Azure Data Lake Storage Gen2

The Azure Data Lake Storage Gen2 origin reads data from Microsoft Azure Data Lake Storage Gen2. The origin can create multiple threads to enable parallel processing in a multithreaded pipeline. Use the origin only in pipelines configured for standalone execution mode. To read data from Microsoft Azure Data Lake Storage Gen1, use the Azure Data Lake Storage Gen1 origin. For information about supported versions, see Supported Systems and Versions in the Data Collector documentation.

Tip: Data Collector provides several Azure storage origins to address different needs. For a quick comparison chart to help you choose the right one, see Comparing Azure Storage Origins.

The origin uses the Microsoft Azure Data Lake Storage Gen2 API to request a list of objects located in a storage container or file system and that match a pattern in a directory. As Azure returns pages with the requested objects, the origin launches threads to read and process the data. The objects must be fully written.

After processing an object or upon encountering errors, the origin can keep, archive, or delete the object. When archiving, the origin can move the object.

The origin can generate events for an event stream. For more information about dataflow triggers and the event framework, see Dataflow Triggers Overview.

When the pipeline stops, the Azure Data Lake Storage Gen2 origin notes where it stops reading. When the pipeline starts again, the origin continues reading from where it stopped by default. You can reset the origin to read and process all requested objects.

When you configure the Azure Data Lake Storage Gen2 origin, you specify connection information for Azure Data Lake Storage Gen2, including the storage container or file system and authentication method. You can also use a connection to configure the origin. You also specify the number of objects for Azure to list on a page and the maximum time to wait for Azure to return the requested objects.

You specify information about the objects to read and how to process, including the directory that contains the objects, the matching name pattern, the order to read the objects, the number of threads used to process the data, and the batch size.

You also specify what to do with processed objects and how to handle objects that cannot be processed.

Prerequisites

Complete the following prerequisites before you configure the Azure Data Lake Storage Gen2 origin:

If necessary, create a new Azure Active Directory application for Data Collector.
For information about creating a new application, see the Azure documentation.
Ensure that the Azure Active Directory Data Collector application has the appropriate access control to perform the necessary tasks.
The Data Collector application requires Read and Execute permissions to read data in Azure. If also writing to Azure, the application requires Write permission as well.
For information about configuring Gen2 access control, see the Azure documentation.
Retrieve information from Azure to configure the origin.

After you complete all of the prerequisite tasks, you can configure the Azure Data Lake Storage Gen2 origin.

Retrieve Authentication Information

The Azure Data Lake Storage Gen2 origin can use different methods to authenticate connections with Azure.

The authentication information required depends on the selected authentication method:

OAuth with Service Principal

Connections made with OAuth with Service Principal authentication require the following information:

Application ID - Application ID for the Azure Active Directory Data Collector application. Also known as the client ID.
For information on accessing the application ID from the Azure portal, see the Azure documentation.
Tenant ID - Tenant ID for the Azure Active Directory Data Collector application. Also known as the directory ID.
For information on accessing the tenant ID from the Azure portal, see the Azure documentation.
Application Key - Authentication key or client secret for the Azure Active Directory application. Also known as the client secret.
For information on accessing the application key from the Azure portal, see the Azure documentation.

Azure Managed Identity

Connections made with Azure Managed Identity authentication require the following information:

Application ID - Application ID for the Azure Active Directory Data Collector application. Also known as the client ID.
For information on accessing the application ID from the Azure portal, see the Azure documentation.

Shared Key

Connections made with Shared Key authentication require the following information:

Account Shared Key - Shared access key that Azure generated for the storage account.
For more information on accessing the shared access key from the Azure portal, see the Azure documentation.

Common Path, Path Pattern, and Wildcards

The Azure Data Lake Storage Gen2 origin concatenates the common path and the path pattern to define the objects that the origin reads. You can specify an exact path pattern or you can specify an Ant-style path pattern to have the origin read multiple objects recursively.

Ant-style path patterns can include the following wildcards:

Question mark (?) to match a single character
Asterisk (*) to match zero or more characters
Double asterisks (**) to match zero or more directories

You can specify the objects that the origin reads by entering values in both the Common Path and Path Pattern properties or in only the Path Pattern property. Consider the following examples:

Read all log files in all nested directories

The following table presents two options for configuring the properties to read all log files in US/East/MD/ and all nested directories:


Property	Option 1	Option 2
Common Path	`US/East/MD/`	<blank>
Path Pattern	`*/.log`	`US/East/MD/*/.log`

Read all log files in a particular subdirectory

The following table presents two options for configuring the properties to read all log files in a weblogs subdirectory nested anywhere in the hierarchy under US/:


Property	Option 1	Option 2
Common Path	`US/`	<blank>
Path Pattern	`*/weblogs/.log`	`US/*/weblogs/.log`

Read Order

The Azure Data Lake Storage Gen2 origin reads objects in ascending order based on the object name or the last modified timestamp. For best performance when reading a large number of objects, configure the origin to read objects based on the object name. Azure always returns objects sorted by name. To read in timestamp order, the origin sorts returned objects by timestamp. Therefore, timestamp ordering requires all objects be returned before the origin starts reading.

