AI Report: Analytics & Business Intelligence Stack at Meta and Google

Executive Summary

Both companies have built extensive custom analytics infrastructures over 15+ years to process petabytes of user data daily. Unlike their ML stacks, much of this is proprietary internal tooling designed for scale that far exceeds commercial BI platforms.

Meta's Analytics Infrastructure

Core Data Processing Stack

Component Purpose Notes
Presto Distributed SQL query engine Created at Facebook; open-sourced in 2013
Hive Data warehousing & ETL Heavily modified internally
Apache Kafka Real-time data streaming Petabytes/day throughput
Scribe/Scribe 2.0 Logging infrastructure Custom-built for scale

Query & Analytics Tools

  1. Presto (Open Source)

    • Developed specifically at Facebook to enable analysts to query live user data without heavy ETL
    • Enables SQL queries across billions of rows in seconds
    • Apache Presto Documentation

  2. Internal BI Dashboards

    • Custom-built visualization platforms for product metrics, ad performance, user engagement
    • Tightly integrated with their data pipelines
    • Not publicly documented (proprietary)

  3. Ad Measurement & Attribution Systems

    • Proprietary tools for tracking campaign effectiveness
    • Cross-platform attribution (Instagram, Facebook, WhatsApp)
    • Conversion lift measurement

Engineering Documentation

Google's Analytics Infrastructure

Core Data Processing Stack

Component Purpose Notes
BigQuery Serverless data warehouse External product; internal version powers much of Google
Colossus Distributed file system (GFS successor) Proprietary, not public
MapReduce / FlumeJava Batch processing framework Internal tools
MillWheel / Dataflow Stream processing Open-sourced as Apache Beam/Dataflow

Query & Analytics Tools

  1. BigQuery (Internal + External)

    • Google's internal analytics largely run on BigQuery infrastructure
    • Enables SQL queries on exabytes of data with sub-second latency
    • Google Cloud BigQuery

  2. Search Quality & AdWords Analytics

    • Highly proprietary tools for understanding search behavior, ad relevance
    • Click-through rate analysis, quality scoring systems
    • Not publicly documented in detail

  3. YouTube Analytics Infrastructure

    • Separate but integrated with core Google analytics
    • Viewer retention, engagement, recommendation effectiveness

Engineering Documentation

Common Patterns in Their Analytics Stacks

Architectural Principles Shared by Both

Pattern Description
Lambda Architecture Separate batch and real-time processing layers that merge for analysis
Data Lakehouse Model Raw data stored cheaply; processed on demand with SQL-like interfaces
Columnar Storage Parquet, ORC formats optimized for analytical queries
Tiered Storage Hot/warm/cold data placement based on access patterns

What They Measure (Common Metrics)

Both companies track similar categories of actionable information:

  1. User Engagement

    • Time spent, sessions per user, retention cohorts
    • Feature adoption rates

  2. Revenue & Monetization

    • Ad impressions, CTR, conversion rates
    • ARPU (Average Revenue Per User)

  3. Product Health

    • Error rates, latency percentiles
    • System uptime and availability

  4. Attribution & Lift

    • Campaign effectiveness
    • Incrementality testing

Key Differences: Meta vs Google Analytics Approach

Dimension Meta Google
Open Source Strategy Very aggressive (Presto, Hive contributions) Selective; much remains internal
Primary Focus Social graph analysis, ad targeting Search quality, ranking optimization
Real-time Emphasis Heavy focus on live feed/personalization Strong in Ads, more batch for Search
External Exposure Limited (mostly via ads platform) BigQuery available to customers

What Remains Opaque / Proprietary

⚠️ Important Caveats:

  1. Internal dashboards are not public — Neither company publishes their internal BI tool names or screenshots

  2. Custom-built metrics engines — Their attribution and measurement systems are highly proprietary trade secrets

  3. Real-time recommendation analytics — The systems powering “people you may know” (Meta) or search ranking adjustments (Google) are not documented in detail

  4. A/B testing infrastructure — Both run thousands of experiments daily; their experimentation platforms are internal

Sources & Further Reading

Meta

  1. Engineering.fb.com — Official engineering blog
  2. Presto Documentation
  3. Facebook's Data Engineering talks at QCon, Strata conferences (archived)

Google

  1. Google Research Publications
  2. BigQuery Architecture Papers
  3. “The Datacenter as a Computer” — Book on Google infrastructure

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