Real-Time Analytics Platform

Who produces events, who consumes analytics, and which enterprise systems the platform depends on — the boundary of scope with no internal components shown.

The end-to-end shape in one picture: managed ingestion into an Event Hubs durable log, Databricks stream processing, and Azure Data Explorer as the hot analytics store behind a governed query surface.

What depends on what. Dependencies point downward only; platform services are consumed by every layer above them.

The deployable units, the technology each runs on, and the protocol on every hop — what an engineering team actually builds and owns.

Every way the platform touches another system, with protocol, direction and cadence — the single page an enterprise architect can review against the estate.

Where the tenant boundary sits at each layer, and how one tenant's traffic or one team's ad-hoc query is prevented from degrading everybody else.

Where data comes from, what format and cadence it moves in, what is retained at each hop, and where an event goes when it cannot be processed.

Every store the platform owns, grouped by who owns it and whether it can be rebuilt — the view that answers what happens if a given store is lost.

The tables the hot store actually holds, their keys and cardinality — including the dead-letter and schema-contract tables that most models omit.

How an event schema changes without a redeployment and without breaking a consumer — the lane-by-lane path from proposal to retirement.

What each micro-batch does, stage by stage, and exactly where the delivery guarantee is established.

Each window type against what state it keeps, when it emits, and what happens to an event that arrives after its window has closed.

One event from SDK call to dashboard pixel, with the latency budget spent at each hop against the 5-second p95 target.

How a query reaches an answer in under 2 seconds while dashboards, ad-hoc analysis and exports run concurrently without competing.

How a defect discovered after the fact is corrected without taking the platform down or letting a consumer read a half-corrected table.

What runs where, which failure domains it spans, and what is actually standing by in the secondary region.

How a change reaches production, what stops a bad one at each gate, and how a streaming job is released without skipping an event.

Which signal is collected at which stage, and which of them will page somebody — the matrix an SRE reads before accepting the service.

The loop the platform runs when something goes wrong, and the point at which retained raw events turn a detection into a recovery.

Where the trust boundaries are, what crosses each one and under what authentication, and where an attacker with a stolen credential actually arrives.

Who proves what, to whom, in what order — for a machine producer publishing events and a human analyst running a query.