You can configure one of the following read orders:

Lexicographically Ascending Names

The origin reads objects in lexicographically ascending order based on object names. Lexicographically ascending order reads the numbers 1 through 11 as follows:

1, 10, 11, 2, 3, 4... 9

To read objects with names that sort before already processed objects, reset the origin to read all available objects.

For example, suppose you configure the following properties in the origin:


Property	Value
Storage Container/File System	`WebServer`
Common Path	`2023/`
Path Pattern	`*/web.log`

The origin reads the following objects in the following order:

WebServer/2023/February/web-10.log
WebServer/2023/February/web-11.log
WebServer/2023/February/web-5.log
WebServer/2023/February/web-6.log
WebServer/2023/February/web-7.log
WebServer/2023/February/web-8.log
WebServer/2023/February/web-9.log
WebServer/2023/January/web-1.log
WebServer/2023/January/web-2.log
WebServer/2023/January/web-3.log
WebServer/2023/January/web-4.log

To read these objects in logical and lexicographically ascending order, you might add leading zeros to the object naming convention as follows:

WebServer/2023/February/web-0005.log
WebServer/2023/February/web-0006.log
...
WebServer/2023/February/web-0010.log
WebServer/2023/February/web-0011.log
WebServer/2023/January/web-0001.log
WebServer/2023/January/web-0002.log
WebServer/2023/January/web-0003.log
WebServer/2023/January/web-0004.log

If a new object arrives with a name of WebServer/2023/February/web-0012.log, the origin does not read the object unless you reset the origin.

Last Modified Timestamp

The origin reads objects in ascending order based on the last modified timestamp. After Azure returns all objects from a request, the origin sorts the objects by timestamp and then reads them in chronological order. If two or more objects have the same timestamp, the origin reads those objects in lexicographically increasing order by object name.

To read objects that include a timestamp earlier than already processed objects, reset the origin to read all available objects.

For example, suppose you configure the following properties:


Property	Value
Storage Container/File System	`ServerEast`
Common Path	`LogFiles/`
Path Pattern	`*.log`

Suppose Azure returns the following log files:

ServerEast/LogFiles/file1.log        04-30-2016 12:00:00
ServerEast/LogFiles/file2.log        04-30-2016 18:39:44
ServerEast/LogFiles/fileA.log        04-30-2016 12:03:23
ServerEast/LogFiles/fileB.log        04-30-2016 15:34:51

The origin reads these objects in order of the timestamp, as follows:

ServerEast/LogFiles/file1.log        04-30-2016 12:00:00
ServerEast/LogFiles/fileA.log        04-30-2016 12:03:23
ServerEast/LogFiles/fileB.log        04-30-2016 15:34:51
ServerEast/LogFiles/file2.log        04-30-2016 18:39:44

If a new object arrives with a timestamp of 04-29-2016 12:00:00, the origin does not process the object unless you reset the origin.

Buffer Limit and Error Handling

The Azure Data Lake Storage Gen2 origin uses a buffer to read objects into memory to produce records. The size of the buffer determines the maximum size of the record that can be processed.

The buffer limit helps prevent out-of-memory errors. Decrease the buffer limit when memory on the Data Collector machine is limited. Increase the buffer limit to process larger records when memory is available.

When a record is larger than the specified limit, the origin processes the object based on the stage error handling:

Discard

The origin discards the record and all remaining records in the object, and then continues processing the next object.

Send to Error

With a buffer limit error, the origin cannot send the record to the pipeline for error handling because it is unable to fully process the record.

Instead, the origin displays a message indicating that a buffer overrun error occurred in the pipeline history.

If an error container and path are configured for the stage, the origin moves the object to that location and continues processing the next object.

Stop Pipeline

The origin stops the pipeline and displays a message indicating that a buffer overrun error occurred. The message includes the object and offset where the buffer overrun error occurred. The information displays in the pipeline history.

Note: You can also check the Data Collector log file for error details.

Multithreaded Processing

The Azure Data Lake Storage Gen2 origin uses multiple concurrent threads to process data based on the Number of Threads property.

Each thread reads data from a single object, and each object can have a maximum of one thread read from it at a time. The object read order is based on the configuration for the Read Order property.

As the pipeline runs, each thread connects to the origin system, creates a batch of data, and passes the batch to an available pipeline runner. A pipeline runner is a sourceless pipeline instance - an instance of the pipeline that includes all of the processors, executors, and destinations in the pipeline and handles all pipeline processing after the origin.

