import Header from './_source-info-header.md';

Workable#

Workable is an online platform for posting jobs and managing the hiring process. With Workable,
employers can create job listings, receive applications, track candidates, collaborate with team members, schedule interviews, and manage the overall hiring workflow.

This Workable dlt verified source and
pipeline example
loads data using the “Workable API” to the destination of your choice.

Default endpoints#

This verified source loads data from the following default endpoints:

Name	Description
members	Individuals who have access to your Workable account
recruiters	Individuals who are responsible for managing the hiring and recruitment processes
stages	Represent the different steps or phases in the hiring process for a job position
requisitions	Formal requests made by an organization to fill a specific job opening or position
jobs	Individual job postings or job listings created by employers or recruiters
custom_attributes	Additional fields or data points that you can define and assign to candidates or jobs
events	Specific occurrences or actions related to the hiring and recruitment process
candidates	Individuals who have applied for job positions within an organization

Dependent endpoints#

Besides the main endpoints, for the "candidate" and "jobs" endpoints, the following are their dependent endpoints:

Name	Dependent endpoints
candidates//activities	Retrieve activities or events related to the candidate's interaction with the hiring process.
candidates//offer	A specific candidate's offer information
jobs//activities	Activities associated with a particular job posting identified by its shortcode
jobs//application_form	Application form details for a specified job
jobs//questions	Retrieve the interview questions associated with a specific job posting
jobs//stages	Retrieve information about the hiring stages associated with a particular job
jobs//custom_attributes	Retrieve custom attributes associated with a particular job posting
jobs//members	Retrieve information about the members associated with a particular job within the Workable system
jobs//recruiters	Retrieve the list of recruiters associated with a particular job.

Setup guide#

Grab API credentials#

Log into Workable.
Click the top right user icon and select "Settings".
Under "RECRUITING", select "Integrations" on the left.
Find "ACCESS TOKEN" and generate a new token.
Safely copy the new token for pipeline configuration.

Note: The Workable UI, which is described here, might change.
The full guide is available at this link.

Initialize the verified source#

To get started with your data pipeline, follow these steps:

Enter the following command:
```
dlt init workable duckdb
```
This command will initialize
the pipeline example
with Workable as the source and duckdb
as the destination.
If you'd like to use a different destination, simply replace duckdb with the name of your
preferred destination.
After running this command, a new directory will be created with the necessary files and
configuration settings to get started.

Add credentials#

In the .dlt folder, there's a file called secrets.toml. It's where you store sensitive
information securely, like access tokens. Keep this file safe. Here's its format for service
account authentication:
```
# put your secret values and credentials here. do not share this file and do not push it to github
[sources.workable]
access_token = "access_token" # Your Workable token copied above
```
Replace the value of "access_token" with the one that
you copied above. This will ensure that your data pipeline
example can access your Workable resources securely.

Next, you need to configure ".dlt/config.toml", which looks like:

[sources.workable]
subdomain = "subdomain" # please set me up!

Replace the subdomain with the value from the address bar. For example, if your URL is
"https://my-company.workable.com/", use "my-company".
Finally, enter credentials for your chosen destination as per the docs.

For more information, read the General Usage: Credentials.

Run the pipeline#

Before running the pipeline, ensure that you have installed all the necessary dependencies by
running the command:
```
pip install -r requirements.txt
```
You're now ready to run the pipeline! To get started, run the following command:
```
python workable_pipeline.py
```
Once the pipeline has finished running, you can verify that everything loaded correctly by using
the following command:
```
dlt pipeline <pipeline_name> show
```
For example, the pipeline_name for the above pipeline example is workable, you may also use
any custom name instead.

For more information, read the guide on how to run a pipeline.

Sources and resources#

dlt works on the principle of sources and
resources.

Note the default definitions of DEFAULT_ENDPOINTS and DEFAULT_DETAILS in "workable/settings.py".

