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Flatten complex JSON into SQL

ready tables in minutes
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Solve your nested JSON challenges with
Flatbread
Schema Changes break ETL Pipelines
Schema Changes break ETL Pipelines
Adding/removing keys in source JSON (semi-structured) data might break downstream ETL pipeline logic & requires data reloading. 
Polymorphic Data (Changing data-types for same key)
Polymorphic Data (Changing data-types for same key)
Different key data types across multiple records break the ETL & require a rewrite of ETL logic to handle, leading to constant maintenance. Example : Key changes from String to JSON object type.
Arrays are difficult to parse and query
Arrays are difficult to parse and query
Extracting data from Array/stringified arrays requires parsing into an Array type (if the warehouse supports it) and complex transformation logic if the data is not flattened/exploded. This also requires a huge memory while querying leading to higher costs and latency.
Nested JSON requires Flattening to query
Nested JSON requires Flattening to query
Parsing semi-structured JSON data poses challenges due to its nested structures and variable schema, complicating data flattening and leading to inefficient queries.
Requirement of Data Quality Monitoring
Requirement of Data Quality Monitoring
Schema changes, data-type changes or parsing failures requires monitoring & alerting to avoid long downtimes/missing data.
Flatbread is a data engineering tool that simplifies
the handling of complex JSON data pipelines.
Functionality
Normalises complex JSON into SQL-ready tables for analytics handling changing JSON nested structures, ensuring downstream processes remain robust.
It automates parsing, extraction, transformation, and consistency checks without coding, providing reliable relational data for analysis.
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Read more from our blogs
New Release
7 Techniques for Managing Changing Data Types in Semi-Structured Data Ingestion
Polymorphic keys in semi-structured data can complicate storage and retrieval. This blog explores seven approaches to manage these dynamic fields, including schema enforcement, dynamic typing, and data type promotion, offering practical strategies for data engineers facing evolving data challenges.
Read more
New Release
How to Flatten and Query Arrays in Semi-Structured Nested JSON for Effective Data Extraction
Struggling with querying arrays in semi-structured nested JSON data? This blog addresses the common pain points of flattening these arrays for effective querying. Discover various strategies to simplify data extraction, ensuring you can efficiently access and analyze your insights.
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Frequently Asked Questions

How does this tool compare to other data integration and transformation solutions?

This tool specializes in handling JSON data, offering a more streamlined and efficient approach compared to generic solutions. Built to manage polymorphic data, arrays and schema changes.
Yes, the tool is designed to efficiently process large volumes of JSON data.
Yes, it integrates with most of the standard data warehouse and big data platforms like Postgres, Snowflake, Databricks, Redshift and BigQuery
FlatBread offers customised manner to define the level of flattening and for arrays it creates nested tables with separate table for each column with array data type.
We have multiple ways to deal with this, one of the ways is to append data_type in column name which will make it unique, another way is to alert the user on unaccommodating new rows/jsons and put them into a dead-letter table, where user can process them later based on intelligent AI suggestions.