Each pipeline runner processes one batch at a time, just like a pipeline that runs on a single thread. When the flow of data slows, the pipeline runners wait idly until they are needed, generating an empty batch at regular intervals. You can configure the Runner Idle Time pipeline property to specify the interval or to opt out of empty batch generation.

Multithreaded pipelines preserve the order of records within each batch, just like a single-threaded pipeline. But since batches are processed by different pipeline runners, the order that batches are written to destinations is not ensured.

For example, suppose you configure the origin to use five threads to read objects in the order of last-modified timestamp. When you start the pipeline, the origin creates five threads, and Data Collector creates a matching number of pipeline runners.

The origin assigns a thread to each of the five oldest objects. Each thread processes its assigned object, passing batches of data to the origin. Upon receiving data, the origin passes a batch to each of the pipeline runners for processing.

After a thread completes processing an object, the origin assigns the thread to the next object based on the last-modified timestamp, until all objects are processed.

For more information about multithreaded pipelines, see Multithreaded Pipeline Overview.

Record Header Attributes

When the Azure Data Lake Storage Gen2 origin processes Avro data, it includes the Avro schema in an avroSchema record header attribute. When the origin processes Parquet data and Skip Union Indexes is not enabled, it generates an avro.union.typeIndex./id record header attribute identifying the index number of the element in a union the data is read from.

You can also configure the origin to include Azure Data Lake Storage Gen2 object metadata in record header attributes.

You can use the record:attribute or record:attributeOrDefault functions to access the information in the attributes. For more information about working with record header attributes, see Working with Header Attributes.

Object Metadata in Record Header Attributes

You can include Azure Data Lake Storage Gen2 object metadata in record header attributes. Include metadata when you want to use the information to help process records. For example, you might include metadata if you want to route records to different branches of a pipeline based on the last-modified timestamp.

Enable the Include Metadata property to include metadata in the record header attributes. When you include metadata in record header attributes, the Azure Data Lake Storage Gen2 origin includes the following information:

System-defined metadata

The origin includes the following system-defined metadata:

Creation-Time
Last-Modified
Etag
Content-Length
Content-Encoding
Content-Language
Content-MD5
Content-Disposition
Cache-Control

Custom metadata

The origin includes the following custom metadata:

container
objectKey
file
filename
mtime
size
owner
permissions
continuationToken

For more information about record header attributes, see Record Header Attributes.

Event Generation

The Azure Data Lake Storage Gen2 origin can generate events that you can use in an event stream. With event generation enabled, the origin generates event records each time the origin starts or completes reading an object and after the configured batch wait time has elapsed following all processing of the available data.

Events generated by the Azure Data Lake Storage Gen2 origin can be used in any logical way. For example:

With the Pipeline Finisher executor to stop the pipeline and transition the pipeline to a Finished state when the origin completes processing available data.
When you restart a pipeline stopped by the Pipeline Finisher executor, the origin continues processing from the last-saved offset unless you reset the origin.
For an example, see Stopping a Pipeline After Processing All Available Data.
With a destination to store event information.
For an example, see Preserving an Audit Trail of Events.

Event Records

Event records generated by the Azure Data Lake Storage Gen2 origin have the following event-related record header attributes. Record header attributes are stored as String values:


Record Header Attribute	Description
sdc.event.type	Event type. Uses one of the following types: new-file - Generated when the origin starts processing a new object. finished-file - Generated when the origin completes processing an object. no-more-data - Generated after the origin completes processing all available objects and the number of seconds configured for Batch Wait Time has elapsed.
sdc.event.version	Integer that indicates the version of the event record type.
sdc.event.creation_timestamp	Epoch timestamp when the stage created the event.

The Azure Data Lake Storage Gen2 origin can generate the following types of event records:

new-file

The Azure Data Lake Storage Gen2 origin generates a new-file event record when it starts processing a new object.

New-file event records have the sdc.event.type record header attribute set to new-file and include the following field:


Event Record Field	Description
filepath	Path and name of the object that the origin started processing.

finished-file

The Azure Data Lake Storage Gen2 origin generates a finished-file event record when it finishes processing an object.

Finished-file event records have the sdc.event.type record header attribute set to finished-file and include the following fields:


Event Record Field	Description
filepath	Path and name of the object that the origin finished processing.
record-count	Number of records successfully generated from the object.
error-count	Number of error records generated from the object.

no-more-data

The Azure Data Lake Storage Gen2 origin generates a no-more-data event record when the origin completes processing all available records and the number of seconds configured for Batch Wait Time elapses without any new objects appearing to be processed.

No-more-data event records have the sdc.event.type record header attribute set to no-more-data and include the following fields:


Event Record Field	Description
record-count	Number of records successfully generated since the pipeline started or since the last no-more-data event was created.
error-count	Number of error records generated since the pipeline started or since the last no-more-data event was created.
file-count	Number of objects that the origin attempted to process. Can include objects that were unable to be processed or were not fully processed.

