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Snowflake Software / SaaS New York, NY, USA

Order.co

Data Lake / Data Warehouse Modernization

replace a warehouse where development workloads, production pipelines, ad hoc analytics, and real-time traffic competed for the same resources, creating operational risk and scaling complexity.

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2 - 7x Faster analytics queries
9,000 Dashboards migrated in 2 months
more than 200 business users

Reported impact

Business outcome

Order.co improved analytics query speed by 2x to 7x, migrated nearly 9,000 dashboards in two months, and gave more than 200 users faster access to reliable analytics on one platform.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Order.co uses Snowflake as its unified analytics platform, with dbt transformations and AI-assisted migration tooling, to replace a resource-constrained warehouse and support customer-facing data products, analytics, and machine-learning workloads. Production pipelines, ad hoc analysis, real-time traffic, and large JSON objects are consolidated onto Snowflake so separate compute can run without the contention that previously put customer data products at risk. dbt models power enterprise transformations, while Snowflake AIM and Snowflake CoCo helped one engineer convert and validate nearly 9,000 dashboards during the migration. With fewer platforms to maintain and faster queries across more than 200 business users, the data team can focus on compute efficiency and future conversational analytics experiences instead of tuning queues and clusters.

Data flow

Operational data, large JSON objects, analytics workloads, and customer-facing data-product pipelines are consolidated in Snowflake, where dbt transformations prepare curated datasets. Business users then consume faster dashboards and analytics while the team validates migrations and prepares future conversational AI experiences.

Solution architecture

2 components · 2 layers
  1. Compute
    • dbt Runs the company’s transformation logic on Snowflake to support enterprise analytics and data-product workloads.
  2. Storage
    • Snowflake Provides the unified analytics platform with independent compute for production pipelines, ad hoc analysis, and customer-facing data products.

Architecture clues

  • Snowflake AIM migration agents and Snowflake CoCo were used to automate dashboard conversion and data validation.
  • Snowflake replaced a design where separate clusters and queue tuning were needed to isolate workloads.
  • The company consolidated two separate systems onto Snowflake because native JSON processing simplified development.

Evidence from the source

2 - 7x Faster analytics queries
9,000 Dashboards migrated in 2 months
After moving to Snowflake, we saw query performance improve by two to seven times
The AIM migration agent in Snowflake CoCo saved us a tremendous amount of time
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 impact15/25
    1 metric 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
    4 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

    Order.co customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/order-co/
    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 11, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Sep 11, 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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