JSON Load - Maia Documentation

The JSON Load orchestration component loads data into a table from one or more JSON files. The component automatically infers the schema from the structure of the selected file(s), creates the corresponding table, and then loads the data into the table.

For Google BigQuery projects, the JSON Load connector only supports newline-delimited JSON files (also called NDJSON or JSON Lines) with one complete JSON object per line.

Use cases

JSON Load suits data engineering and analytics tasks that bring semi-structured or API-originated data into your warehouse. For example, you can use this component to:

Load API response exports or webhook payloads stored as JSON files for analysis alongside structured warehouse data.
Ingest document exports from NoSQL databases such as MongoDB or DynamoDB to enable relational querying.
Load application event logs or audit trails stored in JSON for compliance reporting or operational analytics.
Bring in IoT device telemetry or mobile app event data stored as JSON files as a first step before downstream transformation.
Ingest configuration or metadata files produced by application deployments to track changes over time.

You can authorize this component using either of these two methods:

Cloud Provider Credentials: By default, explicit Cloud Provider Credentials are used to facilitate cross-account or cross-cloud data loads.
Agent Identity: If cloud provider credentials are not specified, the component attempts to use the Matillion Agent’s native service identity to access the source data.

Properties

Name

string

required

A human-readable name for the component.

Connect

Snowflake
Databricks
Google BigQuery

Type

drop-down

required

Snowflake Managed: Load your file from a Snowflake internal stage.
S3: Load your file from an S3 bucket.
Google Cloud Storage: Load your file from a Google Cloud Storage bucket.
Azure Blob Storage: Load your file from an Azure Blob Storage container.

Stage

drop-down

required

Select a staging area for the data. The special value [New Stage] will create a temporary stage to be used for loading the data when the corresponding parameter values are provided.The options in this drop-down menu depend on the values you select for the Database and Schema parameters. If you change these values, the list of available options updates automatically, and the previously selected option may become invalid.When [New Stage] is selected, the component uses the cloud credentials configured for your environment to access the required resources.

Snowflake Managed
S3
Google Cloud Storage
Azure Blob Storage

Staged file path

string

A stage may include directories. The user has the option to browse and select files from a specific directory path, for example, /Example/Path.If this is left blank, and if the Pattern parameter is also empty, all files from the stage will be loaded.

S3 object prefix

file explorer

required

To retrieve the intended files, use the file explorer to enter the container path where the S3 bucket is located, or select from the list of S3 buckets.This must have the format s3://<bucket>/<path>.

Encryption

drop-down

required

Decide how the files are encrypted inside the S3 bucket. This property is available when using an existing Amazon S3 location for staging.

Client Side Encryption: Encrypt the data according to a client-side master key. For more information, read Protecting data using client-side encryption.
KMS Encryption: Encrypt the data according to a key stored on KMS. For more information, read Using server-side encryption with AWS KMS keys.
S3 Encryption: Encrypt the data according to a key stored on an S3 bucket. For more information, read Using server-side encryption with Amazon S3-managed encryption keys (SSE-S3).
None: No encryption.

KMS key ID

drop-down

required

The ID of the KMS encryption key you have chosen to use in the Encryption property.Only available when Encryption is set to KMS Encryption.

Master key

drop-down

required

The secret definition denoting your master key for client-side encryption. Your password should be saved as a secret definition before using this component.Only available when Encryption is set to Client Side Encryption.

Storage integration

drop-down

required

Select the storage integration. Storage integrations are required to permit Snowflake to read from and write to a cloud storage location. Integrations must be set up in advance and configured to support Google Cloud Storage. For more information, read Create storage integration.

Google Storage URL location

file explorer

required

Azure storage location

file explorer

required

To retrieve the intended files, use the file explorer to enter the container path where the Azure Storage account is located, or select from the list of storage accounts.This must have the format azure://<STORAGEACCOUNT>/<path>.

Pattern

string

A valid regular expression (regex) to match part of a file’s path or name. Files that match the pattern will be included in the load.If this parameter is left empty, the component automatically uses the pattern *, which matches all files within the specified Staged file path or Stage parameters. The pattern applies to the entire file path, not just the directory defined in Staged file path.The subfolder containing the object to load must be included here.

Only JSON files are supported.
Ensure that the pattern entered correctly targets the files you intend to load.