Data Formats

The Azure Data Lake Storage Gen2 origin processes data differently based on the data format. The origin processes the following types of data:

Avro

Generates a record for every Avro record. Includes a precision and scale field attribute for each Decimal field.

The stage includes the Avro schema in an avroSchema record header attribute. You can use one of the following methods to specify the location of the Avro schema definition:

Message/Data Includes Schema - Use the schema in the file.
In Pipeline Configuration - Use the schema that you provide in the stage configuration properties.
Confluent Schema Registry - Retrieve the schema from Confluent Schema Registry. Confluent Schema Registry is a distributed storage layer for Avro schemas. You can configure the stage to look up the schema in Confluent Schema Registry by the schema ID or subject specified in the stage configuration.

Using a schema in the stage configuration or retrieving a schema from Confluent Schema Registry overrides any schema that might be included in the file and can improve performance.

The stage reads files compressed by Avro-supported compression codecs without requiring additional configuration. To enable the stage to read files compressed by other codecs, use the compression format property in the stage.

Delimited

Generates a record for each delimited line.

The CSV parser that you choose determines the delimiter properties that you configure and how the stage handles parsing errors. You can specify if the data includes a header line and whether to use it. You can define the number of lines to skip before reading, the character set of the data, and the root field type to use for the generated record.

You can also configure the stage to replace a string constant with null values and to ignore control characters.

For more information about reading delimited data, see Reading Delimited Data.

Excel

Generates a record for every row in the file. Can process .xls or .xlsx files.

You can configure the origin to read from all sheets in a workbook or from particular sheets in a workbook. You can specify whether files include a header row and whether to ignore the header row. You can also configure the origin to skip cells that do not have a corresponding header value. A header row must be the first row of a file. Vertical header columns are not recognized.

The origin cannot process Excel files with large numbers of rows. You can save such files as CSV files in Excel, and then use the origin to process with the delimited data format.

JSON

Generates a record for each JSON object. You can process JSON files that include multiple JSON objects or a single JSON array.

When an object exceeds the maximum object length defined for the origin, the origin cannot continue processing data in the file. Records already processed from the file are passed to the pipeline. The behavior of the origin is then based on the error handling configured for the stage:

Discard - The origin continues processing with the next file, leaving the partially-processed file in the directory.
To Error - The origin continues processing with the next file. If a post-processing error directory is configured for the stage, the origin moves the partially-processed file to the error directory. Otherwise, it leaves the file in the directory.
Stop Pipeline - The origin stops the pipeline.

Log

Generates a record for every log line.

When a line exceeds the user-defined maximum line length, the origin truncates longer lines.

You can include the processed log line as a field in the record. If the log line is truncated, and you request the log line in the record, the origin includes the truncated line.

You can define the log format or type to be read.

Parquet

The origin generates records for every Parquet record in the file. The file must contain the Parquet schema. The origin uses the Parquet schema to generate records.

The stage includes the Parquet schema in a parquetSchema record header attribute.

When Skip Union Indexes is not enabled, the origin generates an avro.union.typeIndex./id record header attribute identifying the index number of the element in the union that the data is read from. If a schema contains many unions and the pipeline does not depend on index information, you can enable Skip Union Indexes to avoid long processing times associated with storing a large number of indexes.

Protobuf

Generates a record for every protobuf message.

Protobuf messages must match the specified message type and be described in the descriptor file.

When the data for a record exceeds 1 MB, the origin cannot continue processing data in the file. The origin handles the file based on file error handling properties and continues reading the next file.

For information about generating the descriptor file, see Protobuf Data Format Prerequisites.

SDC Record

Generates a record for every record. Use to process records generated by a Data Collector pipeline using the SDC Record data format.

For error records, the origin provides the original record as read from the origin in the original pipeline, as well as error information that you can use to correct the record.

When processing error records, the origin expects the error file names and contents as generated by the original pipeline.

Text

Generates a record for each line of text or for each section of text based on a custom delimiter.

When a line or section exceeds the maximum line length defined for the origin, the origin truncates it. The origin adds a boolean field named Truncated to indicate if the line was truncated.

For more information about processing text with a custom delimiter, see Text Data Format with Custom Delimiters.

Whole File

Streams whole files from the origin system to the destination system. You can specify a transfer rate or use all available resources to perform the transfer.

The origin uses checksums to verify the integrity of data transmission.

The origin generates two fields: one for a file reference and one for file information. For more information, see Whole File Data Format.

XML

Generates records based on a user-defined delimiter element. Use an XML element directly under the root element or define a simplified XPath expression. If you do not define a delimiter element, the origin treats the XML file as a single record.

Generated records include XML attributes and namespace declarations as fields in the record by default. You can configure the stage to include them in the record as field attributes.

You can include XPath information for each parsed XML element and XML attribute in field attributes. This also places each namespace in an xmlns record header attribute.

