ClickStream Streaming.
Replacing brittle overnight batch jobs with a real-time streaming backbone — cutting end-to-end data latency from 24 hours to under 5 minutes across the entire pipeline.
The Chaos.
Overnight batch jobs left every dashboard and decision running on day-old data, and the process got slower, heavier, and less trusted as volume grew.
Filtering or pulling data at specific intervals was severely limited, and keeping pace meant standing up more databases and more infrastructure — driving costs higher every quarter.
The System.
We built an event-streaming backbone on Amazon Kinesis, with sharded streams feeding containerized consumers on AWS ECS that transform and load into a Snowflake warehouse within seconds of arrival.
SnowDDL manages the entire Snowflake schema as code — tables, roles, and grants stay consistent and reviewable — while idempotent consumers guarantee no duplicates or loss, and an Express API exposes the live data to every dashboard.
The Stack.
Engineered for real-time scale.
Execution Process.
01. Discovery & Audit
Profiled the existing batch jobs, mapped data dependencies, and identified the latency-critical paths driving business decisions.
02. Streaming Architecture
Designed the Kinesis streams — shard strategy and partition keys — with containerized consumers on AWS ECS that scale independently and process records in order.
03. Schema & Load
Modeled the Snowflake warehouse with SnowDDL managing schema, roles, and grants as code, while consumers transform and load events within seconds of arrival.
04. Cutover & API
Ran the new pipeline in parallel, validated parity, then cut over with zero data loss and shipped the Express API powering live, real-time dashboards.
Turn Complexity Into Advantage.
If your data is scattered, your systems don't talk, or your AI roadmap is stuck in prototype mode, we'll help you identify the fastest path to a production system that pays for itself. Free architecture review included.
Trusted by teams modernizing data, AI, and operations
