To construct apps serving the plenty, firms should develop resilient techniques that deal with all the info effectively and use it to empower their enterprise choices and enhance the end-user expertise. In such circumstances, the info crew, with its progressive approaches to dealing with and processing knowledge, does the heavy lifting.
On the Information Engineering Summit 2025, Abhinav Raghuvanshi, Zepto’s affiliate director of information engineering, addressed the corporate’s problem in managing knowledge: How can knowledge be delivered as shortly as meals? For a corporation that established the 10-minute supply mannequin, real-time visibility isn’t just advantageous, however is important for profitable execution, forecasting, and enhancing buyer expertise.
The discuss, “From Warehouse to Lakehouse: The Way forward for Actual-Time Information at Zepto,” traced how the info platform developed from a single Redshift warehouse to a hybrid structure powering operational analytics and application-grade real-time techniques. Alongside the best way, Zepto needed to rethink ingestion, transformation, price governance, and the way it dealt with SQL queries for the backend.
When Redshift Wasn’t Quick Sufficient
Zepto’s authentic setup relied on Redshift as its central warehouse. It labored, till it didn’t. Question bottlenecks, stale reporting, and lengthy wait occasions uncovered cracks. “We gave a type of patient-facing design to Redshift,” Raghuvanshi stated, “With the amount we had been getting, it was changing into unimaginable to serve knowledge in close to actuality.”
Including compute nodes or partitioning tables solely went to this point. Question collisions, a scarcity of separation between storage and compute, and expensive I/O pushed the crew to rethink. Citing an instance, Raghuvanshi stated, if somebody had been to run a really inefficient question on the Redshift cluster, they’d discover that this strategy can be fairly gradual for them.
So started the shift to a contemporary structure with S3 because the central storage layer, Kafka for streaming ingestion, and Databricks (powered by Apache Spark and Photon) for transformation and orchestration. He defined, PII columns are stripped early, enterprise logic is layered via a structured knowledge pipeline, and each job is encrypted and scheduled utilizing Apache Airflow.
Zepto additionally made a shift to MongoDB to cut back latency lately. It will likely be attention-grabbing to see what they do subsequent.
Information Democratised, Governance Intact
The system isn’t simply real-time, it’s real-access. Zepto helps almost 400 analysts throughout enterprise, product, and operations. To make sure usability, the engineering crew constructed a low-code in-house framework the place customers can merely drop in SQL queries. “Most analysts are very comfy with SQL queries,” Raghuvanshi defined. “We put in abstractions when it comes to transformations they need to do.”
Every crew operates inside price boundaries because of dbt catalog tags, role-based entry, and job-level funds dashboards. Groups get alerted in the event that they’re burning via compute, and reporting workloads are sandboxed to keep away from collisions. “As an alternative of worrying about when the info would arrive,” he famous, “it offers extra management to the end-users when it comes to what frequencies of information they need to eat.”
The lakehouse mannequin, underpinned by S3 and Databricks, now helps historic queries, snapshots, and time-series analytics. Snapshots can reconstruct a retailer’s stock state from months in the past, and they’re useful for audits, restocking predictions, or different analyses.
ClickHouse for Actual-Time Analytics
From a enterprise mannequin perspective, Zepto utilises darkish shops, which function central hubs for objects like ice cream. Sustaining optimum circumstances, corresponding to temperature, requires real-time monitoring of varied metrics in milliseconds to make sure product security and high quality. Moreover, monitoring supply routes necessitates IoT knowledge integration for real-time reporting. This rapid suggestions is essential for environment friendly operations, guaranteeing well timed retailer visits and stopping delays.
Raghuvanshi talked about utilizing ClickHouse for that. The Iot system captures all these metrics and places them into ClickHouse for real-time evaluation, reporting, and monitoring.
ClickHouse powers Zepto’s production-facing real-time analytics that embrace order drop charges, visitors circulation, IoT metrics from storage amenities, and even fridge temperature monitoring. It helps MergeTree engines—replicated, changing, summing—every designed for various workloads. “In case your knowledge is immutable, be at liberty to simply dump it into ClickHouse,” he stated.
To simplify entry, the crew wrapped Flink with a SQL-friendly interface, enabling analysts to plug queries into Kafka sources with out writing code. Outcomes are routed into ClickHouse, the place apps can question metrics inside milliseconds.
ClickHouse isn’t just a database, it’s a monitoring nerve centre. Retailer-level metrics, stock depletion, and fulfilment charges—all now replace in close to real-time. “Let’s say in case your milk in a retailer at 10 a.m. is getting exhausted,” he stated, “you need the milk to be distributed in shops by 2 p.m. You’d have the info in real-time.”
Future Roadmap
Zepto is seeking to allow extra expressive queries in real-time. Instruments like StarRocks and different On-line Analytical Processing (OLAP) engines are being evaluated to deal with complicated joins with out latency penalties. “It would take a 12 months and a half for StarRocks to be prepared for self-improvement,” Raghuvanshi famous.
The bigger purpose is to make real-time intelligence as accessible as historic reporting, with out anticipating each enterprise consumer to grow to be a techniques engineer. With a system like Zepto’s, he famous, “Now folks can concentrate on constructing extra knowledge merchandise out to the ecosystem of particular person customers”.
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