Use Case Intelligence RSS Search use cases Search
Google Cloud Software / SaaS Global

Etsy

Data Lake / Data Warehouse Modernization · IT Operations Automation · Real-time Analytics

To serve its massive community, Etsy relies on multiple compute engines to process vast amounts of structured and unstructured items and search data. Previously, users read and wrote to Google Cloud Storage using Apache Spark or similar processing engines by referencing direct file paths or internal modules.

Read the original story Explore all use cases
Accelerated data joining by up to 60%
This efficiency is largely due to the ability to now use storage-partitioned joins (SPJ) in Spark, a capability unlocked by the Iceberg format. By implementing SPJ, Etsy has accelerated data joining by up to 60%.

Reported impact

Business outcome

Migrated two petabytes of data to a managed lakehouse; Accelerated data joining by up to 60%; Completed setup in weeks versus months; By migrating to Google Cloud’s Lakehouse, Etsy accelerated ML pipelines and improved search relevance to match unique items..

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

By using Google Cloud’s Managed Service for Apache Spark serverless solution, Etsy established a streamlined, recommended path for all big data compute jobs. The Lakehouse runtime catalog , based on the Iceberg REST catalog, eliminates the need for developers to track raw Cloud Storage file paths. Now, data written through Spark jobs becomes instantly and automatically readable in BigQuery for analytics teams, allowing seamless interoperability across the organization's favored engines. With a self-hosted solution, the setup would’ve taken months. With Google’s Lakehouse solution, the team completed setup in weeks. By taking a strategic, use-case-driven approach rather than a single large-scale migration, Etsy seamlessly moved more than two petabytes of data to the new architecture.

Data flow

stored or unified in BigQuery, Cloud Storage, processed with Managed Spark

Solution architecture

3 components · 2 layers
  1. Compute
    • Managed Spark Processes large datasets with managed Apache Spark jobs on Google Cloud.
  2. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.

Architecture clues

  • By migrating to Google Cloud’s Lakehouse, Etsy accelerated ML pipelines and improved search relevance to match unique items.
  • Products: Cloud Storage BigQuery

Evidence from the source

Accelerated data joining by up to 60%
By migrating to Google Cloud’s Lakehouse, Etsy accelerated ML pipelines and improved search relevance to match unique items.
Completed setup in weeks versus months
Migrated two petabytes of data to a managed lakehouse
Overcoming file-path dependency and data silos
Transparency Evidence & methodology 95/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    Etsy customer story

    First-party
    https://cloud.google.com/customers/etsy-lakehouse
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 9, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 7, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
Google Cloud Archived · Jun 27, 2026 Google Cloud Support Software / SaaS United States Google Cloud Support revolutionized operations, moving from fragmented tools to a governed, AI-powered Looker environment supercharged by conversational analytics. In a global enterprise, support operations inherently… 10x the speed of analysis for end users through conversational analytics Data Lake / Data Warehouse ModernizationIT Operations Automation BigQuery · Gemini · Gemini Enterprise Google Cloud Archived · Apr 30, 2026 Sonic Labs Software / SaaS Australia However, in a competitive landscape, it’s not enough for a blockchain to be “fast and cheap,” according to Seg Rashid, Head of AI and Developer Relations at Sonic Labs. The company recognized that to sustainably attract… , a Google Cloud partner, builds elite Web3 and AI solutions. It provides expert cloud delivery, IaaS,… Data Lake / Data Warehouse ModernizationIT Operations Automation BigQuery · Cloud Composer · Cloud Pub/Sub Google Cloud Archived · Oct 16, 2026 Alta Software / SaaS United States Alta needed a transactable Google Cloud Marketplace path for its AI and technology spend management platform without pulling its product team off core forecasting work, while also helping finance and IT leaders… Saved 125 engineering and 60 project management hours IT Operations AutomationReal-time Analytics Cloud Pub/Sub · Cloud Run · Cloud SQL