    u.signup_date,
    p.total_events,
    s.mrr,
    h.support_tickets
FROM users u
LEFT JOIN product_events p ON u.user_id = p.user_id
LEFT JOIN stripe_subscriptions s ON u.stripe_id = s.customer_id
LEFT JOIN hubspot_tickets h ON u.email = h.contact_email
WHERE u.signup_date >= '2025-01-01'

You Have Complex Business Logic

When definitions require sophisticated SQL:

  • Custom attribution models spanning multiple touchpoints
  • Complex cohort definitions with multiple criteria
  • Multi-touch conversion tracking across channels
  • Revenue recognition rules and financial metrics
  • Custom LTV calculations with churn predictions

You Need Historical Data Flexibility

Warehouse-first allows you to:

  • Redefine metrics retroactively without losing history
  • Change event schemas and backfill historical data
  • Run complex backfills and corrections
  • Maintain data lineage and audit trails

You Have Data Engineering Resources

The approach requires:

  • SQL expertise for modeling (dbt proficiency)
  • Data engineering for pipeline maintenance
  • BI tool administration and governance
  • Understanding of data modeling best practices

You Require Strong Data Governance

Warehouse-first provides:

  • Centralized metric definitions via semantic layers
  • Version-controlled transformations with dbt
  • Built-in testing and documentation
  • Clear data lineage across all downstream uses

When to Choose Product Analytics

Product analytics platforms shine in different scenarios:

You Need Fast Time-to-Insight

Product analytics provides:

  • Minutes to first dashboard (vs. weeks for warehouse setup)
  • Pre-built visualizations for common analyses
  • No SQL required for basic questions
  • Immediate value from SDK installation

You're Focused on User Behavior

Built specifically for questions like:

  • How do users navigate through my app?
  • Where do users drop off in the funnel?
  • Which features correlate with retention?
  • What do users do before churning?
  • How do different cohorts behave over time?

You Want Self-Serve Analytics

Product managers and designers can:

  • Create funnels without SQL
  • Build cohorts with point-and-click interfaces
  • Watch session recordings to understand context
  • Analyze A/B test results independently
  • Ask natural language questions (increasingly supported)

You Have Limited Technical Resources

Product analytics is more accessible:

  • No dedicated data engineering team required
  • Managed infrastructure (for cloud versions)
  • Built-in best practices for common use cases
  • Faster onboarding for new team members

You Need Integrated Experimentation

Modern product analytics platforms (especially PostHog) include:

  • Feature flags for gradual rollouts
  • A/B testing with statistical significance
  • User surveys for qualitative feedback
  • Session replay for debugging and UX insights

Comparison Matrix

Dimension Warehouse-First Product Analytics
Time to first insight Weeks to months Hours to days
Data sources Unlimited Primarily product events
Query flexibility Full SQL power Pre-defined patterns + SQL on some platforms
Self-serve capability Limited (requires SQL or BI tool training) High (point-and-click interfaces)
Technical requirements Data engineering team SDK integration only
Cost at scale Lower marginal cost (own infrastructure) Higher marginal cost (per-event pricing)
Real-time analysis Varies (batch to streaming) Usually real-time or near real-time
Session replay Separate tool needed Often built-in (PostHog, Mixpanel, Amplitude)
Feature flags Separate tool needed (LaunchDarkly, etc.) Built-in on some platforms (PostHog)
Data governance Strong (version control, lineage, testing) Platform-dependent, generally lighter
Metric consistency Centralized definitions via semantic layer Defined per-dashboard or per-report

Decision Framework Based on Team Size

Startups (< 20 people)

Recommendation: Product Analytics First

  • No dedicated data team yet
  • Need quick answers to product questions
  • Focus on finding product-market fit
  • Limited budget for infrastructure

Start with PostHog (generous free tier, open-source option) or Mixpanel (20M free events). Add warehouse later when you have data engineering capacity or need cross-functional analysis.

