I've built a product that completely parses highly nested JSON data in cloud data warehouses. Forge works by methodically dissecting each subcollection and each field of Json data, one by one, and creates 3NF tables for each json sub-object. This completely flattens Json data of any complexity and depth and fully accounts for any schema changes in the entire dataset.

While hand crafted scripts work once and ok for a quick look, a systematic deconstruction and rebuild of the entire Json object is required to truly understand the structure. Some companies have Json data coming from MongoDb or Firestore which has undergone hundreds of even thousands of changes from changing data types to abstract manipulations such as changing Json object to array. A simple parsing script won't cut it. You will either sacrifice some data in order to get something out of it or spend weeks writing dozens of scripts and manipulations to correctly process it. Repeat this for each API and each schema that your company utilizes.

Forge doesn't stop at just unnesting. With the included AI schema classifier, Excalibur, we automatically identify which API your data is coming from based upon tens of thousands of examples. From Stripe to hubspot to segment, we detect it, classify it, and automatically apply field mappings. Additionally, Forge uses advanced AI and ML techniques to document and identify PII fields in your data. No more painstaking scrubbing and parsing of your data, just quick and ready analytics.

How does Forge handle schema changes? Automatic detection and adaptation. When new fields appear, Forge regenerates models while maintaining backward compatibility. Zero downtime.

Does my data leave my warehouse? SaaS: Forge connects via service account to process data in-place. Only schema fingerprints (not actual data) sent for AI classification. Enterprise: Everything runs in YOUR VPC. Zero data egress.

What warehouses do you support? BigQuery, Snowflake, Databricks, and Redshift. One parse generates native models for all four simultaneously.

How accurate is PII detection? Pridwen uses a 3-layer hybrid system (rules + ML + crowd) with 95%+ accuracy. Context-aware and supports 20+ languages.

Do you replace Fivetran/Airbyte? No, we're complementary. Use Fivetran/Airbyte to load raw JSON → Use Forge to transform it into analytics tables.

How much engineering time does this save? Conservative estimate: 2-4 weeks initial build + 10 hours/month maintenance = $50,000-100,000/year for mid-size teams.