Event Stream Ingestion
The live events flowing through your systems — clicks, orders, sensor readings — land safely in your data tables: recorded exactly once, checked at the door, with a lost-and-found for anything malformed.
Think of a mailroom handling a constant river of letters. Each letter is stamped in exactly once — never lost, never counted twice — anything with a bad address goes to a lost-and-found shelf instead of the trash, and a clock on the wall shows how far behind the sorting is. Boring, and everything upstairs depends on it.
The problem, in plain words
Your systems produce a constant stream of events — every order, click, and status change — flowing through a message pipeline (Kafka, Kinesis, or Pulsar). To be useful for reports or AI, those events have to land in your data tables. Today, that landing is a homegrown script written by someone three jobs ago. When it crashes mid-batch, some events load twice and Tuesday's totals are mysteriously high. When a source team renames a field, the script loads empty values for a week before anyone notices. And when you ask 'how fresh is this data?', the honest answer is 'probably fine?'
What we set up
We replace the homegrown loader with a contracted landing path. Stream processors (Flink, Spark Structured Streaming, or Kafka Connect) write events into the shared tables (Iceberg or Delta) with exactly-once delivery — a setup where each event is recorded one time, even if something crashes and retries midway. A schema registry (Confluent or Apicurio — a gatekeeper that knows what each event is supposed to look like) checks every event at the door. Events that don't match go to a dead-letter table (a lost-and-found for bad records) instead of vanishing, and the source's owner is notified. A freshness gauge per stream shows exactly how far behind the data is.
How it works, step by step
- An event happens
An order is placed, a sensor reports, a status flips. It enters the stream immediately.
- The gatekeeper checks its shape
Does it match the agreed format? Field types, required fields, no surprises. Schema changes happen deliberately, not by accident.
- Good events land exactly once
The processor writes to the shared tables with crash-safe bookkeeping — a retry never creates a duplicate, a failure never loses a record.
- Bad events go to lost-and-found
Malformed records land in a dead-letter table with a note about what was wrong, and the team that owns the source gets pinged.
- Freshness is measured, not guessed
Every stream reports how far behind it is, on the same monitoring as everything else. 'Is this current?' has a numeric answer.
What changes for you
Before: real-time data is a black box that works until it very quietly doesn't. After: streams are first-class sources in your data catalog, with a named owner, a checked format, a visible freshness gauge, and a lost-and-found you can inspect. Duplicates and silent gaps stop being a quarterly surprise. What it won't do: it won't catch values that are wrong but well-formed. If a source sends a price of zero in a perfectly valid event, it will land — catching that is the job of data quality gates, one step downstream.