Note: Field attributes and record header attributes are written to destination systems automatically only when you use the SDC RPC data format in destinations. For more information about working with field attributes and record header attributes, and how to include them in records, see Field Attributes and Record Header Attributes.

When a record exceeds the user-defined maximum record length, the origin cannot continue processing data in the file. Records already processed from the file are passed to the pipeline. The behavior of the origin is then based on the error handling configured for the stage:

Discard - The origin continues processing with the next file, leaving the partially-processed file in the directory.
To Error - The origin continues processing with the next file. If a post-processing error directory is configured for the stage, the origin moves the partially-processed file to the error directory. Otherwise, it leaves the file in the directory.
Stop Pipeline - The origin stops the pipeline.

Use the XML data format to process valid XML documents. For more information about XML processing, see Reading and Processing XML Data.

Tip: If you want to process invalid XML documents, you can try using the text data format with custom delimiters. For more information, see Processing XML Data with Custom Delimiters.

Configuring an Azure Data Lake Storage Gen2 Origin

Configure an Azure Data Lake Storage Gen2 origin to read data from Microsoft Azure Data Lake Storage Gen2. Be sure to complete the necessary prerequisites before you configure the origin.

In the Properties panel, on the General tab, configure the following properties:


General Property	Description
Name	Stage name.
Description	Optional description.
Produce Events	Generates event records when events occur. Use for event handling.
On Record Error	Error record handling for the stage: Discard - Discards the record. Send to Error - Sends the record to the pipeline for error handling. Stop Pipeline - Stops the pipeline.

On the Azure tab, configure the following properties:


Azure Property	Description
Connection	Connection that defines the information required to connect to an external system. To connect to an external system, you can select a connection that contains the details, or you can directly enter the details in the pipeline. When you select a connection, Control Hub hides other properties so that you cannot directly enter connection details in the pipeline.
Account FQDN	The host name of the Data Lake Storage Gen2 account. For example: `<storage account name>.dfs.core.windows.net`
Storage Container / File System	Name of the storage container or file system that contains the data.
Authentication Method	Authentication method used to connect to Azure: OAuth with Service Principal Azure Managed Identity Shared Key
Application ID	Application ID for the Azure Active Directory Data Collector application. Also known as the client ID. For information on accessing the application ID from the Azure portal, see the Azure documentation. Available when using the OAuth with Service Principal or the Azure Managed Identity authentication method.
Endpoint Type	Method to provide endpoint details. Available when using the OAuth with Service Principal authentication method.
Tenant ID	Tenant ID for the Azure Active Directory Data Collector application. Also known as the directory ID. For information on accessing the tenant ID from the Azure portal, see the Azure documentation. Available when Endpoint Type is set to Tenant ID.
Endpoint URL	Endpoint URL for the Azure Active Directory Data Collector application. Default is `https://login.microsoftonline.com/<tenant-id>/oauth2/token`. In the URL, specify the tenant ID for the Azure Active Directory Data Collector application. For information on accessing the tenant ID from the Azure portal, see the Azure documentation. Available when Endpoint Type is set to Endpoint URL.
Application Key	Authentication key or client secret for the Azure Active Directory application. Also known as the client secret. For information on accessing the application key from the Azure portal, see the Azure documentation. Available when using the OAuth with Service Principal authentication method.
Account Shared Key	Shared access key that Azure generated for the storage account. For more information on accessing the shared access key from the Azure portal, see the Azure documentation. Available when using the Shared Key authentication method.
Max Results per Page	Requested number of objects per page. Enter a smaller value to increase the number of pages but simplify error recovery.
Timeout (ms)	Maximum number of milliseconds allowed for Azure to return the requested objects. Negative values indicate no limit. Note that more time is required to return more pages.

On the File Configuration tab, configure the following properties:


File Configuration Property	Description
Common Path	Optional common path that describes the location of the objects in the storage container or file system. When defined, the common path acts as a root path.
Path Pattern	Path pattern that describes the objects to be processed. You can specify the entire path to the objects. You can include Ant-style path patterns to specify objects in nested directories.
Include Metadata	Includes system-defined and custom metadata in record header attributes.
Number of Threads	Number of threads the origin generates and uses for multithreaded processing. Default is 1.
Spooling Period (secs)	Number of seconds between requests for new objects. If more time is required to retrieve new objects, the next request is delayed until the previous request is retrieved.
Delimiter	Character that separates path segments to define a directory hierarchy. Default is slash ( / ).
Read Order	Order to read objects: Lexicographically Ascending Names - Read objects in lexicographically ascending order based on name. Last Modified Timestamp - Read objects in ascending order based on the last-modified timestamp. When objects have matching timestamps, read objects in lexicographically ascending order based on names. For best performance when reading a large number of objects, use lexicographical order based on names.
Object Pool Size	Maximum number of objects that the origin stores in memory for processing. Increasing this number can improve pipeline performance when Data Collector resources permit. Default is 100.
Buffer Limit (KB)	Maximum buffer size. The buffer size determines the size of the record that can be processed. Decrease when memory on the Data Collector machine is limited. Increase to process larger records when memory is available. Default is 128 KB.
File Processing Delay (ms)	The minimum number of milliseconds that must pass from the time a file is created before it is processed. Default is 10000 milliseconds.
Max Batch Size (records)	Maximum number of records processed at one time. Honors values up to the Data Collector maximum batch size. Default is 1000. The Data Collector default is 1000.
Batch Wait Time (ms)	Number of milliseconds to wait before sending a partial or empty batch.

