Connecting EZLynx to Snowflake

Architecture

How EZLynx and Snowflake actually connect

Two systems that know nothing about each other. Everything below exists to give them one shared reference.

1. Step 1

   EZLynx holds the truth

   Comparative raters make quoting cheap and abundant. That makes the quote-to-bind rate the only interesting number, and it differs enormously by traffic source.

2. Step 2

   Snowflake needs a click to match

   A schema both sides agree on, consistent types, and identifiers normalised identically before hashing. The failure mode is not rejection but silent divergence — a timestamp in the wrong zone or a trimmed-versus-untrimmed email produces answers that look plausible.

3. Step 3

   The identifier is the bridge

   Whatever join key both parties agree on, usually a hashed identifier, captured at the visit and carried into the EZLynx quote. Without it there is nothing to join on and no tool fixes that.

4. Step 4

   The event source decides reliability

   Send bound policies at first-year commission. Quote requests are a stage event, not revenue.

5. Step 5

   Deduplication at the destination

   Nothing deduplicates for you. Loads are at-least-once, so a retried pipeline creates duplicate rows and the only protection is a deduplication step you write and actually schedule.

Diagnose it honestly

Working vs. broken, side by side

Both states look identical from the Snowflake dashboard, which is why this runs for months before anyone notices.

Diagnostic sequence

Diagnostic order

Each step rules out a class of cause. Run them in order and the last one usually has a single explanation standing.

1. 1

   Count policys carrying whatever join key both parties agree on, usually a hashed identifier

   The ceiling on everything else. Do this before debugging anything downstream.

2. 2

   Follow one real conversion end to end

   From the ad click through EZLynx and into Snowflake.

3. 3

   Measure quote-to-bind rate by traffic source

   It is the number that separates shoppers who will switch from shoppers who are checking their renewal price.

4. 4

   Write down both parties' conversion definitions before writing any query

   Definition mismatch, not technology, is what makes most partner measurement projects produce numbers neither side trusts.

5. 5

   Read the match rate, not the success message

   Snowflake will not tell you anything is wrong. Verify by querying against each party's own totals and checking for duplicate rows on the join key before trusting any aggregate.

Source-of-truth validation

How it gets proven

Against EZLynx's own records, not against either platform's dashboard.

• Whatever join key both parties agree on, usually a hashed identifier confirmed present on a live policy.
• A real conversion followed from click through EZLynx to Snowflake.
• Row counts and revenue reconciled against each source system for a closed window, with duplicates checked on the join key and both definitions documented.
• Bound policies reconciled at commission against imported conversions, with quote volume reported as a cost.
• Reconciled over a fully closed window, matched per record rather than on totals.
• Re-checked 30 days later.

Straight talk

When you don't need this

Worth reading before paying anyone, including us.

• If Snowflake already reports conversions that reconcile against EZLynx's records within a few percent, this join is working and there is nothing to buy.
• Measure identifier coverage yourself first — pull your last hundred policys and count how many carry whatever join key both parties agree on, usually a hashed identifier. Ten minutes, free, and it tells you whether any of this is worth doing.
• If your bind rate is uniform across sources, optimising on quotes is defensible and learns much faster — but report on bound policies regardless.
• If you have no partner to share data with and one cloud, a warehouse you already run does the same job. The sharing capability is what justifies this one.