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Databricks Financial Services United States

RDSolutions

Data Lake / Data Warehouse Modernization · Real-time Analytics · Security and Compliance

Their legacy data pipeline faced scalability, maintenance and slow data processing issues, causing delays in time to insights. The Databricks Data + AI Platform resolved these challenges with a unified lakehouse and automated scalable data pipelines, improving data processing speed and reducing latency.

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97% faster data processing (from 4 days to 3 hours)
A pop-up offers assistance with an error, and nine times out of 10, you click ‘yes,’ and the assistant makes everything perfect with the click of that button.
RDSolutions conducts 65,000+ online audits and thousands of weekly in-store audits to normalize and collect more than 500 billion data points weekly.

Reported impact

Business outcome

RDSolutions achieved measurable business impact on Databricks, including 97% faster data processing (from 4 days to 3 hours); RDSolutions conducts 65,000+ online audits and thousands of weekly in-store audits to normalize and collect more than 500 billion data points weekly.; A pop-up offers assistance with an error, and nine times out of 10, you click ‘yes,’ and the assistant makes everything perfect with the click of that button..

  • Compliance
  • Cost Reduction
  • Productivity Improvement

Solution

RDSolutions provides retail pricing intelligence, collecting billions of data points daily. Their legacy data pipeline faced scalability, maintenance and slow data processing issues, causing delays in time to insights. The Databricks Data + AI Platform resolved these challenges with a unified lakehouse and automated scalable data pipelines, improving data processing speed and reducing latency. The use of Databricks Assistant helped to automate time-consuming development tasks, resulting in faster data ingestion, improved accuracy, increased efficiency and the ability for RDSolutions to deliver timely, actionable insights to their customers. Andy Featherstone, Manager of Data Engineering at RDSolutions, said, “My CEO’s goal is to scrape the internet.

Data flow

Unstructured documents and reference data lands in Databricks and is transformed into curated datasets stored in Delta Lake under Unity Catalog governance

Solution architecture

4 components · 4 layers
  1. Compute
    • Agent Bricks Supports agentic or GenAI applications that RDSolutions uses to automate or accelerate work.
  2. Storage
    • Delta Lake Stores RDSolutions's curated datasets for analytics, machine learning, and operational use cases.
  3. Orchestration
    • Databricks Assistant Automates repetitive engineering tasks for RDSolutions's development teams.
  4. Governance
    • Unity Catalog Provides access control, governance, and lineage across RDSolutions's shared data products and AI workloads.

Architecture clues

  • Cloud: Azure
  • Key product: Agent Bricks
  • Key product: Databricks Assistant
  • Key product: Delta Lake
  • Key product: Unity Catalog

Evidence from the source

97% faster data processing (from 4 days to 3 hours)
A pop-up offers assistance with an error, and nine times out of 10, you click ‘yes,’ and the assistant makes everything perfect with the click of that button.
Andy Featherstone, Manager of Data Engineering at RDSolutions, said, “My CEO’s goal is to scrape the internet.
RDSolutions conducts 65,000+ online audits and thousands of weekly in-store audits to normalize and collect more than 500 billion data points weekly.
RDSolutions provides retail pricing intelligence, collecting billions of data points daily.
Transparency Evidence & methodology 95/100 reference value · 89% 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
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Databricks

    RDSolutions customer story

    First-party
    https://www.databricks.com/customers/rdsolutions
    Source type
    Vendor-published customer story
    Published
    Nov 7, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Nov 7, 2024 · Databricks · First-party
    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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