On the Post Processing tab, configure the following properties:


Post Processing Property	Description
Post-Processing Option	Action taken after successfully processing an object: None - Keep the object in place. Archive - Move the object to another location. Delete - Delete the object.
Archiving Option	Method for archiving an object. The origin can move the object to another location. Important: The copy option is not supported at this time. Available when Post-Processing Option is set to Archive.
Post-Processing Path	Path where successfully processed objects are archived. Available when Post-Processing Option is set to Archive.

On the Error Handling tab, configure the following properties:


Error Handling Property	Description
Error Handling Option	Action taken when an error occurs while processing an object: None - Keep the object in place. Archive - Copy or move the object to another location. Delete - Delete the object. When archiving processed objects, best practice is to also archive objects that cannot be processed.
Archiving Option	Method for archiving an object following an error. The origin can either copy or move the object to another location. Copying the object leaves the original object in place. Available when Error Handling Option is set to Archive.
Error Path	Path where objects are archived following an error. Available when Error Handling Option is set to Archive.

On the Data Format tab, configure the following property:


Data Format Property	Description
Data Format	Data format for source files. Use one of the following formats: Avro Delimited Excel JSON Log Parquet Protobuf SDC Record Text Whole File XML

For Avro data, on the Data Format tab, configure the following properties:


Avro Property	Description
Avro Schema Location	Location of the Avro schema definition to use when processing data: Message/Data Includes Schema - Use the schema in the file. In Pipeline Configuration - Use the schema provided in the stage configuration. Confluent Schema Registry - Retrieve the schema from Confluent Schema Registry. Using a schema in the stage configuration or in Confluent Schema Registry can improve performance.
Avro Schema	Avro schema definition used to process the data. Overrides any existing schema definitions associated with the data. You can optionally use the `runtime:loadResource` function to load a schema definition stored in a runtime resource file.
Schema Registry URLs	Confluent Schema Registry URLs used to look up the schema. To add a URL, click Add and then enter the URL in the following format: `http://<host name>:<port number>`
Basic Auth User Info	User information needed to connect to Confluent Schema Registry when using basic authentication. Enter the key and secret from the `schema.registry.basic.auth.user.info` setting in Schema Registry using the following format: `<key>:<secret>` Tip: To secure sensitive information such as user names and passwords, you can use runtime resources or credential stores. For more information about credential stores, see Credential Stores in the Data Collector documentation.
Lookup Schema By	Method used to look up the schema in Confluent Schema Registry: Subject - Look up the specified Avro schema subject. Schema ID - Look up the specified Avro schema ID. Overrides any existing schema definitions associated with the data.
Schema Subject	Avro schema subject to look up in Confluent Schema Registry. If the specified subject has multiple schema versions, the origin uses the latest schema version for that subject. To use an older version, find the corresponding schema ID, and then set the Look Up Schema By property to Schema ID.
Schema ID	Avro schema ID to look up in Confluent Schema Registry.

For delimited data, on the Data Format tab, configure the following properties:


Delimited Property	Description
Header Line	Indicates whether a file contains a header line, and whether to use the header line.
Delimiter Format Type	Delimiter format type. Use one of the following options: Default CSV - File that includes comma-separated values. Ignores empty lines in the file. RFC4180 CSV - Comma-separated file that strictly follows RFC4180 guidelines. MS Excel CSV - Microsoft Excel comma-separated file. MySQL CSV - MySQL comma-separated file. Tab-Separated Values - File that includes tab-separated values. PostgreSQL CSV - PostgreSQL comma-separated file. PostgreSQL Text - PostgreSQL text file. Custom - File that uses user-defined delimiter, escape, and quote characters. Multi Character Delimited - File that uses multiple user-defined characters to delimit fields and lines, and single user-defined escape and quote characters. Available when using the Apache Commons parser type.
Multi Character Field Delimiter	Characters that delimit fields. Default is two pipe characters (\|\|). Available when using the Apache Commons parser with the multi-character delimiter format.
Multi Character Line Delimiter	Characters that delimit lines or records. Default is the newline character (\n). Available when using the Apache Commons parser with the multi-character delimiter format.
Delimiter Character	Delimiter character. Select one of the available options or use Other to enter a custom character. You can enter a Unicode control character using the format `\uNNNN`, where N is a hexadecimal digit from the numbers 0-9 or the letters A-F. For example, enter `\u0000` to use the null character as the delimiter or `\u2028` to use a line separator as the delimiter. Default is the pipe character ( \| ). Available when using the Apache Commons parser with a custom delimiter format.
Field Separator	One or more characters to use as delimiter characters between columns. Available when using the Univocity parser.
Escape Character	Escape character. Available when using the Apache Commons parser with the custom or multi-character delimiter format. Also available when using the Univocity parser.
Quote Character	Quote character. Available when using the Apache Commons parser with the custom or multi-character delimiter format. Also available when using the Univocity parser.
Line Separator	Line separator. Available when using the Univocity parser.
Allow Comments	Allows commented data to be ignored for custom delimiter format. Available when using the Univocity parser.
Comment Character	Character that marks a comment when comments are enabled for custom delimiter format. Available when using the Univocity parser.
Enable Comments	Allows commented data to be ignored for custom delimiter format. Available when using the Apache Commons parser.
Comment Marker	Character that marks a comment when comments are enabled for custom delimiter format. Available when using the Apache Commons parser.
Lines to Skip	Number of lines to skip before reading data.
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.
CSV Parser	Parser to use to process delimited data: Apache Commons - Provides robust parsing and a wide range of delimited format types. Univocity - Can provide faster processing for wide delimited files, such as those with over 200 columns. Default is Apache Commons.
Max Columns	Maximum number of columns to process per record. Available when using the Univocity parser.
Max Character per Column	Maximum number of characters to process in each column. Available when using the Univocity parser.
Skip Empty Lines	Allows skipping empty lines. Available when using the Univocity parser.
Allow Extra Columns	Allows processing records with more columns than exist in the header line. Available when using the Apache Commons parser to process data with a header line.
Extra Column Prefix	Prefix to use for any additional columns. Extra columns are named using the prefix and sequential increasing integers as follows: `<prefix><integer>`. For example, `_extra_1`. Default is `_extra_`. Available when using the Apache Commons parser to process data with a header line while allowing extra columns.
Max Record Length (chars)	Maximum length of a record in characters. Longer records are not read. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size. Available when using the Apache Commons parser.
Ignore Empty Lines	Allows empty lines to be ignored. Available when using the Apache Commons parser with the custom delimiter format.
Root Field Type	Root field type to use: List-Map - Generates an indexed list of data. Enables you to use standard functions to process data. Use for new pipelines. List - Generates a record with an indexed list with a map for header and value. Requires the use of delimited data functions to process data. Use only to maintain pipelines created before 1.1.0.
Parse NULLs	Replaces the specified string constant with null values.
NULL Constant	String constant to replace with null values.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.

For Excel files, on the Data Format tab, configure the following properties:


Excel Property	Description
Excel Header Option	Indicates whether files include a header row and whether to ignore the header row. A header row must be the first row of a file.
Skip Cells With No Header	Skips processing cells when they do not have a corresponding header value. Available when Excel Header Option is set to With Header Line.
Include Cells With Empty Value	Includes empty cells in records.
Read All Sheets	Reads all sheets in the Excel file.
Import Sheets	Name of sheet to read. Using simple or bulk edit mode, click Add Another to add additional sheets. Available when Read All Sheets is not selected.

For Excel files, on the Data Format tab, configure the following properties:


Excel Property	Description
Excel Header Option	Indicates whether files include a header row and whether to ignore the header row. A header row must be the first row of a file.
Skip Cells With No Header	Skips processing cells when they do not have a corresponding header value. Available when Excel Header Option is set to With Header Line.
Include Cells With Empty Value	Includes empty cells in records.
Read All Sheets	Reads all sheets in the Excel file.
Import Sheets	Name of sheet to read. Using simple or bulk edit mode, click Add Another to add additional sheets. Available when Read All Sheets is not selected.

For JSON data, on the Data Format tab, configure the following properties:


JSON Property	Description
JSON Content	Type of JSON content. Use one of the following options: JSON array of objects Multiple JSON objects
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.
Max Object Length (chars)	Maximum number of characters in a JSON object. Longer objects are diverted to the pipeline for error handling. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.

For log data, on the Data Format tab, configure the following properties:


Log Property	Description
Log Format	Format of the log files. Use one of the following options: Common Log Format Combined Log Format Apache Error Log Format Apache Access Log Custom Format Regular Expression Grok Pattern Log4j Common Event Format (CEF) Log Event Extended Format (LEEF)
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.
Max Line Length	Maximum length of a log line. The origin truncates longer lines. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
Retain Original Line	Determines how to treat the original log line. Select to include the original log line as a field in the resulting record. By default, the original line is discarded.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.

