Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Real-Time M&A Intelligence for 18,000+ Dealerships
Streamlining data operations at a leading automotive marketplace—migrating 100+ pipelines to run efficiently and achieve a critical speed increase of ~62%.
Industry
Automotive Marketplace
Region
North America
Technology
Snowflake
Executive Summary
A leading North American online automotive marketplaces company needed a high-performance data platform to unify siloed sources, ensure speed delivery of revenue-critical reports, and unlock advanced analytics. Shorthills designed and built a modern data stack that ingests from 100+ pipelines (internal DBs, third-party analytics, call-tracking, social), processes data with PySpark, lands it in an AWS S3 data lake, and serves analysis from Snowflake—fully orchestrated by Apache Airflow and secured via AWS VPC/VPN. As a result, daily processing dropped from 8+ hours to <3 hours (≈62% faster), reports now land reliably by 3:00 AM EST, and the platform scales to 1.5 TB/day (billions of records). Teams gained self-service access, fault tolerance, and a single source of truth to power reporting and future machine learning (ML) initiatives.
Tech Stack
Snowflake
PySpark
Apache Airflow
AWS (S3)
MariaDB
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Tech Stack
Databricks | Python (Django) | React | AWS S3 | Gemini
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Executive Summary
A leading North American online automotive marketplaces company needed a high-performance data platform to unify siloed sources, ensure speed delivery of revenue-critical reports, and unlock advanced analytics. Shorthills designed and built a modern data stack that ingests from 100+ pipelines (internal DBs, third-party analytics, call-tracking, social), processes data with PySpark, lands it in an AWS S3 data lake, and serves analysis from Snowflake—fully orchestrated by Apache Airflow and secured via AWS VPC/VPN. As a result, daily processing dropped from 8+ hours to <3 hours (≈62% faster), reports now land reliably by 3:00 AM EST, and the platform scales to 1.5 TB/day (billions of records). Teams gained self-service access, fault tolerance, and a single source of truth to power reporting and future machine learning (ML) initiatives.
Tech Stack
Snowflake
PySpark
Apache Airflow
AWS (S3)
MariaDB
Challenges
Data silos & inefficiency
Fragmented sources (internal DBs, Adobe Analytics, call tracking, social) slowed consolidation.
Slow, fragile processing
Legacy Redshift scripts ran 8+ hours and often failed, requiring manual fixes.
Limited scale & innovation
Volume (1.5 TB/day) blocked timely reporting and stalled ML/predictive use cases.
Automotive marketplaces juggle dozens of fast-moving data sources across analytics, ads, calls, and inventory. Siloed feeds and fragile overnight pipelines slow reporting, miss SLAs, and stall ML initiatives—raising costs and decreasing growth. Without a scalable, resilient platform, teams can’t deliver reliable, early-morning insights.
Our Solutions
Data Foundation: Lakehouse & Entity Resolution
We stood up a Databricks-powered lakehouse with medallion layers (bronze → silver → gold) and survivorship rules to reconcile conflicts. Fuzzy matching plus brand/state heuristics created a durable golden dealer record across renames, mergers, and closures—an analytics-ready backbone with end-to-end lineage.
Signals & Feature Engineering
On unified records, we built a reusable catalog of 150+ signals per dealership spanning performance, market, and macro indicators. Features are standardized across brands/states and versioned over time, so valuations, forecasts, and benchmarks stay fair and reproducible.
Valuation & Forecasting Engines
A model suite blends store performance with market signals to produce explainable valuations and forward-looking forecasts. Scenario/sensitivity views test brand, geography, and macro assumptions—accelerating buy/no-buy calls with consistent methodology.
Delivery Experience: Analyst App for M&A Workflows
A secure analytics app streamlines real M&A tasks: search/filter/compare, geospatial views, and exportable diligence summaries. Built on governed tables and shared definitions, it keeps every stakeholder aligned—from board decks to deep dives.
What Shorthills AI Did
We brought numerous fast-moving data feeds into one reliable flow, cleaned and standardized them, and set up smart scheduling so overnight jobs run quickly and finish on time. Teams get a single, trusted source for reports and analysis, with built-in checks and alerts to catch issues early. The result is faster daily processing, timely reports, and a platform that scales as data grows.
Ingestion at scale (100+ pipelines)
We connected internal MariaDB, Adobe Analytics, call-tracking (Marchex), social, and vendor APIs; automated extraction feeds a governed, lineage-aware pipeline.
Compute & storage architecture
PySpark transforms, standardize and prepare data; processed outputs are stored in AWS S3 data lake and are loaded into Snowflake (hybrid star/snowflake schemas) for fast analytics.
Orchestration & security
Our system condenses lengthy documents or sections into concise, structured summaries on demand, that highlight arguments, facts, and precedents—standardizing first-pass review across teams.
Overview
A leading automotive advisory firm that provides M&A and investment insights for the U.S. car dealership market struggled to leverage its raw data, coming from over 18,000 dealerships spanning decades. Each record had roughly 150 fields drawn from Polk, Helix, demographic and population datasets and other open sources and APIs. This had issues of inconsistent formats, missing common identifiers that prevented easy merging, and large gaps. These problems slowed extraction of actionable insights: full data refreshes took more than a week and blocked timely, strategic decisions such as dealership valuations.
To resolve the client's data challenges, Shorthills AI developed JumpIQ, an AI-powered platform that ingests and processes raw data from Polk, Helix, and other open APIs directly into Databricks. A robust data engineering pipeline was built for intelligent merging (using techniques like fuzzy matching and address normalization), cleaning, mapping, and formatting to create a unified “golden record” for each dealership. On this refined data foundation, advanced AI/ML models were deployed for predictive analytics, including revenue forecasting, sales efficiency, dealership valuation, and performance scoring—all accessible through a web-based dashboard offering detailed analytical reports and visual insights.
As a result, the client reduced data processing time from over a week to just 8 hours, gained a single clean and accurate database, and obtained significantly stronger predictive insights that enable faster, more confident strategic decisions.
Outcomes
A leading North American automotive marketplace was missing early-morning reporting windows because siloed feeds and daily runs took 8+ hours and often failed. With Shorthills AI’s modernized data operations, daily processing now finishes in under 3 hours—about 62% faster—so dealer and internal reports land reliably by 3:00 AM EST. The platform handles ~1.5 TB/day with fault tolerance, reducing manual fixes and late-night firefights. A single, standardized data model cuts rework and disputes, while self-service access speeds analysis across teams. Costs drop as failures and reruns decrease, and the business finally has a stable foundation for advanced analytics and ML. In short, pipelines moved from slow and brittle to fast, reliable, and ready for growth.
Processing time ↓ ~62%
Daily runs cut from 8+ hours to <3 hours; deadlines met reliably.
Timely reports
Dealer and internal reports delivered by 3 AM EST—on time, every day.
Scale, stability, and cost efficiency
Handles 1.5 TB/day with fault tolerance; less manual rework; single source of truth for analytics and ML.
Frequently Asked Questions
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