Tablizer

Convert JSON to Avro Schema Online

Paste a JSON table below, edit it if you need to, and get Avro Schema back. The conversion runs in your browser, so nothing is uploaded, there is no size limit and there is no signup.

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Nothing here is uploaded. The parsing happens on this page.
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The output will appear here once there is a table to convert.

What JSON is

JSON is a text format for nested data: objects with named keys, arrays, strings, numbers, booleans and null. It has no date type and no comments.

Every web API, most configuration files and anything that talks to JavaScript.

What Avro Schema is

An Avro schema describes a record: its name and its typed fields. It is the contract, not the data.

Kafka pipelines and anything in the Hadoop family.

What changes when you convert JSON to Avro Schema

Four shapes all read correctly: an array of objects, an array of arrays with a header row, an object of column arrays, and an object keyed by row id. A single wrapper key holding the array, which is how most APIs reply, is unwrapped automatically. Nested objects are flattened one level into dotted keys; anything deeper is kept as JSON text in the cell, because a table cannot hold a tree.

Types are inferred per column from the values present. Any column with a gap in it becomes a union with null, because a reader rejects a record whose non-nullable field is missing.

What carries over from JSON to Avro Schema

Avro can express everything JSON holds about a table, so this conversion is about shape rather than loss.

The result can be read a record at a time, and appended to by adding to the end of it. That is worth having for a table too large to hold in memory, and it means a Avro file that is cut off part way through still gives you every record before the cut.

Both formats record types, so numbers, booleans and nulls survive as themselves rather than as text.

A worked example

Three rows of staff data, with an identifier that has a leading zero, a value containing a comma, a value containing quotes and one blank cell. Those are the four places formats disagree, so they are the four places to look.

JSON in
[
  { "id": "007", "name": "Halima Yusuf", "role": "Analyst, data", "started": "2024-03-15", "hours": "38.5" },
  { "id": "012", "name": "Jonah \"Jo\" Pryce", "role": "Engineer", "started": "2025-11-02", "hours": "" },
  { "id": "104", "name": "Wei Chen", "role": "Manager", "started": "2023-06-30", "hours": "40" }
]
Avro Schema out
{
  "type": "record",
  "name": "staff",
  "fields": [
    {
      "name": "id",
      "type": "long"
    },
    {
      "name": "name",
      "type": "string"
    },
    {
      "name": "role",
      "type": "string"
    },
    {
      "name": "started",
      "type": "string"
    },
    {
      "name": "hours",
      "type": [
        "null",
        "double"
      ]
    }
  ]
}

Questions

How do I convert JSON to Avro Schema?

Paste your JSON into the box above or drop the file onto it. Check the table in the grid, then copy or download the Avro Schema from the output panel. It takes one step and the data never leaves your browser.

Why did my zip code lose its leading zero?

Type inference turned the text 07030 into the number 7030. Tablizer leaves inference off by default for exactly this reason, and refuses to convert any value starting with a zero followed by a digit even when it is on.

Is there a limit on file size?

No. The work happens on your own machine, so the limit is your machine's memory rather than an upload cap. A file with tens of thousands of rows converts in a second or two.

Is my data uploaded anywhere?

No. The parsing and generating are done by JavaScript running on this page. You can watch the network tab while you convert and see that nothing is sent.

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