Flatten JSON Array
Infoveave Data Automation — Pivot & Transpose
A column holding a JSON array of objects becomes individual rows with proper columns — each array element in its own place, ready to filter and aggregate.
APIs and databases commonly embed related collections inside a single column as a JSON array. Order line items, product attributes, sensor readings, and event sequences are often returned as an array of objects in a single response column rather than as a separate relational table. Flatten JSON Array expands those embedded arrays by converting each object in the array into its own row, with the object properties becoming individual columns. The result is a properly normalized relational table where every element that was inside the array column is now its own queryable row.
What Flatten JSON Array does
Expand a JSON array column into individual rows in Infoveave. Convert embedded JSON arrays returned from APIs, NoSQL exports, or event logs into a flat, queryable row-per-element table.
Part of Infoveave Data Automation on the Unified Data Platform.
When to use Flatten JSON Array
- A column in your dataset contains a JSON array where each element is an object with its own properties, and you need to process or analyze each element as an independent row
- You are processing API responses where nested collections — such as line items, attributes, or readings — are embedded as JSON arrays in a single column rather than returned as a separate relational entity
- You want to count, filter, sum, or aggregate on the individual objects within an array column, which requires first expanding the array to rows
- You are normalizing a document-oriented or NoSQL export where one-to-many relationships are stored as embedded JSON arrays inside parent records
When to avoid it
- Your column contains a JSON object rather than a JSON array — use Flatten JSON Object, which extracts JSON object properties as new flat columns without increasing row count
- The JSON array contains primitive values such as strings or numbers rather than objects — use Fold an Array for native array columns with scalar values
- You want to merge or zip multiple JSON arrays together rather than expand a single array — use Zip JSON Array to combine multiple array columns by index
Where it fits in your Infoveave automation
Flatten JSON Array is one step inside a multi-step Infoveave workflow. Chain it with other activities — no code, no manual hand-offs.
Build this workflow visually in Infoveave Data Automation — drag, connect, and schedule with no infrastructure setup.
How teams use Flatten JSON Array
Real scenarios where this transformation saves hours of manual work.
Expand Order Line Items from E-Commerce API Order Records
An e-commerce operations team ingests order records from a platform API. Each order row has a line_items column containing a JSON array where each object in the array represents a purchased product with its own SKU, quantity, and price properties. Flatten JSON Array expands each line item object into its own row, adding SKU, quantity, and price as flat columns. The team can then filter, aggregate, and report on individual product performance without unpacking the nested arrays manually.
Flatten Attribute Variants from Product Configuration Export
A manufacturing team exports a product configuration dataset where each product row includes an attributes column containing a JSON array of configuration variants — each with an attribute name and value. Flatten JSON Array expands each attribute object into its own row with Attr as a column, allowing the team to filter by specific attribute types, count attribute combinations per product, and join against specification reference tables.
Normalize Metric Event Arrays from Application Performance Logs
A DevOps team processes application performance logs where each log record contains an events column holding a JSON array of metric events captured during the session. Flatten JSON Array expands the events into individual rows with event type, timestamp, and value as separate columns. The team can then filter by event type, aggregate metric values over time, and alert on threshold breaches — all without custom parsing code in their pipeline.
See Flatten JSON Array in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
AttributesInput Data
| ProductID | ProductName | Attributes |
|---|---|---|
| P001 | Widget Alpha | Attr: A1 | Attr: A2 |
| P002 | Widget Beta | Attr: B1 | Attr: B2 | Attr: B3 |
Output Data
| ProductID | ProductName | Attr |
|---|---|---|
| P001 | Widget Alpha | A1 |
| P001 | Widget Alpha | A2 |
| P002 | Widget Beta | B1 |
| P002 | Widget Beta | B2 |
| P002 | Widget Beta | B3 |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
JSON Columns
Select the columns that contain JSON array strings. Infoveave parses the JSON array in each selected column and creates one row per array element. The properties of each array element object become flat columns on the output row. The original parent row's other columns are duplicated onto each expanded row — the same behavior as Fold an Array, but applied to JSON array strings rather than native array types.
Row duplication
All non-expanded columns from the original parent row are duplicated on every row generated from that parent's array elements. This preserves the join key — such as OrderID or ProductID — alongside each expanded child row, maintaining the parent-child relationship in the flat output without needing a separate join step.
Frequently asked questions
Everything you need to know about Flatten JSON Array in Infoveave.
Also in Pivot & Transpose — and what runs before & after
Transformations in the same family as Flatten JSON Array, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Flatten JSON Array is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
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