Growth Stage (20-100 people)

Recommendation: Hybrid Approach

  • Product analytics for day-to-day product decisions
  • Warehouse for finance, marketing, and cross-functional analysis
  • Export product data to warehouse for advanced analysis
  • Consider hiring first data engineer to build foundation
# Hybrid architecture
Product Analytics (PostHog/Amplitude)
  |
  +-> Product team self-serve
  |
  +-> Export to warehouse (via native connectors or Segment/RudderStack)
       |
       +-> Join with CRM, billing, marketing data
       |
       +-> dbt models for business metrics
       |
       +-> BI tools for exec dashboards

Scale-up (100+ people)

Recommendation: Warehouse-First with Product Analytics Layer

  • Data team can build custom models and maintain governance
  • Need for cross-functional analytics is high
  • Product analytics for specialized use cases (session replay, quick experimentation)
  • Single source of truth in warehouse with semantic layer
  • Clear ownership model between data platform and product teams

Decision Framework Based on Data Needs

Primary Use Case: Product Optimization

If your main questions are:

  • How do users engage with features?
  • What's our activation funnel conversion?
  • Which users are likely to churn?
  • What does the user journey look like?

Recommendation: Product Analytics

Primary Use Case: Business Intelligence

If your main questions are:

  • What's our CAC by channel over time?
  • How does product usage correlate with revenue?
  • What's the LTV of different customer segments?
  • How do marketing campaigns affect pipeline?

Recommendation: Warehouse-First

Primary Use Case: Both

If you need both types of analysis:

Recommendation: Hybrid with clear boundaries

# Clear ownership model
Product Analytics: Product and UX teams
  - Feature usage and adoption
  - User flows and funnel analysis
  - A/B testing and experimentation
  - Session replay and debugging

Warehouse + BI: Data team
  - Revenue and financial metrics
  - Marketing attribution
  - Cross-functional reporting
  - Executive dashboards
  - ML feature stores

Implementation Patterns

Pattern 1: Product Analytics Primary

Best for product-led growth companies focused on user behavior:

App --> PostHog/Amplitude
         |
         +--> Self-serve dashboards for product team
         |
         +--> Feature flags and experiments
         |
         +--> Export to warehouse (optional)
              for advanced analysis and data science

Pattern 2: Warehouse Primary

Best for data-mature organizations with strong governance needs:

App --> Event streaming (Segment/RudderStack) --> Warehouse
                                                      |
         +--------------------------------------------+
         |                    |                       |
    dbt models           BI tools              ML/Data Science
         |              (Looker/Tableau)
    Semantic layer
         |
    Reverse ETL --> Product analytics (light use)
                    CRM enrichment
                    Marketing platforms

Pattern 3: Parallel Systems with CDP

Best for organizations needing both real-time product insights and comprehensive business analytics:

App --> Segment/RudderStack (CDP)
            |
            +-> PostHog (real-time product analytics)
            |
            +-> Warehouse (batch business analytics)
            |       |
            |       +-> dbt + BI tools
            |
            +-> Marketing tools (Braze, HubSpot)
            |
            +-> Data enrichment services

Cost Considerations

Product Analytics Costs (2025)

  • PostHog: 1M events free, then ~$0.00045/event. Self-hosted option available. $50k startup credits for eligible companies.
  • Mixpanel: 20M events free, Growth plans from ~$24/month scaling with events.
  • Amplitude: 50,000 MTUs free, usage-based pricing above that. Enterprise features (experiments, advanced cohorts) require higher tiers.
  • At 100M events/month: Expect $10,000-50,000+/month depending on platform and features.

Warehouse-First Costs

  • Warehouse compute/storage: $1,000-15,000/month (varies significantly by usage pattern)
    • BigQuery: Pay per query (bytes scanned) or flat-rate slots
    • Snowflake: Pay for compute credits + storage separately
    • Redshift: Provisioned nodes or serverless (RPU-seconds)
  • BI tools: $0 (Metabase OSS) to $40,000+/year (Looker enterprise)
  • ETL/ELT tools: $500-5,000/month (Fivetran, Airbyte Cloud)
  • dbt Cloud: Free tier available, Team plan ~$100/seat/month
  • Personnel: Data engineer ($130-180K/year), Analytics engineer ($110-150K/year)

Crossover Analysis

# Product analytics becomes expensive when:
- Events > 50-100M/month AND
- Questions are mostly cross-functional AND
- You have data engineering capacity AND
- Data governance is a priority

# Warehouse-first is expensive when:
- You don't have data engineering AND
- Questions are primarily product-focused AND
- You need real-time insights AND
- Time-to-insight is critical for experimentation

Migration Strategies

From Product Analytics to Warehouse-First

  1. Export historical data: Most platforms allow data export (PostHog exports to S3/GCS, Amplitude has export APIs)
  2. Build warehouse foundation: Set up dbt, create staging and mart models
  3. Replicate key metrics: Ensure metric parity before switching teams over
  4. Gradual transition: Move teams one at a time, starting with those needing cross-functional data
  5. Keep product analytics: Retain for specialized features (session replay, experiments) if cost-effective

