Microsoft SharePoint

Availability:

Airbyte Cloud Airbyte OSS PyAirbyte

Support Level:

Airbyte Connector

Connector Version:

0.5.2(Last updated a month ago)

CDK Version:

4.5.4

Sync Success Rate:

Usage Rate:

This page contains the setup guide and reference information for the Microsoft SharePoint source connector.

Prerequisites

• Application (client) ID
• Directory (tenant) ID
• Drive name
• Folder Path
• Client secrets

Setup guide

Set up Microsoft SharePoint

For Airbyte Cloud:

1. Log into your Airbyte Cloud account.
2. Click Sources and then click + New source.
3. On the Set up the source page, select Microsoft SharePoint from the Source type dropdown.
4. Enter a name for the Microsoft SharePoint connector.
5. Select Search Scope. Specifies the location(s) to search for files. Valid options are 'ACCESSIBLE_DRIVES' for all SharePoint drives the user can access, 'SHARED_ITEMS' for shared items the user has access to, and 'ALL' to search both. Default value is 'ALL'.
6. Enter Folder Path. Leave empty to search all folders of the drives. This does not apply to shared items.
7. The OAuth2.0 authorization method is selected by default. Click Authenticate your Microsoft SharePoint account. Log in and authorize your Microsoft account.
8. For Start Date, enter the date in YYYY-MM-DD format. The data added on and after this date will be replicated.
9. Add a stream:
   1. Write the File Type
   2. In the Format box, use the dropdown menu to select the format of the files you'd like to replicate. The supported formats are CSV, Parquet, Avro and JSONL. Toggling the Optional fields button within the Format box will allow you to enter additional configurations based on the selected format. For a detailed breakdown of these settings, refer to the File Format section below.
   3. Give a Name to the stream
   4. (Optional) - If you want to enforce a specific schema, you can enter a Input schema. By default, this value is set to {} and will automatically infer the schema from the file(s) you are replicating. For details on providing a custom schema, refer to the User Schema section.
   5. Optionally, enter the Globs which dictates which files to be synced. This is a regular expression that allows Airbyte to pattern match the specific files to replicate. If you are replicating all the files within your bucket, use ** as the pattern. For more precise pattern matching options, refer to the Path Patterns section below.
10. Click Set up source

For Airbyte Open Source:

1. Navigate to the Airbyte Open Source dashboard.
2. Click Sources and then click + New source.
3. On the Set up the source page, select Microsoft SharePoint from the Source type dropdown.
4. Enter a name for the Microsoft SharePoint connector.

Step 1: Set up SharePoint application

The Microsoft Graph API uses OAuth for authentication. Microsoft Graph exposes granular permissions that control the access that apps have to resources, like users, groups, and mail. When a user signs in to your app, they or in some cases an administrator are given a chance to consent to these permissions. If the user consents, your app is given access to the resources and APIs that it has requested. For apps that don't take a signed-in user, permissions can be pre-consented to by an administrator when the app is installed.

Microsoft Graph has two types of permissions:

• Delegated permissions are used by apps that have a signed-in user present. For these apps, either the user or an administrator consents to the permissions that the app requests, and the app can act as the signed-in user when making calls to Microsoft Graph. Some delegated permissions can be consented by non-administrative users, but some higher-privileged permissions require administrator consent.
• Application permissions are used by apps that run without a signed-in user present; for example, apps that run as background services or daemons. Application permissions can only be consented by an administrator.

This source requires Application permissions. Follow these instructions for creating an app in the Azure portal. This process will produce the client_id, client_secret, and tenant_id needed for the tap configuration file.

