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Your data already tells the story; nested JSON just hides the plot.
In Snowflake, most modern customer, product, and event data lands as VARIANT columns full of JSON. That’s perfect for ingestion speed, but painful when a sales leader wants a clean list of accounts, or a marketing manager needs campaign events by day. The FLATTEN table function in Snowflake turns those tangled arrays and objects into tidy rows and columns, so you can query them with standard SQL, join to other tables, and confidently feed models or dashboards.
By flattening JSON in Snowflake, you unlock:
Now imagine you never again have to click through the console, paste queries, download CSVs, and upload them into Google Sheets. That is where delegation to an AI agent becomes transformative. Instead of a human analyst babysitting every run, an AI agent executes the same Snowflake FLATTEN logic, validates row counts, refreshes your Google Sheets tabs, and notifies stakeholders when it’s done. You get the reliability of a production pipeline with the flexibility of ad‑hoc analysis, and your team finally steps away from the keyboard to work on strategy instead of plumbing.
JSON is everywhere in modern data stacks: product events, marketing logs, CRM webhooks. In Snowflake, that usually means big VARIANT columns that are powerful but hard to report on. The FLATTEN table function is your bridge from messy JSON to clean, tabular data you can send straight into Google Sheets. Let’s walk through three layers of maturity:
Throughout, keep Snowflake’s FLATTEN docs handy: https://docs.snowflake.com/en/sql-reference/functions/flatten and Google Sheets help here: https://support.google.com/docs.
When to use: quick exploration, ad-hoc questions.
Steps:
SELECT * FROM your_table LIMIT 10; and note the VARIANT column (e.g. payload).SELECT
t.id,
f.value AS event
FROM your_table t,
LATERAL FLATTEN(input => t.payload:events) f;
This explodes the events array into multiple rows.PATH or direct JSON path access:SELECT
t.id,
f.value:event_type::string AS event_type,
f.value:timestamp::timestamp AS ts
FROM your_table t,
LATERAL FLATTEN(input => t.payload:events) f;
Pros: Fast to start; great for discovery.
Cons: Manual, error-prone, no scheduling, no direct link to Sheets.
When JSON is messy or deeply nested, use FLATTEN’s options (see: https://docs.snowflake.com/en/sql-reference/functions/flatten#usage-notes):
OUTER => TRUE keeps rows even when the path doesn’t exist, filling NULLs.RECURSIVE => TRUE walks nested structures.MODE controls what to flatten: OBJECT, ARRAY, or BOTH.
Example:
SELECT
t.id,
f.path,
f.index,
f.value
FROM your_table t,
LATERAL FLATTEN(
input => t.payload,
recursive => TRUE,
mode => 'BOTH'
) f;
This is helpful when you’re reverse-engineering unfamiliar JSON.
When to use: repeatable but still human-triggered workflows.
CREATE OR REPLACE VIEW v_events_flat AS
SELECT
t.id,
f.value:event_id::string AS event_id,
f.value:user_id::string AS user_id,
f.value:campaign::string AS campaign,
f.value:created_at::timestamp AS created_at
FROM raw.events t,
LATERAL FLATTEN(input => t.payload:events) f;
Pros: Central logic lives in one place; easier to maintain.
Cons: Still manual exports and refreshes; no built-in Google Sheets sync.
As your team grows, you don’t want analysts downloading CSVs daily. No-code tools can call Snowflake on a schedule and push results into Google Sheets with minimal engineering.
Use a data connector or BI tool that supports Snowflake and Google Sheets. Many let you schedule exports from a SQL query or view.
Typical setup:
v_events_flat).SELECT *
FROM v_events_flat
WHERE created_at >= dateadd(day, -7, current_timestamp());
Pros: No code; business teams can manage; Sheets is always current.
Cons: Limited flexibility around branching logic, QA checks, or multi-step workflows.
Google provides ways to pull data directly into Sheets.
You can:
Pros: Lives entirely in Sheets; great for sales or marketing managers.
Cons: Harder to manage complex JSON logic or multi-table joins outside Snowflake; Sheets becomes the bottleneck for heavy data volumes.
When JSON volumes grow and requirements change weekly, you need something smarter than fixed schedules. This is where an AI agent such as Simular Pro becomes your digital data analyst.
Simular Pro agents can:
Workflow concept: The agent behaves like a trained ops analyst.
Steps:
CREATE OR REPLACE VIEW v_events_flat AS ... (if not created).v_events_flat view.”
Pros:
Cons:
Beyond scheduled jobs, AI agents shine when JSON structures evolve.
Example: You add new event properties to your JSON. Instead of a human spelunking through the structure, the agent can:
FLATTEN with RECURSIVE => TRUE to map out keys and paths.This turns what used to be a half-day of trial-and-error into a few supervised agent runs.
Start at the layer that fits today, but design your Snowflake FLATTEN logic (views and queries) so an AI agent can take over tomorrow without rewriting everything.
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