Type

drop-down

required

S3: Load your file from an S3 bucket.
Google Cloud Storage: Load your file from a Google Cloud Storage bucket.
Azure Blob Storage: Load your file from an Azure Blob Storage container.

S3
Google Cloud Storage
Azure Blob Storage

S3 object prefix

file explorer

required

Google Storage URL location

file explorer

required

To retrieve the intended files, use the file explorer to enter the container path where the Google Cloud Storage bucket is located, or select from the list of Google Cloud Storage buckets.This must have the format gs://<bucket>/<path>.For this to work, an external location with access to the specified location must be configured. For more information, read External Locations| Databricks on AWS

Azure storage location

file explorer

required

Glob pattern

string

A valid glob pattern to match part of a file’s path or name. Files that match the pattern will be included in the load.If this parameter is left empty, the component automatically uses the pattern *, which matches all files within the specified location parameters. The pattern applies to the entire file path, not just the directory defined in the location parameter.

Only JSON files are supported.
Ensure that the pattern entered correctly targets the files you intend to load.

Type

drop-down

required

Select Google Cloud Storage to load your file from a Google Cloud Storage bucket.

Google Storage URL location

file explorer

required

File filter

string

Filter for specific files by their file name, including their file extension. You can use wildcards in the file name, for example prefix_*{filetype}.If this field is left empty, all files at the specified Google Storage URL location are loaded.If the specified Google Storage URL location contains a *, you can’t apply a file filter.

Configure

Snowflake
Databricks
Google BigQuery

File format

drop-down

required

The default value is set to [New File Format]. Specify a file format, and a temporary format with those settings will be used when the component runs. Alternatively, select a pre-made file format.

Compression

drop-down

required

Select the compression algorithm used on the file being loaded.The default setting is AUTO.

Enable octal

boolean

required

When True, enables the parsing of octal values.Default setting is False.

Allow duplicates

boolean

required

When True, allows duplicate object field names.Default setting is False.

Strip outer array

boolean

required

When True, instructs the JSON parser to remove outer brackets.Default setting is False.

Strip null values

boolean

required

When True, instructs the JSON parser to remove any object fields or array elements containing NULL values.Default setting is False.

Ignore UTF8 errors

boolean

required

When True, replaces any invalid UTF-8 sequences with Unicode characters. When False, UTF-8 errors will not produce an error in the pipeline run.Default setting is False.

Binary format

drop-down

required

Determines how binary string values are encoded in data files. Use this setting when importing data into a table’s binary columns.Choose from the following options:

HEX: Binary string values are encoded as hexadecimal strings.
BASE64: Binary string values are encoded as Base64 strings.
UTF8: Binary string values are encoded as UTF-8 strings.

Default setting is HEX.This will only work when Match by column name is set to either Case Insensitive or Case Sensitive.

Multi line

boolean

required

Allows multiple lines in a JSON record. When set to No, any JSON record containing a newline character is considered invalid, and will trigger an error.Default setting is False.

Replace invalid characters

boolean

required

Snowflake replaces invalid UTF-8 characters with the Unicode replacement character. When False, the load operation produces an error when invalid UTF-8 character encoding is detected.Default setting is False.

Skip byte order mark

boolean

required

Determines whether to ignore a BOM (byte order mark) in the input file.If set to FALSE, Snowflake processes any BOM found in the file, which may lead to errors or cause the BOM to be included in the first column of the table.Default setting is True.

Null if

editor

Specify one or more strings (one string per row of the table) to convert to NULL values. When one of these strings is encountered in the file, it is replaced with an SQL NULL value for that field in the loaded table. Click + to add a string.This will only work when Match By Column Name is set to either Case Insensitive or Case Sensitive.

Ignore corrupt files

boolean

When set to Yes, corrupt input files are skipped and not included in the load.

Ignore missing files

boolean

When set to Yes, files that cannot be found are ignored during the load process.

Modified after

string

An optional timestamp as a filter to only ingest files that have a modification timestamp after the provided timestamp.

Modified before

string

An optional timestamp as a filter to only ingest files that have a modification timestamp before the provided timestamp.

Recursive file lookup

boolean

When set to Yes, files are read recursively from all subdirectories under the specified path. When enabled, partition inference is disabled. To control which files are loaded, use the Glob pattern property instead.