1. Login to Azure Portal
2. Click upper-left menu icon and select Azure Active Directory
3. Select App Registrations
4. Click New registration
5. Register an application
   1. Name:
   2. Supported account types: Accounts in this organizational directory only
   3. Register (button)
6. Record the client_id and tenant_id which will be used by the tap for authentication and API integration.
7. Select Certificates & secrets
8. Provide Description and Expires
   1. Description: tap-microsoft-teams client secret
   2. Expires: 1-year
   3. Add
9. Copy the client secret value, this will be the client_secret
10. Select API permissions
    1. Click Add a permission
11. Select Microsoft Graph
12. Select Application permissions
13. Select the following permissions:
    1. Files
       • Files.Read.All
14. Click Add permissions
15. Click Grant admin consent

Step 2: Set up the Microsoft SharePoint connector in Airbyte

1. Navigate to the Airbyte Open Source dashboard.
2. Click Sources and then click + New source.
3. On the Set up the source page, select Microsoft SharePoint from the Source type dropdown.
4. Enter the name for the Microsoft SharePoint connector.
5. Select Search Scope. Specifies the location(s) to search for files. Valid options are 'ACCESSIBLE_DRIVES' for all SharePoint drives the user can access, 'SHARED_ITEMS' for shared items the user has access to, and 'ALL' to search both. Default value is 'ALL'.
6. Enter Folder Path. Leave empty to search all folders of the drives. This does not apply to shared items.
7. Switch to Service Key Authentication
8. For User Practical Name, enter the UPN for your user.
9. Enter Tenant ID, Client ID and Client secret.
10. For Start Date, enter the date in YYYY-MM-DD format. The data added on and after this date will be replicated.
11. Add a stream:
    1. Write the File Type
    2. In the Format box, use the dropdown menu to select the format of the files you'd like to replicate. The supported formats are CSV, Parquet, Avro and JSONL. Toggling the Optional fields button within the Format box will allow you to enter additional configurations based on the selected format. For a detailed breakdown of these settings, refer to the File Format section below.
    3. Give a Name to the stream
    4. (Optional) - If you want to enforce a specific schema, you can enter a Input schema. By default, this value is set to {} and will automatically infer the schema from the file(s) you are replicating. For details on providing a custom schema, refer to the User Schema section.
    5. Optionally, enter the Globs which dictates which files to be synced. This is a regular expression that allows Airbyte to pattern match the specific files to replicate. If you are replicating all the files within your bucket, use ** as the pattern. For more precise pattern matching options, refer to the Path Patterns section below.
12. Click Set up source

Path Patterns

(tl;dr -> path pattern syntax using wcmatch.glob. GLOBSTAR and SPLIT flags are enabled.)

This connector can sync multiple files by using glob-style patterns, rather than requiring a specific path for every file. This enables:

• Referencing many files with just one pattern, e.g. ** would indicate every file in the folder.
• Referencing future files that don't exist yet (and therefore don't have a specific path).

You must provide a path pattern. You can also provide many patterns split with | for more complex directory layouts.

Each path pattern is a reference from the root of the folder, so don't include the root folder name itself in the pattern(s).

Some example patterns:

• ** : match everything.
• **/*.csv : match all files with specific extension.
• myFolder/**/*.csv : match all csv files anywhere under myFolder.
• */** : match everything at least one folder deep.
• */*/*/** : match everything at least three folders deep.
• **/file.*|**/file : match every file called "file" with any extension (or no extension).
• x/*/y/* : match all files that sit in sub-folder x -> any folder -> folder y.
• **/prefix*.csv : match all csv files with specific prefix.
• **/prefix*.parquet : match all parquet files with specific prefix.

Let's look at a specific example, matching the following folder layout (MyFolder is the folder specified in the connector config as the root folder, which the patterns are relative to):

MyFolder
    -> log_files
    -> some_table_files
        -> part1.csv
        -> part2.csv
    -> images
    -> more_table_files
        -> part3.csv
    -> extras
        -> misc
            -> another_part1.csv

We want to pick up part1.csv, part2.csv and part3.csv (excluding another_part1.csv for now). We could do this a few different ways:

• We could pick up every csv file called "partX" with the single pattern **/part*.csv.
• To be a bit more robust, we could use the dual pattern some_table_files/*.csv|more_table_files/*.csv to pick up relevant files only from those exact folders.
• We could achieve the above in a single pattern by using the pattern *table_files/*.csv. This could however cause problems in the future if new unexpected folders started being created.
• We can also recursively wildcard, so adding the pattern extras/**/*.csv would pick up any csv files nested in folders below "extras", such as "extras/misc/another_part1.csv".

