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Snowflake Telecommunications Paris, France

Eutelsat Group

Predictive Maintenance · Real-time Analytics

GEO and LEO satellite operations generated petabyte-scale telemetry that needed unified storage, cross-domain access, and AI-driven outage prediction without adding major platform-management overhead.

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$2M savings in just six months
100% faster compute
40% reduction in compute costs in just one year
67% increase in data workloads

Reported impact

Business outcome

Eutelsat doubled compute performance on some workloads, saved more than $2 million in six months, cut compute costs 40% in a year, and now uses AI to reduce disruption across critical connectivity services.

  • Cost Reduction
  • Customer Experience
  • Risk Reduction
  • Scalability

Solution

Eutelsat uses Snowflake as the shared data plane for satellite telemetry, business systems, and AI workloads across its combined Eutelsat and OneWeb operations. Measurements from the LEO network arrive every few milliseconds, producing about 70 billion records a day, and Snowflake stores that data alongside Salesforce and SAP feeds plus GEO data surfaced through Apache Iceberg tables. Separate domain accounts handle satellite data, ground networks, finance, and CRM, while shared data products let teams query combined information without moving or duplicating it. On top of that foundation, Eutelsat uses Cortex AI to forecast outages and analyze customer sentiment, and Cortex Analyst gives nontechnical users natural-language access to data. Gen2 warehouses and ongoing optimization improve both speed and cost, making the same governed platform the home for current operations and future AI expansion.

Data flow

LEO telemetry, GEO data, Salesforce records, and SAP data flow into Snowflake on AWS, where domain accounts share curated data products across operations. Cortex AI and related ML workflows analyze network and customer data for outage prediction and sentiment, and Cortex Analyst serves natural-language answers to users.

Solution architecture

4 components · 4 layers
  1. Ingest
    • Apache Iceberg Brings GEO satellite data from a separate environment into Snowflake without large migration or duplication work.
  2. Compute
    • Cortex AI Supports outage forecasting and sentiment analysis on top of Eutelsat's large satellite and customer datasets.
  3. Storage
    • Snowflake Provides the central storage and sharing platform for satellite telemetry, business data, and enterprise data products.
  4. Serving
    • Cortex Analyst Lets nontechnical users explore governed data with natural-language questions.

Architecture clues

  • Apache Iceberg brings GEO data into the same analytics estate as LEO data.
  • Gen2 warehouses and a dedicated Snowflake architect drive ongoing optimization.
  • Separate Snowflake accounts exist for satellite data, ground networks, financials, and CRM.

Evidence from the source

Just six months in, Eutelsat experienced savings of over $2 million
Snowflake’s Cortex AI and machine learning capabilities have given Eutelsat new opportunities
compute speeds that are between 20-100% faster than before
creating 70 billion records a day. That’s multiple petabytes of data - all stored in Snowflake’s AI Data Cloud
uses Apache Iceberg tables to bring its GEO data from a separate environment into Snowflake
Transparency Evidence & methodology 100/100 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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
    6 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. Snowflake

    Eutelsat Group customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/eutelsat/
    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 Sep 4, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Sep 4, 2026 · Snowflake · 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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