Allows backslash escaping any character

boolean

If Yes, will allow the quoting of all characters using the backslash quoting mechanism in JSON records.

Allow comments

boolean

If Yes, will allow JAVA/C++ style comments in JSON records.

Allow non-numeric numbers

boolean

If Yes, will allow using non-numeric number strings such as NaN, Infinity, -Infinity in JSON records.

Allow numeric leading zeros

boolean

If Yes, will allow leading zeros in numbers in JSON records.

Allow single quotes

boolean

If Yes, will allow single quotes in addition to double quotes in JSON records.

Allow unquoted control chars

boolean

If Yes, will allow JSON strings to include unquoted control characters (ASCII characters with values less than 32, including tab and line feed characters) in JSON records.

Allow unquoted field names

boolean

If Yes, will allow unquoted JSON field names (which are allowed by JavaScript, but not by the JSON specification).

Bad records path

string

An optional path to store bad records encountered during parsing. When specified, records that cannot be parsed are written to this location instead of causing the load to fail.

Column name of corrupt record

string

An optional column name to store the malformed JSON string when a record cannot be parsed. This allows you to capture and analyze corrupt records in the target table.

Date format

string

Describe the format of date values in the data files to be loaded. For example, yyyy-MM-dd.

Drop field if all null

boolean

If Yes, will ignore columns that contain only null values when inferring the schema.

Encoding

string

Select the character encoding to use when reading the input files.

Infer timestamp

boolean

If Yes, will attempt to infer timestamp strings as a TimestampType during schema inference.

Line separator

string

Specify the character or sequence of characters used to separate individual records (rows) in the JSON file.

Locale

string

Sets the locale for date and timestamp parsing. The locale should be in BCP 47 language tag format (e.g., en-US).

Mode

drop-down

Controls how the parser handles corrupt or malformed records. Options include:

PERMISSIVE: Sets fields to null when encountering corrupted records and places the malformed string into a field configured by Column name of corrupt record. This is the default setting.
DROPMALFORMED: Ignores and drops rows containing corrupted records.
FAILFAST: Throws an exception when encountering corrupted records.

Multi line

boolean

If Yes, will parse records which may span multiple lines.

Primitives as string

boolean

If Yes, primitive data types are interpreted as strings in JSON files. If not specified, the default is No.

Prefers decimal

boolean

If Yes, all floating-point values will be treated as a decimal type during schema inference.

Reader case sensitive

boolean

Specifies case sensitivity behavior during schema rescue. If Yes, data columns are matched to the schema by name in a case-sensitive manner.

Rescued data column

string

An optional column name to store data that does not match the schema. When specified, fields that don’t match the expected schema are collected into this column as a JSON string.

Single variant column

string

An optional column name to store the entire JSON record as a single VARIANT type value, rather than parsing it into separate columns.

Timestamp format

string

Describe the format of timestamp values in the data files to be loaded.

Time zone

string

Sets the timezone to use when parsing timestamps.

Date format

string

Define how date values are formatted in your files, for example MM/DD/YYYY.If you leave this blank, the default format YY-MM-DD is used.

Datetime format

string

Define how date values are formatted in your files, for example MM/DD/YYYY HH24:MI:SS.FF3.If you leave this blank, the default format YYYY-MM-DD HH:MM:SS[.SSSSSS] is used.

Time format

string

Define how time values are formatted in your files, for example HH24:MI:SS.FF3.If you leave this blank, the default format HH:MM:SS[.SSSSSS] is used.

Timestamp format

string

Define how timestamp values are formatted in your files, for example MM/DD/YYYY HH24:MI:SS.FF3.If you leave this blank, Google BigQuery attempts to parse timestamp values automatically.

Time zone

string

The default time zone to apply when parsing timestamp values that do not include a specific time zone. For example, America/New_York or UTC+05:30.If you leave this blank, UTC is used.

Gzip data

boolean

When set to Yes, Google BigQuery treats the source files as gzip-compressed.Note that compressed files cannot be read in parallel, which may impact load performance. Default is No.

Max bad records

string

The maximum number of bad records to ignore before returning an error. A record is considered bad if it cannot be parsed. For example, enter 10 to allow up to 10 unparseable rows before the load fails.If you leave this blank, the load will fail if there are any bad records.