DEFAULT_ENDPOINTS = ("members", "recruiters", "stages", "requisitions", "jobs", "custom_attributes","events")

DEFAULT_DETAILS = {
    "candidates": ("activities", "offer"),
    "jobs": ("activities", "application_form", "questions", "stages", "custom_attributes", "members", "recruiters" ),
}

Source `workable_source`#

This function loads data from the default and "candidates" endpoints.
Most endpoints in the workable, verified source lack the 'updated_at' key,
necessitating data loading in 'replace' mode. However, the 'candidates'
endpoints allow incremental 'merge' mode loading.

This source returns a sequence of dltResources that correspond to the endpoints.

@dlt.source(name="workable")
def workable_source(
    access_token: str = dlt.secrets.value,
    subdomain: str = dlt.config.value,
    start_date: Optional[DateTime] = None,
    load_details: bool = False,
) -> Iterable[DltResource]:
   ...

access_token: Authenticate the Workable API using the token specified in ".dlt/secrets.toml".

subdomain: Your Workable account name, specified in ".dlt/config.toml".

start_date: Optional. Sets a data retrieval start date; defaults to January 1, 2000.

load_details: A boolean parameter. Set to true to load dependent endpoints with main ones ("jobs"
& "candidates").

Resource `candidate_resource`#

This function is used to retrieve "candidates" endpoints.

@dlt.resource(name="candidates", write_disposition="merge", primary_key="id")
def candidates_resource(
    updated_at: Optional[Any] = dlt.sources.incremental(
        "updated_at", initial_value=workable.start_date_iso
    )
) -> Iterable[TDataItem]:
   ...

updated_at: Uses the dlt.sources.incremental method. Defaults to the function's start_date or Jan
1, 2000 if undefined.

Customization#

Create your own pipeline#

If you wish to create your own pipelines, you can leverage source and resource methods from this verified source.

To create your data pipeline using single loading and incremental data loading (only for the Candidates endpoint), follow these steps:

Configure the pipeline by specifying the pipeline name, destination, and dataset as follows:

pipeline = dlt.pipeline(
     pipeline_name="workable", # Use a custom name if desired
     destination="duckdb", # Choose the appropriate destination (e.g., duckdb, redshift, post)
     dataset_name="workable_data" # Use a custom name if desired
)

To load all data:
```
load_data = workable_source()
load_info = pipeline.run(load_data)
print(load_info)
```
Note: In the run, the "candidates" endpoint loads incrementally via 'merge' mode using 'updated_by'. All other endpoints load in 'replace' mode.
To load data from a specific date, including dependent endpoints:
```
load_data = workable_source(start_date=pendulum.DateTime(2022, 1, 1), load_details=True)
load_info = pipeline.run(load_data)
print(load_info)
```
For instance, the above loads data from January 1, 2022, with corresponding details.

Note: Set the "load_details" parameter to True to load dependent endpoints. Otherwise, use False.
To load custom endpoints “candidates” and “members”:
```
load_info = pipeline.run(load_data.with_resources("candidates", "members"))
# print the information on data that was loaded
print(load_info)
```
Note: "candidates" loads incrementally in merge mode, while "members" uses replace mode.

To load data from the “jobs” endpoint and its dependent endpoints like "activities" and "application_form":

load_data = workable_source(start_date=pendulum.DateTime(2022, 2, 1), load_details=True)
# Set the load_details as True to load all the dependent endpoints.
load_info = pipeline.run(load_data.with_resources("jobs","jobs_activities","jobs_application_form"))
print(load_info)

Note: "load_details" parameter is set to True.

To use incremental loading for the candidates endpoint, maintain the same pipeline and destination dataset names. The pipeline name helps retrieve the state of the last run, essential for incremental data loading. Changing these names might trigger a “dev_mode”, disrupting metadata tracking for incremental data loading.

import Header from './_source-info-header.md';

Workable#

This Workable dlt verified source and
pipeline example
loads data using the “Workable API” to the destination of your choice.