As you can probably tell, there are many ways to achieve the same goal with path patterns. We recommend using a pattern that ensures clarity and is robust against future additions to the directory structure.

User Schema

When using the Avro, Jsonl, CSV or Parquet format, you can provide a schema to use for the output stream. Note that this doesn't apply to the experimental Document file type format.

Providing a schema allows for more control over the output of this stream. Without a provided schema, columns and datatypes will be inferred from the first created file in the bucket matching your path pattern and suffix. This will probably be fine in most cases but there may be situations you want to enforce a schema instead, e.g.:

• You only care about a specific known subset of the columns. The other columns would all still be included, but packed into the _ab_additional_properties map.
• Your initial dataset is quite small (in terms of number of records), and you think the automatic type inference from this sample might not be representative of the data in the future.
• You want to purposely define types for every column.
• You know the names of columns that will be added to future data and want to include these in the core schema as columns rather than have them appear in the _ab_additional_properties map.

Or any other reason! The schema must be provided as valid JSON as a map of {"column": "datatype"} where each datatype is one of:

• string
• number
• integer
• object
• array
• boolean
• null

For example:

• {"id": "integer", "location": "string", "longitude": "number", "latitude": "number"}
• {"username": "string", "friends": "array", "information": "object"}

File Format Settings

CSV

Since CSV files are effectively plain text, providing specific reader options is often required for correct parsing of the files. These settings are applied when a CSV is created or exported so please ensure that this process happens consistently over time.

• Header Definition: How headers will be defined. User Provided assumes the CSV does not have a header row and uses the headers provided and Autogenerated assumes the CSV does not have a header row and the CDK will generate headers using for f{i} where i is the index starting from 0. Else, the default behavior is to use the header from the CSV file. If a user wants to autogenerate or provide column names for a CSV having headers, they can set a value for the "Skip rows before header" option to ignore the header row.
• Delimiter: Even though CSV is an acronym for Comma Separated Values, it is used more generally as a term for flat file data that may or may not be comma separated. The delimiter field lets you specify which character acts as the separator. To use tab-delimiters, you can set this value to \t. By default, this value is set to ,.
• Double Quote: This option determines whether two quotes in a quoted CSV value denote a single quote in the data. Set to True by default.
• Encoding: Some data may use a different character set (typically when different alphabets are involved). See the list of allowable encodings here. By default, this is set to utf8.
• Escape Character: An escape character can be used to prefix a reserved character and ensure correct parsing. A commonly used character is the backslash (\). For example, given the following data:

Product,Description,Price
Jeans,"Navy Blue, Bootcut, 34\"",49.99

The backslash (\) is used directly before the second double quote (") to indicate that it is not the closing quote for the field, but rather a literal double quote character that should be included in the value (in this example, denoting the size of the jeans in inches: 34" ).

Leaving this field blank (default option) will disallow escaping.

• False Values: A set of case-sensitive strings that should be interpreted as false values.
• Null Values: A set of case-sensitive strings that should be interpreted as null values. For example, if the value 'NA' should be interpreted as null, enter 'NA' in this field.
• Quote Character: In some cases, data values may contain instances of reserved characters (like a comma, if that's the delimiter). CSVs can handle this by wrapping a value in defined quote characters so that on read it can parse it correctly. By default, this is set to ".
• Skip Rows After Header: The number of rows to skip after the header row.
• Skip Rows Before Header: The number of rows to skip before the header row.
• Strings Can Be Null: Whether strings can be interpreted as null values. If true, strings that match the null_values set will be interpreted as null. If false, strings that match the null_values set will be interpreted as the string itself.
• True Values: A set of case-sensitive strings that should be interpreted as true values.