Ignore unknown values

boolean

When set to Yes, extra fields in the file that are not present in the table schema are silently ignored.When set to No, extra fields cause the load to fail.

Destination

Snowflake
Databricks
Google BigQuery

Warehouse

drop-down

required

Database

drop-down

required

Schema

drop-down

required

Target table name

string

required

Specify the name of the newly created or existing table to load data into. The table will be created according to the Load strategy you select below.

Load strategy

drop-down

required

Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
Truncate and Insert: Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated.
Fail if Exists: If the specified table name already exists, this pipeline will fail to run, and no data will be copied to the table.
Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It’s appended onto the end of the existing data in the table. If the specified table name doesn’t exist, then an error message will appear and the table is not created. You must always provide a table name.

Catalog

drop-down

required

Schema (database)

drop-down

required

Target table name

string

required

The name of the table to be created. The table will be created according to the Load strategy you select below.

Load strategy

drop-down

required

Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
Truncate and Insert: Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated.
Fail if Exists: If the specified table name already exists, this pipeline will fail to run, and no data will be copied to the table.
Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It’s appended onto the end of the existing data in the table. If the specified table name doesn’t exist, then an error message will appear and the table is not created. You must always provide a table name.

If not specified, the default is Fail if Exists.

Databricks uses COPY INTO, which is idempotent. This means it tracks which source files have already been loaded into the target Delta table and skips them on subsequent runs.Enabling Force load overrides this behavior for the Truncate and Insert and Append load strategies, causing all matching source files to be reprocessed.

Project

drop-down

required

Select the ID of the Google Cloud Platform project to load data into. The special value [Environment Default] uses the project defined in the environment.

Dataset

drop-down

required

Select the Google BigQuery dataset to load data into. The special value [Environment Default] uses the dataset defined in the environment.

Target table name

string

required

The name of the table to be created in your Google BigQuery project. You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.

Load strategy

drop-down

required

Define what happens if the table name already exists in the specified destination.

Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
Truncate and Insert: Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated.
Fail if Exists: If the specified table name already exists, this pipeline will fail to run.
Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It’s appended onto the end of the existing data in the table. If the specified table name doesn’t exist, then the table will be created, and your data will be inserted into the table.

Advanced Settings

Snowflake
Databricks
Google BigQuery

integer

required

Specify the number of errors or the percentage of errors required to skip the file. This parameter only accepts integer characters. % is not accepted. Specify percentages as a number only.This parameter is only available when On Error is set to either Skip File When n Errors or Skip File When n% Errors.

Size limit (in bytes)

integer

Specify the maximum size, in bytes, of data to be loaded for a given COPY statement. If the maximum is exceeded, the COPY operation discontinues loading files. For more information, refer to the Snowflake documentation.

Purge files

boolean

required

When True, purges data files after the data is successfully loaded. Default setting is False.

Match by column name

drop-down

required

Specify whether to load semi-structured data into columns in the target table that match corresponding columns represented in the data.

Case Insensitive: Load semi-structured data into columns in the target table that match corresponding columns represented in the data. Column names should be case-insensitive.
Case Sensitive: Load semi-structured data into columns in the target table that match corresponding columns represented in the data. Column names should be case-sensitive.
None: The COPY operation loads the semi-structured data into a variant column or, if a query is included in the COPY statement, transforms the data.

Truncate columns

boolean

required

When True, strings are automatically truncated to the target column length. When False (default), the COPY statement produces an error if a loaded string exceeds the target column length.

Force load

boolean

required

Force load

boolean

required

When True, loads all files, regardless of whether they have been loaded previously and haven’t changed since they were loaded. Default setting is False.When set to True, this option reloads files and can lead to duplicated data in a table. The Load strategy in the destination needs to be Truncate and Insert or Append for this to work.

Default rounding mode

drop-down

Select the rounding mode to use when storing numeric data types. For more information, read the Google BigQuery RoundingMode documentation.

ROUND_HALF_AWAY_FROM_ZERO rounds half values away from zero.
ROUND_HALF_EVEN rounds half values to the nearest even value.

​Use cases

​Properties

​Connect

​Configure

​Destination

​Advanced Settings

Use cases

Properties

Connect

Configure

Destination

Advanced Settings