When you select Apache Access Log Custom Format, use Apache log format strings to define the Custom Log Format.
When you select Regular Expression, enter the regular expression that describes the log format, and then map the fields that you want to include to each regular expression group.
When you select Grok Pattern, you can use the Grok Pattern Definition field to define custom grok patterns. You can define a pattern on each line.
In the Grok Pattern field, enter the pattern to use to parse the log. You can use a predefined grok patterns or create a custom grok pattern using patterns defined in Grok Pattern Definition.
For more information about defining grok patterns and supported grok patterns, see Defining Grok Patterns.

When you select Log4j, define the following properties:


Log4j Property	Description
On Parse Error	Determines how to handle information that cannot be parsed: Skip and Log Error - Skips reading the line and logs a stage error. Skip, No Error - Skips reading the line and does not log an error. Include as Stack Trace - Includes information that cannot be parsed as a stack trace to the previously-read log line. The information is added to the message field for the last valid log line.
Use Custom Log Format	Allows you to define a custom log format.
Custom Log4J Format	Use log4j variables to define a custom log format.

For Parquet data, on the Data Format tab, configure the following property:


Parquet Property	Description
Skip Union Indexes	Omits header attributes identifying the index number of the element in a union that data is read from. If a schema contains many unions and the pipeline does not depend on index information, you can enable this property to avoid long processing times associated with storing a large number of indexes.

For protobuf data, on the Data Format tab, configure the following properties:


Protobuf Property	Description
Protobuf Descriptor File	Descriptor file (.desc) to use. The descriptor file must be in the Data Collector resources directory, `$SDC_RESOURCES`. For more information about environment variables, see Data Collector Environment Configuration in the Data Collector documentation. For information about generating the descriptor file, see Protobuf Data Format Prerequisites.
Message Type	The fully-qualified name for the message type to use when reading data. Use the following format: `<package name>.<message type>`. Use a message type defined in the descriptor file.
Delimited Messages	Indicates if a file might include more than one protobuf message.
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.

For SDC Record data, on the Data Format tab, configure the following properties:


SDC Record Property	Description
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.

For text data, on the Data Format tab, configure the following properties:


Text Property	Description
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.
Max Line Length	Maximum number of characters allowed for a line. Longer lines are truncated. Adds a boolean field to the record to indicate if it was truncated. The field name is Truncated. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
Use Custom Delimiter	Uses custom delimiters to define records instead of line breaks.
Custom Delimiter	One or more characters to use to define records.
Include Custom Delimiter	Includes delimiter characters in the record.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.

For whole files, on the Data Format tab, configure the following properties:


Whole File Property	Description
Verify Checksum	Verifies the checksum during the read.
Buffer Size (bytes)	Size of the buffer to use to transfer data.
Rate per Second	Transfer rate to use. Enter a number to specify a rate in bytes per second. Use an expression to specify a rate that uses a different unit of measure per second, e.g. ${5 * MB}. Use -1 to opt out of this property. By default, the origin does not use a transfer rate.

For XML data, on the Data Format tab, configure the following properties:


XML Property	Description
Delimiter Element	Delimiter to use to generate records. Omit a delimiter to treat the entire XML document as one record. Use one of the following: An XML element directly under the root element. Use the XML element name without surrounding angle brackets ( < > ) . For example, msg instead of <msg>. A simplified XPath expression that specifies the data to use. Use a simplified XPath expression to access data deeper in the XML document or data that requires a more complex access method. For more information about valid syntax, see Simplified XPath Syntax.
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.
Preserve Root Element	Includes the root element in the generated records. When omitting a delimiter to generate a single record, the root element is the root element of the XML document. When specifying a delimiter to generate multiple records, the root element is the XML element specified as the delimiter element or is the last XML element in the simplified XPath expression specified as the delimiter element.
Include Field XPaths	Includes the XPath to each parsed XML element and XML attribute in field attributes. Also includes each namespace in an xmlns record header attribute. When not selected, this information is not included in the record. By default, the property is not selected. Note: Field attributes and record header attributes are written to destination systems automatically only when you use the SDC RPC data format in destinations. For more information about working with field attributes and record header attributes, and how to include them in records, see Field Attributes and Record Header Attributes.
Namespaces	Namespace prefix and URI to use when parsing the XML document. Define namespaces when the XML element being used includes a namespace prefix or when the XPath expression includes namespaces. For information about using namespaces with an XML element, see Using XML Elements with Namespaces. For information about using namespaces with XPath expressions, see Using XPath Expressions with Namespaces. Using simple or bulk edit mode, click the Add icon to add additional namespaces.
Output Field Attributes	Includes XML attributes and namespace declarations in the record as field attributes. When not selected, XML attributes and namespace declarations are included in the record as fields. Note: Field attributes are automatically included in records written to destination systems only when you use the SDC RPC data format in the destination. For more information about working with field attributes, see Field Attributes. By default, the property is not selected.
Max Record Length (chars)	The maximum number of characters in a record. Longer records are diverted to the pipeline for error handling. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.