Use Case Intelligence RSS Search use cases Search
Databricks Agriculture India

UPL

Enterprise Knowledge Search · Supply Chain Optimization

UPL, a global leader in sustainable agriculture, is dedicated to securing the world’s food supply across 138 countries by enhancing farm productivity and profitability through innovative solutions. Due to their rapid growth, UPL faced significant challenges in managing and analyzing vast amounts of internal and third-party data sources.

Read the original story Explore all use cases
20 Countries where UPL leverages Databricks to provide AI solutions
From family roots in Mumbai in the 60s, UPL Group is now a global leader in sustainable agriculture, creating innovative solutions that span the entire food chain and beyond.
Looking ahead, UPL plans to further scale their AI solutions, intending to expand their deployment from 20 to 50 countries.
Optimizing agriculture supply chains across 20 countries and counting

Reported impact

Business outcome

Reported outcomes include 20 Countries where UPL leverages Databricks to provide AI solutions; From family roots in Mumbai in the 60s, UPL Group is now a global leader in sustainable agriculture, creating innovative solutions that span the entire food chain and beyond.; Today, they operate in over 130 countries, balancing performance with sustainability through their four pure-play platforms that include UPL Corporat

  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

UPL built its Databricks implementation around Databricks SQL, Delta Lake, Delta Sharing, Apache Spark Declarative Pipelines. UPL, a global leader in sustainable agriculture, is dedicated to securing the world’s food supply across 138 countries by enhancing farm productivity and profitability through innovative solutions. Due to their rapid growth, UPL faced significant challenges in managing and analyzing vast amounts of internal and third-party data sources. Databricks SQL serves governed analytics, dashboards, and ad hoc queries for business users and operational teams. Delta Lake stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning. Delta Sharing shares governed datasets with external partners and downstream consumers. Apache Spark Declarative Pipelines builds transformation pipelines over shared data with Spark-based processing.

Data flow

Source data is ingested into databricks, curated in Delta Lake tables, processed with Databricks compute and orchestration services, and served through analytics, semantic search, or model endpoints for downstream users and applications. It connects with SAP BW, SAP, Salesforce.

Solution architecture

6 components · 5 layers
  1. Compute
    • Apache Spark Declarative Pipelines Builds transformation pipelines over shared data with Spark-based processing.
  2. Storage
    • Delta Lake Stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning.
  3. Serving
    • Databricks SQL Serves governed analytics, dashboards, and ad hoc queries for business users and operational teams.
    • Delta Sharing Shares governed datasets with external partners and downstream consumers.
  4. Governance
    • Unity Catalog Applies centralized governance, lineage, auditing, and access control across shared data assets.
  5. Uncategorized
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment.

Architecture clues

  • Apache Spark Declarative Pipelines
  • Countries where UPL leverages Databricks to provide AI solutions
  • Databricks SQL
  • Delta Lake
  • Delta Sharing
  • serverless

Evidence from the source

20 Countries where UPL leverages Databricks to provide AI solutions
From family roots in Mumbai in the 60s, UPL Group is now a global leader in sustainable agriculture, creating innovative solutions that span the entire food chain and beyond.
However, UPL faced significant challenges in managing vast amounts of data from diverse sources such as SAP, SAP BW, Salesforce, plant IoT data and external third-party data — including weather, regulatory and microeconomic information. “In
The complexity of demand forecasting in agriculture, influenced by numerous external variables and the critical timing of product applications, further complicated UPL’s data challenges. For instance, misjudging demand could result in spoil
The demand forecasting model couldn’t have been built without Databricks Unity Catalog. This component democratizes data across the organization, making it accessible and usable for a wide range of stakeholders.
UPL relies on Databricks SQL, a serverless SQL engine, because, according to Rao, “Data pool efficiency is important to us. The SQL capabilities provided by Databricks let us process and analyze data quickly to support timely and accurate d
Transparency Evidence & methodology 100/100 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 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
    4 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
    6 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

    UPL customer story

    First-party
    https://www.databricks.com/customers/upl
    Source type
    Vendor-published customer story
    Published
    Aug 22, 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 Aug 22, 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.

Related

Similar use cases

See all like this
Databricks Aug 28, 2025 Accuris Software / SaaS United States Accuris oversees one of the world’s most extensive catalogs of electronic components, each component is enriched with structured and unstructured data such as parametric data, environmental attributes, pricing and… 10x Faster development velocity from vision to customer-facing features Enterprise Knowledge SearchSupply Chain Optimization Agent Bricks · Apache Spark Declarative Pipelines · Databricks Data + AI Platform Databricks Aug 28, 2023 Anker Manufacturing China Anker is pioneering power delivery technology to charge phones, tablets and laptops at unprecedented speeds, whether at home, in the car or on the go. Key to their ability to improve operations and margins is the use of… 1,000+ BI reports and dashboards Enterprise Knowledge SearchSupply Chain Optimization Agent Bricks · Anker Innovations Technology · Delta Lake NVIDIA Feb 2, 2026 NVIDIA Software / SaaS Global NVIDIA needed to move employee-built chatbots and agents out of shadow-AI pilots and onto a unified, secure, high-performance platform that could support hundreds of governed AI workflows across engineering, software… from 15 hours to one hour Enterprise Knowledge SearchIT Operations Automation NVIDIA AI Blueprint for Retrieval-Augmented Generation (RAG) · NVIDIA AI Enterprise · NVIDIA GPU Operator