From Warehouse to Adding Product Analytics

  1. Identify gaps: What questions are hard to answer today? (usually: session-level behavior, quick experiments)
  2. Deploy SDK: Add tracking to application with careful event taxonomy
  3. Set up sync: Export warehouse data for context (user properties, segments)
  4. Define ownership: Which team uses which tool for what purpose?
  5. Establish single source of truth: Decide where authoritative metrics live

Common Pitfalls

Pitfall 1: Building Everything in Warehouse

Trying to recreate funnel analysis, retention curves, and session replay in your warehouse is expensive and time-consuming. These are solved problems in product analytics tools.

Solution: Use product analytics for what it's good at. Focus warehouse efforts on unique business logic and cross-functional analysis.

Pitfall 2: Duplicating Metrics

Having "Monthly Active Users" defined differently in product analytics and warehouse causes confusion and erodes trust in data.

Solution: Establish single definitions, ideally in the warehouse with dbt's semantic layer or Looker's LookML, and sync to product analytics where possible.

Pitfall 3: Over-engineering Early

Building a full warehouse stack when you have 1,000 users delays learning about your product and users.

Solution: Start simple with product analytics, add complexity when the need is clear and you have capacity.

Pitfall 4: Under-investing at Scale

Continuing with only product analytics when you need cross-functional analysis limits insights and creates bottlenecks.

Solution: Invest in warehouse infrastructure when you reach growth stage and have recurring needs for joined data.

Pitfall 5: Ignoring Data Governance

As data usage grows, inconsistent definitions and lack of documentation create chaos.

Solution: Implement governance early: use dbt for documented transformations, establish naming conventions, create a data dictionary.

Pitfall 6: Tool Sprawl

Adopting every new analytics tool creates integration headaches and fragmented insights.

Solution: Be intentional about tool adoption. Each tool should serve a clear purpose that isn't covered by existing tools.

Making the Decision

Answer these questions to guide your choice:

  1. What are your top 5 analytics questions?
    • Mostly product behavior? → Product Analytics
    • Mostly cross-functional? → Warehouse-First
    • Mixed? → Hybrid approach
  2. Do you have data engineering capacity?
    • Yes, dedicated team → Warehouse-First is viable
    • Part-time or learning → Start with product analytics, build warehouse skills
    • None → Product Analytics or managed solutions
  3. What's your event volume?
    • Under 20M/month → Product Analytics highly cost-effective
    • 20-100M/month → Evaluate based on other factors
    • Over 100M/month → Consider self-hosted or warehouse-first
  4. How important is real-time?
    • Critical for product decisions → Product Analytics
    • Daily batches are acceptable → Warehouse-First works well
    • Need both → Hybrid with streaming to warehouse
  5. What are your governance requirements?
    • Heavy (regulated industry, SOC2, HIPAA) → Warehouse-First with strong controls
    • Moderate → Either approach with proper configuration
    • Light → Product Analytics is simpler

The Modern Data Stack in 2025

The analytics landscape continues to evolve. Key trends to consider:

  • Warehouse-native CDPs: Tools like RudderStack enable CDP functionality on your own warehouse, bridging product analytics and warehouse-first approaches.
  • Semantic layers: dbt's semantic layer and Looker's LookML enable consistent metric definitions across tools.
  • Reverse ETL: Tools like Census and Hightouch activate warehouse data in operational tools, reducing need for separate product analytics data.
  • Composable CDP: Building CDP functionality from warehouse primitives rather than using monolithic platforms.
  • AI-assisted analytics: Natural language querying is becoming standard across both product analytics and BI tools.

Next Steps

Based on your situation:

  1. Document your requirements: List your top analytics questions and who needs to answer them
  2. Assess your resources: Technical capacity, budget, and timeline constraints
  3. Start appropriate: Don't over-engineer early, but don't under-invest as you scale
  4. Plan for evolution: Your needs will change as you grow; choose tools that export data cleanly
  5. Establish governance early: Define metrics, document decisions, version control transformations
  6. Build feedback loops: Regularly assess whether your analytics stack is serving your needs

The best analytics stack is the one that actually gets used to make decisions. Start with what your team can effectively operate today, and evolve as your needs and capabilities grow. The goal isn't to have the most sophisticated stack—it's to generate insights that drive better product and business decisions.