Copy data from Google Cloud Storage using Azure Data Factory or Synapse Analytics

Статия
10/20/2023

APPLIES TO: Azure Data Factory Azure Synapse Analytics

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This article outlines how to copy data from Google Cloud Storage (GCS). To learn more, read the introductory articles for Azure Data Factory and Synapse Analytics.

Supported capabilities

This Google Cloud Storage connector is supported for the following capabilities:

Supported capabilities	IR
Copy activity (source/-)	① ②
Mapping data flow (source/-)	①
Lookup activity	① ②
GetMetadata activity	① ②
Delete activity	① ②

① Azure integration runtime ② Self-hosted integration runtime

Specifically, this Google Cloud Storage connector supports copying files as is or parsing files with the supported file formats and compression codecs. It takes advantage of GCS's S3-compatible interoperability.

Prerequisites

The following setup is required on your Google Cloud Storage account:

Enable interoperability for your Google Cloud Storage account
Set the default project that contains the data you want to copy from the target GCS bucket.
Create a service account and define the right levels of permissions by using Cloud IAM on GCP.
Generate the access keys for this service account.

Retrieve access key for Google Cloud Storage

Required permissions

To copy data from Google Cloud Storage, make sure you've been granted the following permissions for object operations: storage.objects.get and storage.objects.list.

If you use UI to author, additional storage.buckets.list permission is required for operations like testing connection to linked service and browsing from root. If you don't want to grant this permission, you can choose "Test connection to file path" or "Browse from specified path" options from the UI.

For the full list of Google Cloud Storage roles and associated permissions, see IAM roles for Cloud Storage on the Google Cloud site.

Getting started

To perform the Copy activity with a pipeline, you can use one of the following tools or SDKs:

Create a linked service to Google Cloud Storage using UI

Use the following steps to create a linked service to Google Cloud Storage in the Azure portal UI.

Browse to the Manage tab in your Azure Data Factory or Synapse workspace and select Linked Services, then click New:
- Azure Data Factory
- Azure Synapse
Search for Google and select the Google Cloud Storage (S3 API) connector.
Configure the service details, test the connection, and create the new linked service.

Connector configuration details

The following sections provide details about properties that are used to define Data Factory entities specific to Google Cloud Storage.

Linked service properties

The following properties are supported for Google Cloud Storage linked services:

Property	Description	Required
type	The type property must be set to GoogleCloudStorage.	Yes
accessKeyId	ID of the secret access key. To find the access key and secret, see Prerequisites.	Yes
secretAccessKey	The secret access key itself. Mark this field as SecureString to store it securely, or reference a secret stored in Azure Key Vault.	Yes
serviceUrl	Specify the custom GCS endpoint as `https://storage.googleapis.com`.	Yes
connectVia	The integration runtime to be used to connect to the data store. You can use the Azure integration runtime or the self-hosted integration runtime (if your data store is in a private network). If this property isn't specified, the service uses the default Azure integration runtime.	No

Here's an example:

{
    "name": "GoogleCloudStorageLinkedService",
    "properties": {
        "type": "GoogleCloudStorage",
        "typeProperties": {
            "accessKeyId": "<access key id>",
            "secretAccessKey": {
                "type": "SecureString",
                "value": "<secret access key>"
            },
            "serviceUrl": "https://storage.googleapis.com"
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Dataset properties

Azure Data Factory supports the following file formats. Refer to each article for format-based settings.

The following properties are supported for Google Cloud Storage under location settings in a format-based dataset:

Property	Description	Required
type	The type property under `location` in the dataset must be set to GoogleCloudStorageLocation.	Yes
bucketName	The GCS bucket name.	Yes
folderPath	The path to folder under the given bucket. If you want to use a wildcard to filter the folder, skip this setting and specify that in activity source settings.	No
fileName	The file name under the given bucket and folder path. If you want to use a wildcard to filter the files, skip this setting and specify that in activity source settings.	No

Example:

{
    "name": "DelimitedTextDataset",
    "properties": {
        "type": "DelimitedText",
        "linkedServiceName": {
            "referenceName": "<Google Cloud Storage linked service name>",
            "type": "LinkedServiceReference"
        },
        "schema": [ < physical schema, optional, auto retrieved during authoring > ],
        "typeProperties": {
            "location": {
                "type": "GoogleCloudStorageLocation",
                "bucketName": "bucketname",
                "folderPath": "folder/subfolder"
            },
            "columnDelimiter": ",",
            "quoteChar": "\"",
            "firstRowAsHeader": true,
            "compressionCodec": "gzip"
        }
    }
}

Copy activity properties

For a full list of sections and properties available for defining activities, see the Pipelines article. This section provides a list of properties that the Google Cloud Storage source supports.

Google Cloud Storage as a source type

Azure Data Factory supports the following file formats. Refer to each article for format-based settings.