Parquet

Apache Parquet is a column-oriented data storage format of the Apache Hadoop ecosystem. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. At the moment, partitioned parquet datasets are unsupported. The following settings are available:

• Convert Decimal Fields to Floats: Whether to convert decimal fields to floats. There is a loss of precision when converting decimals to floats, so this is not recommended.

Avro

The Avro parser uses the Fastavro library. The following settings are available:

• Convert Double Fields to Strings: Whether to convert double fields to strings. This is recommended if you have decimal numbers with a high degree of precision because there can be a loss precision when handling floating point numbers.

JSONL

There are currently no options for JSONL parsing.

Document File Type Format (Experimental)

warning

The Document file type format is currently an experimental feature and not subject to SLAs. Use at your own risk.

The Document file type format is a special format that allows you to extract text from Markdown, TXT, PDF, Word, Powerpoint and Google documents. If selected, the connector will extract text from the documents and output it as a single field named content. The document_key field will hold a unique identifier for the processed file which can be used as a primary key. The content of the document will contain markdown formatting converted from the original file format. Each file matching the defined glob pattern needs to either be a markdown (md), PDF (pdf) or Docx (docx) file.

One record will be emitted for each document. Keep in mind that large files can emit large records that might not fit into every destination as each destination has different limitations for string fields.

Before parsing each document, the connector exports Google Document files to Docx format internally. Google Sheets, Google Slides, and drawings are internally exported and parsed by the connector as PDFs.

Supported sync modes

The Microsoft SharePoint source connector supports the following sync modes:

Feature	Supported?(Yes/No)
Full Refresh Sync	Yes
Incremental Sync	Yes

Supported Streams

There is no predefined streams. The streams are based on content of files were added on the Set up page.

Performance considerations

The connector is restricted by normal Microsoft Graph requests limitation.

Data type map

Integration Type	Airbyte Type
string	string
number	number
array	array
object	object

Reference

Config fields reference

Field

Type

Property name

array<object>

streams

object

credentials

string

start_date

string

search_scope

string

folder_path

Changelog

Expand to review

Version	Date	Pull Request	Subject
0.5.2	2024-08-24	45646	Fix: handle wrong folder name
0.5.1	2024-08-24	44660	Update dependencies
0.5.0	2024-08-19	42983	Migrate to CDK v4.5.1
0.4.5	2024-08-19	44382	Update dependencies
0.4.4	2024-08-12	43743	Update dependencies
0.4.3	2024-08-10	43565	Update dependencies
0.4.2	2024-08-03	43235	Update dependencies
0.4.1	2024-07-27	42704	Update dependencies
0.4.0	2024-07-25	42008	Migrate to CDK v3.5.3
0.3.1	2024-07-20	42143	Update dependencies
0.3.0	2024-07-16	42007	Migrate to CDK v2.4.0
0.2.11	2024-07-13	41688	Update dependencies
0.2.10	2024-07-10	41589	Update dependencies
0.2.9	2024-07-06	40917	Update dependencies
0.2.8	2024-06-26	40539	Update dependencies
0.2.7	2024-06-25	40357	Update dependencies
0.2.6	2024-06-24	40233	Update dependencies
0.2.5	2024-06-22	39987	Update dependencies
0.2.4	2024-05-29	38675	Avoid error on empty stream when running discover
0.2.3	2024-04-17	37372	Make refresh token optional
0.2.2	2024-03-28	36573	Update QL to 400
0.2.1	2024-03-22	36381	Unpin CDK
0.2.0	2024-03-06	35830	Add fetching shared items
0.1.0	2024-01-25	33537	New source