Apache Kafkaยฎ๏ธ ๋น„์šฉ ์ ˆ๊ฐ ๋ฐฉ๋ฒ• ๋ฐ ์ตœ์ ์˜ ๋น„์šฉ ์„ค๊ณ„ ์•ˆ๋‚ด ์›จ๋น„๋‚˜ | ์ž์„ธํžˆ ์•Œ์•„๋ณด๋ ค๋ฉด ์ง€๊ธˆ ๋“ฑ๋กํ•˜์„ธ์š”

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LIC Transforms Dairy With Real-Time Data

10,500 farmers supported with real-time insights

Batch pipelines replaced with unified data streaming

Data-driven insights on herd health and breeding

โ€œWe wanted to evolve from traditional ETL and batch processes to a unified streaming platform that empowers real-time insights and faster outcomes.โ€

Vik Mohan

Principal Technologist, LIC

Livestock Improvement Corporation (LIC), a farmer-owned New Zealand co-op, realized it needed to reimagine its data strategy to better serve dairy farmers nationwide.

With 10,500 farmer shareholders, LIC generates vast volumes of dataโ€”from milk analysis to animal geneticsโ€”and its legacy pipelines couldnโ€™t keep up. Complex, multi-hop architecture, delayed insights, fragmented views, limited support, and disconnected systems strained the ability of farmers to get the relevant information they needed to run their operations.

โ€œWe wanted to evolve from traditional ETL and batch processes to a unified streaming platform that empowers real-time insights and faster outcomes,โ€ explained Vik Mohan, LIC Principal Technologist.

The Challenge: Herds of Data, Siloed and Stalled

As LIC grew across R&D labs, farms, and international operations, so did its data sprawl. Insights critical to herd health, breeding decisions, and productivity were trapped across disconnected systems. This meant:

  • Data silos slowed insight delivery

  • Batch processing delayed decisions

  • Fragmented systems hindered proactive farmer support

The Vision: A Unified Streaming Platform

To eliminate delays and build a single source of truth, LIC transitioned to an event-driven architecture powered by Confluent. The goal? Deliver transformed, trusted, and real-time data to every corner of the organizationโ€”securely and at scale.

โ€œWeโ€™re shifting from multi-hop batch flows to real-time stream processing,โ€ Mohan noted. โ€œThis helps us respond faster and serve our farmers better.โ€

From Multi-Hop to Real-Time Flow

LIC replaced sequential batch layers with a unified streaming pipeline, using Confluent Cloud and Apache Kafkaยฎ. Now, events flow continuouslyโ€”no more intermediate landing zones, no more lag. Business wins include:

  • Real-time herd health alerts

  • Timely breeding insights

  • Scalable integration with R&D and partner systems

With Apache Flinkยฎ, raw data is transformed on the flyโ€”cleaned, enriched, and published for immediate use across systems.

โ€œWe built traceable, reusable business events and a common data model that any team or system can access,โ€ Mohan added.

The Impact: Stronger Farms, Smarter Decisions

The transformation is already driving measurable outcomes, including faster, data-driven recommendations to farmers, reduced redundancy through centralized pipelines, and greater agility across services and operations. And with a shared streaming backbone, LIC can now federate partner data, accelerate innovation, and scale support without scaling complexity.

์ง€๊ธˆ Confluent ์‹œ์ž‘ํ•˜๊ธฐ

์‹ ๊ทœ ๊ณ„์ • ์ƒ์„ฑ ํ›„ 30์ผ ๋™์•ˆ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” 400๋‹ฌ๋Ÿฌ ์ƒ๋‹น์˜ ํฌ๋ ˆ๋”ง์„ ๋“œ๋ฆฝ๋‹ˆ๋‹ค.

๋” ๋งŽ์€ ๊ณ ๊ฐ ์‚ฌ๋ก€ ๋ณด๊ธฐ

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