The following properties are supported for Google Cloud Storage under storeSettings settings in a format-based copy source:

Property	Description	Required
type	The type property under `storeSettings` must be set to GoogleCloudStorageReadSettings.	Yes
Locate the files to copy:
OPTION 1: static path	Copy from the given bucket or folder/file path specified in the dataset. If you want to copy all files from a bucket or folder, additionally specify `wildcardFileName` as `*`.
OPTION 2: GCS prefix - prefix	Prefix for the GCS key name under the given bucket configured in the dataset to filter source GCS files. GCS keys whose names start with `bucket_in_dataset/this_prefix` are selected. It utilizes GCS's service-side filter, which provides better performance than a wildcard filter.	No
OPTION 3: wildcard - wildcardFolderPath	The folder path with wildcard characters under the given bucket configured in a dataset to filter source folders. Allowed wildcards are: `*` (matches zero or more characters) and `?` (matches zero or single character). Use `^` to escape if your folder name has a wildcard or this escape character inside. See more examples in Folder and file filter examples.	No
OPTION 3: wildcard - wildcardFileName	The file name with wildcard characters under the given bucket and folder path (or wildcard folder path) to filter source files. Allowed wildcards are: `*` (matches zero or more characters) and `?` (matches zero or single character). Use `^` to escape if your file name has a wildcard or this escape character inside. See more examples in Folder and file filter examples.	Yes
OPTION 3: a list of files - fileListPath	Indicates to copy a given file set. Point to a text file that includes a list of files you want to copy, one file per line, which is the relative path to the path configured in the dataset. When you're using this option, do not specify the file name in the dataset. See more examples in File list examples.	No
Additional settings:
recursive	Indicates whether the data is read recursively from the subfolders or only from the specified folder. Note that when recursive is set to true and the sink is a file-based store, an empty folder or subfolder isn't copied or created at the sink. Allowed values are true (default) and false. This property doesn't apply when you configure `fileListPath`.	No
deleteFilesAfterCompletion	Indicates whether the binary files will be deleted from source store after successfully moving to the destination store. The file deletion is per file, so when copy activity fails, you will see some files have already been copied to the destination and deleted from source, while others are still remaining on source store. This property is only valid in binary files copy scenario. The default value: false.	No
modifiedDatetimeStart	Files are filtered based on the attribute: last modified. The files will be selected if their last modified time is greater than or equal to `modifiedDatetimeStart` and less than `modifiedDatetimeEnd`. The time is applied to the UTC time zone in the format of "2018-12-01T05:00:00Z". The properties can be NULL, which means no file attribute filter will be applied to the dataset. When `modifiedDatetimeStart` has a datetime value but `modifiedDatetimeEnd` is NULL, the files whose last modified attribute is greater than or equal to the datetime value will be selected. When `modifiedDatetimeEnd` has a datetime value but `modifiedDatetimeStart` is NULL, the files whose last modified attribute is less than the datetime value will be selected. This property doesn't apply when you configure `fileListPath`.	No
modifiedDatetimeEnd	Same as above.	No
enablePartitionDiscovery	For files that are partitioned, specify whether to parse the partitions from the file path and add them as additional source columns. Allowed values are false (default) and true.	No
partitionRootPath	When partition discovery is enabled, specify the absolute root path in order to read partitioned folders as data columns. If it is not specified, by default, - When you use file path in dataset or list of files on source, partition root path is the path configured in dataset. - When you use wildcard folder filter, partition root path is the sub-path before the first wildcard. For example, assuming you configure the path in dataset as "root/folder/year=2020/month=08/day=27": - If you specify partition root path as "root/folder/year=2020", copy activity will generate two more columns `month` and `day` with value "08" and "27" respectively, in addition to the columns inside the files. - If partition root path is not specified, no extra column will be generated.	No
maxConcurrentConnections	The upper limit of concurrent connections established to the data store during the activity run. Specify a value only when you want to limit concurrent connections.	No

Example:

"activities":[
    {
        "name": "CopyFromGoogleCloudStorage",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<Delimited text input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "DelimitedTextSource",
                "formatSettings":{
                    "type": "DelimitedTextReadSettings",
                    "skipLineCount": 10
                },
                "storeSettings":{
                    "type": "GoogleCloudStorageReadSettings",
                    "recursive": true,
                    "wildcardFolderPath": "myfolder*A",
                    "wildcardFileName": "*.csv"
                }
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

Folder and file filter examples

This section describes the resulting behavior of the folder path and file name with wildcard filters.

bucket	key	recursive	Source folder structure and filter result (files in bold are retrieved)
bucket	`Folder/`	false	bucket FolderA File1.csv File2.json Subfolder1 File3.csv File4.json File5.csv AnotherFolderB File6.csv
bucket	`Folder/`	true	bucket FolderA File1.csv File2.json Subfolder1 File3.csv File4.json File5.csv AnotherFolderB File6.csv
bucket	`Folder/.csv`	false	bucket FolderA File1.csv File2.json Subfolder1 File3.csv File4.json File5.csv AnotherFolderB File6.csv
bucket	`Folder/.csv`	true	bucket FolderA File1.csv File2.json Subfolder1 File3.csv File4.json File5.csv AnotherFolderB File6.csv

File list examples

This section describes the resulting behavior of using a file list path in the Copy activity source.

