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Confluent Retail India

Swiggy

Marketing Personalization · Real-time Analytics

Swiggy needed to replace self-managed Kafka so it could scale delivery operations, predictions, and recommendations during peak demand without spending engineering time on infrastructure management.

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680 cities
over three billion orders

Reported impact

Business outcome

Swiggy reduced infrastructure overhead, kept service stable during demand spikes, and improved customer-facing predictions and recommendations by putting real-time streaming and continuous processing at the center of its delivery platform.

  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Swiggy uses Confluent's fully managed data streaming platform as the event backbone behind its delivery marketplace so customer, restaurant, and delivery-partner decisions can all be informed in real time. The Confluent cluster handles asynchronous communication across the service graph, powering use cases such as predicted delivery SLA, recommendations, analytics, and other data-driven customer interactions. Elastic scaling removes the manual planning and hardware management that were required with open-source Kafka during peak festival demand, while lineage tooling helps the team understand dependencies and correct anti-patterns across internal systems. Swiggy is also layering Apache Flink and built-in connectors onto this backbone so continuous stream processing can keep all three marketplace participants aligned as the company expands. The result is a more resilient operational platform that supports both near-term customer service quality and longer-term growth across India.

Data flow

Order and operational events flow through Confluent across Swiggy's services, where Flink-powered processing derives predicted SLAs, recommendations, and analytics that are then shared with customers, restaurants, and delivery partners in real time.

Solution architecture

2 components · 2 layers
  1. Compute
    • Apache Flink Performs continuous stream processing so customers, restaurants, and delivery partners receive real-time insights.
  2. Uncategorized
    • Confluent Runs Swiggy's managed streaming backbone for operational, analytical, and recommendation data.

Architecture clues

  • Confluent provides lineage visibility to identify anti-patterns and map relationships across internal data systems.
  • Flink-based continuous stream processing helps keep customer, restaurant, and delivery-partner workflows synchronized.
  • The team is using managed streaming to shift effort from hardware management to governance and business outcomes.

Evidence from the source

"Using the Confluent cluster for real-time predictions, recommendations, and analytics, Swiggy tackled a key challenge-managing asynchronous communications across the various services it offers."
"When a customer opens the Swiggy app, the Confluent cluster powers the predicted SLA, providing an estimated delivery time to the customer."
"With Flink powering continuous stream processing behind the scenes, each of these parties can rely on critical, real-time insights to make informed decisions."
"With over three billion orders delivered in 680 cities across India as of 2024, Swiggy has successfully pioneered the hyperlocal commerce industry."
Transparency Evidence & methodology 70/100 reference value · 77% 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 value70/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 specificity8/10
    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. Confluent

    Swiggy customer story

    First-party
    https://www.confluent.io/customers/swiggy/
    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

  • First archived Apr 24, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Oct 11, 2026 · Confluent · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    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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