Using the Schema Discovery Feature on Static Data - Amazon Kinesis Data Analytics for SQL Applications Developer Guide

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Using the Schema Discovery Feature on Static Data


After September 12, 2023, you will not able to create new applications using Kinesis Data Firehose as a source if you do not already use Kinesis Data Analytics for SQL. For more information, see Limits.

The schema discovery feature can generate a schema from either the data in a stream or data in a static file that is stored in an Amazon S3 bucket. Suppose that you want to generate a schema for a Kinesis Data Analytics application for reference purposes or when live streaming data isn't available. You can use the schema discovery feature on a static file that contains a sample of the data in the expected format of your streaming or reference data. Kinesis Data Analytics can run schema discovery on sample data from a JSON or CSV file that's stored in an Amazon S3 bucket. Using schema discovery on a data file uses either the console, or the DiscoverInputSchema API with the S3Configuration parameter specified.

Running Schema Discovery Using the Console

To run discovery on a static file using the console, do the following:

  1. Add a reference data object to an S3 bucket.

  2. Choose Connect reference data in the application's main page in the Kinesis Data Analytics console.

  3. Provide the bucket, path and IAM role data for accessing the Amazon S3 object containing the reference data.

  4. Choose Discover schema.

For more information on how to add reference data and discover schema in the console, see Example: Adding Reference Data to a Kinesis Data Analytics Application.

Running Schema Discovery Using the API

To run discovery on a static file using the API, you provide the API with an S3Configuration structure with the following information:

To generate a schema from an Amazon S3 object using the DiscoverInputSchema API
  1. Make sure that you have the AWS CLI set up. For more information, see Step 2: Set Up the AWS Command Line Interface (AWS CLI) in the Getting Started section.

  2. Create a file named data.csv with the following contents:

    year,month,state,producer_type,energy_source,units,consumption 2001,1,AK,TotalElectricPowerIndustry,Coal,ShortTons,47615 2001,1,AK,ElectricGeneratorsElectricUtilities,Coal,ShortTons,16535 2001,1,AK,CombinedHeatandPowerElectricPower,Coal,ShortTons,22890 2001,1,AL,TotalElectricPowerIndustry,Coal,ShortTons,3020601 2001,1,AL,ElectricGeneratorsElectricUtilities,Coal,ShortTons,2987681
  3. Sign in to the Amazon S3 console at

  4. Create an Amazon S3 bucket and upload the data.csv file you created. Note the ARN of the created bucket. For information about creating an Amazon S3 bucket and uploading a file, see Getting Started with Amazon Simple Storage Service.

  5. Open the IAM console at Create a role with the AmazonS3ReadOnlyAccess policy. Note the ARN of the new role. For information about creating a role, see Creating a Role to Delegate Permissions to an Amazon Service. For information about how to add a policy to a role, see Modifying a Role.

  6. Run the following DiscoverInputSchema command in the AWS CLI, substituting the ARNs for your Amazon S3 bucket and IAM role:

    $aws kinesisanalytics discover-input-schema --s3-configuration '{ "RoleARN": "arn:aws:iam::123456789012:role/service-role/your-IAM-role", "BucketARN": "arn:aws:s3:::your-bucket-name", "FileKey": "data.csv" }'
  7. The response looks similar to the following:

    { "InputSchema": { "RecordEncoding": "UTF-8", "RecordColumns": [ { "SqlType": "INTEGER", "Name": "COL_year" }, { "SqlType": "INTEGER", "Name": "COL_month" }, { "SqlType": "VARCHAR(4)", "Name": "state" }, { "SqlType": "VARCHAR(64)", "Name": "producer_type" }, { "SqlType": "VARCHAR(4)", "Name": "energy_source" }, { "SqlType": "VARCHAR(16)", "Name": "units" }, { "SqlType": "INTEGER", "Name": "consumption" } ], "RecordFormat": { "RecordFormatType": "CSV", "MappingParameters": { "CSVMappingParameters": { "RecordRowDelimiter": "\r\n", "RecordColumnDelimiter": "," } } } }, "RawInputRecords": [ "year,month,state,producer_type,energy_source,units,consumption\r\n2001,1,AK,TotalElectricPowerIndustry,Coal,ShortTons,47615\r\n2001,1,AK,ElectricGeneratorsElectricUtilities,Coal,ShortTons,16535\r\n2001,1,AK,CombinedHeatandPowerElectricPower,Coal,ShortTons,22890\r\n2001,1,AL,TotalElectricPowerIndustry,Coal,ShortTons,3020601\r\n2001,1,AL,ElectricGeneratorsElectricUtilities,Coal,ShortTons,2987681" ], "ParsedInputRecords": [ [ null, null, "state", "producer_type", "energy_source", "units", null ], [ "2001", "1", "AK", "TotalElectricPowerIndustry", "Coal", "ShortTons", "47615" ], [ "2001", "1", "AK", "ElectricGeneratorsElectricUtilities", "Coal", "ShortTons", "16535" ], [ "2001", "1", "AK", "CombinedHeatandPowerElectricPower", "Coal", "ShortTons", "22890" ], [ "2001", "1", "AL", "TotalElectricPowerIndustry", "Coal", "ShortTons", "3020601" ], [ "2001", "1", "AL", "ElectricGeneratorsElectricUtilities", "Coal", "ShortTons", "2987681" ] ] }