Assume that you have the following source folder structure and want to copy the files in bold:

Sample source structure	Content in FileListToCopy.txt	Configuration
bucket FolderA File1.csv File2.json Subfolder1 File3.csv File4.json File5.csv Metadata FileListToCopy.txt	File1.csv Subfolder1/File3.csv Subfolder1/File5.csv	In dataset: - Bucket: `bucket` - Folder path: `FolderA` In copy activity source: - File list path: `bucket/Metadata/FileListToCopy.txt` The file list path points to a text file in the same data store that includes a list of files you want to copy, one file per line, with the relative path to the path configured in the dataset.

Mapping data flow properties

When you're transforming data in mapping data flows, you can read files from Google Cloud Storage in the following formats:

Format specific settings are located in the documentation for that format. For more information, see Source transformation in mapping data flow.

Source transformation

In source transformation, you can read from a container, folder, or individual file in Google Cloud Storage. Use the Source options tab to manage how the files are read.

Screenshot of Source options.

Wildcard paths: Using a wildcard pattern will instruct the service to loop through each matching folder and file in a single source transformation. This is an effective way to process multiple files within a single flow. Add multiple wildcard matching patterns with the plus sign that appears when you hover over your existing wildcard pattern.

From your source container, choose a series of files that match a pattern. Only a container can be specified in the dataset. Your wildcard path must therefore also include your folder path from the root folder.

Wildcard examples:

* Represents any set of characters.
** Represents recursive directory nesting.
? Replaces one character.
[] Matches one or more characters in the brackets.
/data/sales/**/*.csv Gets all .csv files under /data/sales.
/data/sales/20??/**/ Gets all files in the 20th century.
/data/sales/*/*/*.csv Gets .csv files two levels under /data/sales.
/data/sales/2004/*/12/[XY]1?.csv Gets all .csv files in December 2004 starting with X or Y prefixed by a two-digit number.

Partition root path: If you have partitioned folders in your file source with a key=value format (for example, year=2019), then you can assign the top level of that partition folder tree to a column name in your data flow's data stream.

First, set a wildcard to include all paths that are the partitioned folders plus the leaf files that you want to read.

Screenshot of partition source file settings.

Use the Partition root path setting to define what the top level of the folder structure is. When you view the contents of your data via a data preview, you'll see that the service will add the resolved partitions found in each of your folder levels.

Screenshot of partition root path.

List of files: This is a file set. Create a text file that includes a list of relative path files to process. Point to this text file.

Column to store file name: Store the name of the source file in a column in your data. Enter a new column name here to store the file name string.

After completion: Choose to do nothing with the source file after the data flow runs, delete the source file, or move the source file. The paths for the move are relative.

To move source files to another location post-processing, first select "Move" for file operation. Then, set the "from" directory. If you're not using any wildcards for your path, then the "from" setting will be the same folder as your source folder.

If you have a source path with wildcard, your syntax will look like this:

/data/sales/20??/**/*.csv

You can specify "from" as:

/data/sales

And you can specify "to" as:

/backup/priorSales

In this case, all files that were sourced under /data/sales are moved to /backup/priorSales.

Note

File operations run only when you start the data flow from a pipeline run (a pipeline debug or execution run) that uses the Execute Data Flow activity in a pipeline. File operations do not run in Data Flow debug mode.

Filter by last modified: You can filter which files you process by specifying a date range of when they were last modified. All datetimes are in UTC.

Lookup activity properties

To learn details about the properties, check Lookup activity.

GetMetadata activity properties

To learn details about the properties, check GetMetadata activity.

Delete activity properties

To learn details about the properties, check Delete activity.

Legacy models

If you were using an Amazon S3 connector to copy data from Google Cloud Storage, it's still supported as is for backward compatibility. We suggest that you use the new model mentioned earlier. The authoring UI has switched to generating the new model.

For a list of data stores that the Copy activity supports as sources and sinks, see Supported data stores.

Споделяне чрез

Copy data from Google Cloud Storage using Azure Data Factory or Synapse Analytics

Supported capabilities

Prerequisites

Required permissions

Getting started

Create a linked service to Google Cloud Storage using UI

Connector configuration details

Linked service properties

Dataset properties

Copy activity properties

Google Cloud Storage as a source type

Folder and file filter examples

File list examples

Mapping data flow properties

Source transformation

Lookup activity properties

GetMetadata activity properties

Delete activity properties

Legacy models

Обратна връзка

Допълнителни ресурси

Споделяне чрез

Copy data from Google Cloud Storage using Azure Data Factory or Synapse Analytics

Supported capabilities

Prerequisites

Required permissions

Getting started

Create a linked service to Google Cloud Storage using UI

Connector configuration details

Linked service properties

Dataset properties

Copy activity properties

Google Cloud Storage as a source type

Folder and file filter examples

File list examples

Mapping data flow properties

Source transformation

Lookup activity properties

GetMetadata activity properties

Delete activity properties

Legacy models

Related content

Обратна връзка

Допълнителни ресурси