How to Query Nested JSON API Responses with SQL Directly in the Browser
Modern APIs return deeply nested JSON that's painful to parse in spreadsheets. This developer-focused guide demonstrates converting API response dumps into queryable tables and running SQL with JOINs, aggregations, and window functions — all without spinning up a local database.
Why this matters?
Developers debugging API responses or analysts working with exported JSON data (from tools like Segment, Mixpanel, or internal APIs) often need to filter and aggregate nested structures. Spinning up Postgres or writing Python scripts for a one-off analysis is overkill when DuckDB-Wasm can do it in the browser.
The 3-Step Solution
Follow this streamlined workflow to transform your raw data export into a clean, analysis-ready dataset. Each step leverages our browser-based tools to ensure your sensitive data never leaves your device.
By following these three steps, you eliminate manual data wrangling, reduce human error, and maintain full GDPR compliance throughout the process.
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Related Workflows
Isolate True Net Processing Fees from Stripe Balance Transactions
Stripe's Payouts export only shows the lump-sum deposit hitting your bank account — it tells you nothing about the per-transaction fee composition. To calculate your real effective processing rate, you need the Balance_Transactions.csv file, but this export dumps charges, refunds, adjustments, dispute reversals, and Stripe billing fees into a single flat table with no hierarchy. The Fee column contains negative values for processing fees, but refund-related fee reversals and dispute charges are interspersed without clear grouping, making a simple SUM() wildly inaccurate. This workflow filters the Type column to isolate charge, payment, refund, and dispute rows separately, recalculates the net fee per original transaction by grouping on Source ID, and produces a clean summary showing your true blended rate.
Reconcile Stripe Disputes and Refunds Against Original Charges
Annual Stripe exports for mid-volume merchants routinely exceed 500,000 rows, mixing successful charges, partial refunds, full refunds, dispute creations, dispute wins, and dispute losses in one monolithic CSV. The Type column uses cryptic values like dispute, dispute_reversal, and refund without linking back to the original charge amount in the same row. You must trace each dispute or refund back to its Source ID to find the original charge row and calculate net revenue impact. This workflow filters the export to only dispute and refund event types, joins them back to original charge rows using Source ID as the foreign key, and computes the net realized revenue per order after all adjustments.
Run Ad-Hoc SQL Queries on CSV Files Without Database Setup
When a stakeholder asks 'how many orders from California had a discount code in Q3?', the traditional workflow demands spinning up PostgreSQL, writing CREATE TABLE DDL statements, configuring COPY commands to import the CSV, and only then writing your SELECT query. For a one-off validation question, this environment setup takes 30-45 minutes of friction that discourages exploratory analysis. This tool registers your CSV file as a virtual table in DuckDB-Wasm running entirely in your browser. You write standard SQL — SELECT, WHERE, GROUP BY, JOIN, window functions — and get results in seconds. Schema inference handles type detection automatically, though you can override with explicit CAST operations when needed. The data never leaves your machine.
Join Massive CSV Files Locally Without Silent Data Loss
Merging two CSV files with millions of rows — like joining a transactions.csv against a customers.csv on customer_id — seems straightforward until you hit Excel's row limit or VLOOKUP's performance cliff. Excel becomes unresponsive around 500K rows, eventually showing 'Process not responding' before crashing entirely. Python pandas handles the scale but introduces a subtler failure: automatic schema inference. If one file has customer_id values like '00123', '00456' (parsed as strings preserving leading zeros) and the other has 123, 456 (parsed as integers), a LEFT JOIN produces Unexpected NULL for every row where the types don't match. You lose data silently with no error message. This tool lets you write explicit SQL JOINs with CAST operations to enforce type consistency, running entirely in-browser via DuckDB-Wasm.