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Google Cloud Retail Global

Delivery Hero

Data Lake / Data Warehouse Modernization · Real-time Analytics

Delivery Hero needed a data platform that could ingest petabytes of Q-commerce data at scale, keep mission-critical cost calculations resilient, and stop senior engineers from spending time firefighting Spark infrastructure.

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Reported impact

Business outcome

Delivery Hero lowered platform overhead, improved engineering velocity, and gave the business fresher data for near-real-time supply-chain and inventory decisions.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Delivery Hero uses Google Cloud to address this challenge: Delivery Hero needed a data platform that could ingest petabytes of Q-commerce data at scale, keep mission-critical cost calculations resilient, and stop senior engineers from spending time firefighting Spark infrastructure. Managed Service for Apache Spark runs high-volume raw data import pipelines on provisioned clusters and also executes critical workloads in serverless mode when resilience matters most, serving as the compute layer. BigQuery receives the clean, governed data that downstream analytics, supply-chain planning, personalization, and machine learning teams depend on, serving as the storage layer. Petabyte-scale raw data is ingested through Apache Spark workloads on Google Cloud, critical jobs such as weighted average cost calculations move to serverless Spark for resilience, and the resulting clean datasets are delivered into BigQuery for analytics, forecasting, and downstream ML use.

Data flow

Petabyte-scale raw data is ingested through Apache Spark workloads on Google Cloud, critical jobs such as weighted average cost calculations move to serverless Spark for resilience, and the resulting clean datasets are delivered into BigQuery for analytics, forecasting, and downstream ML use.

Solution architecture

2 components · 2 layers
  1. Compute
    • Managed Service for Apache Spark runs high-volume raw data import pipelines on provisioned clusters and also executes critical workloads in serverless mode when resilience matters most
  2. Storage
    • BigQuery receives the clean, governed data that downstream analytics, supply-chain planning, personalization, and machine learning teams depend on

Architecture clues

  • BigQuery becomes the governed landing zone for analytics and machine learning consumers
  • The platform uses a two-pronged Spark strategy with provisioned clusters for scale and serverless Spark for resilient critical jobs
  • Twice-daily planning replaced weekly or bi-weekly planning for key operational functions

Evidence from the source

By migrating the Weighted Average Cost (WAC) calculation to serverless Managed Spark, the team no longer has to think about the underlying infrastructure.
For their large-scale, high-volume raw data import pipelines, the team runs Spark on provisioned Managed Service for Apache Spark clusters.
Lowered TCO and reduced hours spent on cluster management
The company is a pioneer in Quick Commerce (Q-commerce)—the next generation of ecommerce aiming to bring groceries and household goods to customers in under one hour and often in 20 to 30 minutes. For the data platform team supporting this vertical, this high-speed business model demands a data architecture that is equally agile and robust.
The stability of their data platform provides the fresh, accurate data needed to support this evolution.
Transparency Evidence & methodology 65/100 reference value · 77% 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 value65/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 specificity8/10
    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

    Delivery Hero customer story

    First-party
    https://cloud.google.com/customers/delivery-hero-managed-spark
    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

  • Sitemap last modified Jul 7, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jul 7, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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