Connect to a Data File#

You can connect with data in a CSV, JSON, or JSONL file. Apperate infers a schema from a sampling of the file, validates the data using that schema, ingests the data, and generates a REST endpoint and endpoint documentation. What’s more is that Apperate does this in one step. Just drop your file onto the console and Apperte does the rest.

Prerequisite

IEX Cloud Apperate account - Create one here.

Load Data in One Step#

Simply drag your CSV, JSON, or JSONL file onto the Apperate console and drop it in.

Tip

If you need a sample file to load, see Appendix: Create a Data File.

Your new dataset’s Overview page appears. A dataset is a table with a schema for validation, an auto-generated API, and more.

Here’s the heavy lifting Apperate did for you:

  • Inferred a data schema, including data types, constraints, indexes, and SmartLinks to Apperate’s financial metadata graph.

  • Validated the records against the schema

  • Loaded the data into a table

  • Generated an API endpoint and a corresponding API docs page

To view the schema and optionally modify it, see Modify a Data Schema.

Tip

If data ingestion fails or you suspect issues, check the ingestion details in the Data Jobs tab or navigate to Logs, and check the Log Stream or Ingestion Logs. For guidance, see Monitor Deployments.

Important

20,000,000 record limit per ingestion.

Note

Apperate supports CSV files that use the following common data delimiters: comma (,), tab, or pipe (|) characters. JSON and JSONL files are also supported.

Retrieve the Data#

In the Overview page, get the data by clicking the Example Request URL.

The URL opens in a new browser tab and the last data record appears in a JSON object like this.

[
    {
        "current_date": "2020-03-27",
        "estimated_value": 38650,
        "make": "Ford",
        "mileage": 8900,
        "model": "F-150",
        "owner_count": 1,
        "purchase_date": "2022-01-11",
        "vin": "SD089VN7678997566",
        "year": 2022
    }
]

It’s that easy for your apps to use data!

Congratulations on making data available in Apperate!

What’s Next#

Now that you’ve created a dataset, you can examine it and or modify it in the schema editor (click Edit). You can also add more data to it (click Ingest) or modify its data via the dataset’s Database page.

Here are some more topics you can learn:

Ingesting More Data into a Dataset

Learn about more Connectors

Use Apperate’s APIs shows how to query datasets and operate on resources programmatically.

Understanding Datasets explains dataset properties, constraints, indexes, and mappings.

Appendix: Create a Data File#

If you don’t already have a file to load into Apperate, you can create a simple CSV file (has three records) using a command below for your operating system.

echo "vin,make,model,year,current_date,purchase_date,estimated_value,mileage,owner_count
XV859643N98D98E7C,Chevrolet,Camaro,2020,2020-03-27,2020-03-13,45955.00,32000,2
SD089VN7678997566,Ford,F-150,2022,2020-03-27,2022-01-11,38650.00,8900,1
59ADFG60929087DAH,Toyota,Prius,2018,2020-03-27,2019-09-23,22876.00,76000,1" \
>>cars
(
echo vin,make,model,year,current_date,purchase_date,estimated_value,mileage,owner_count
echo XV859643N98D98E7C,Chevrolet,Camaro,2020-03-27,2020,2020-03-13,45955.00,32000,2
echo SD089VN7678997566,Ford,F-150,2022,2020-03-27,2022-01-11,38650.00,8900,1
echo 59ADFG60929087DAH,Toyota,Prius,2018,2020-03-27,2019-09-23,22876.00,76000,1
)>cars

Load your file into Apperate following the instructions at Load Data in One Step.