Default endpoints#

This verified source loads data from the following default endpoints:

Name	Description
members	Individuals who have access to your Workable account
recruiters	Individuals who are responsible for managing the hiring and recruitment processes
stages	Represent the different steps or phases in the hiring process for a job position
requisitions	Formal requests made by an organization to fill a specific job opening or position
jobs	Individual job postings or job listings created by employers or recruiters
custom_attributes	Additional fields or data points that you can define and assign to candidates or jobs
events	Specific occurrences or actions related to the hiring and recruitment process
candidates	Individuals who have applied for job positions within an organization

Dependent endpoints#

Besides the main endpoints, for the "candidate" and "jobs" endpoints, the following are their dependent endpoints:

Name	Dependent endpoints
candidates//activities	Retrieve activities or events related to the candidate's interaction with the hiring process.
candidates//offer	A specific candidate's offer information
jobs//activities	Activities associated with a particular job posting identified by its shortcode
jobs//application_form	Application form details for a specified job
jobs//questions	Retrieve the interview questions associated with a specific job posting
jobs//stages	Retrieve information about the hiring stages associated with a particular job
jobs//custom_attributes	Retrieve custom attributes associated with a particular job posting
jobs//members	Retrieve information about the members associated with a particular job within the Workable system
jobs//recruiters	Retrieve the list of recruiters associated with a particular job.

Setup guide#

Grab API credentials#

Log into Workable.
Click the top right user icon and select "Settings".
Under "RECRUITING", select "Integrations" on the left.
Find "ACCESS TOKEN" and generate a new token.
Safely copy the new token for pipeline configuration.

Note: The Workable UI, which is described here, might change.
The full guide is available at this link.

Initialize the verified source#

To get started with your data pipeline, follow these steps:

Enter the following command:
```
dlt init workable duckdb
```
This command will initialize
the pipeline example
with Workable as the source and duckdb
as the destination.
If you'd like to use a different destination, simply replace duckdb with the name of your
preferred destination.
After running this command, a new directory will be created with the necessary files and
configuration settings to get started.

Add credentials#

In the .dlt folder, there's a file called secrets.toml. It's where you store sensitive
information securely, like access tokens. Keep this file safe. Here's its format for service
account authentication:
```
# put your secret values and credentials here. do not share this file and do not push it to github
[sources.workable]
access_token = "access_token" # Your Workable token copied above
```
Replace the value of "access_token" with the one that
you copied above. This will ensure that your data pipeline
example can access your Workable resources securely.

Next, you need to configure ".dlt/config.toml", which looks like:

[sources.workable]
subdomain = "subdomain" # please set me up!

Replace the subdomain with the value from the address bar. For example, if your URL is
"https://my-company.workable.com/", use "my-company".
Finally, enter credentials for your chosen destination as per the docs.

For more information, read the General Usage: Credentials.

Run the pipeline#

Before running the pipeline, ensure that you have installed all the necessary dependencies by
running the command:
```
pip install -r requirements.txt
```
You're now ready to run the pipeline! To get started, run the following command:
```
python workable_pipeline.py
```
Once the pipeline has finished running, you can verify that everything loaded correctly by using
the following command:
```
dlt pipeline <pipeline_name> show
```
For example, the pipeline_name for the above pipeline example is workable, you may also use
any custom name instead.

For more information, read the guide on how to run a pipeline.

Sources and resources#

dlt works on the principle of sources and
resources.

Note the default definitions of DEFAULT_ENDPOINTS and DEFAULT_DETAILS in "workable/settings.py".

DEFAULT_ENDPOINTS = ("members", "recruiters", "stages", "requisitions", "jobs", "custom_attributes","events")

DEFAULT_DETAILS = {
    "candidates": ("activities", "offer"),
    "jobs": ("activities", "application_form", "questions", "stages", "custom_attributes", "members", "recruiters" ),
}

Source `workable_source`#

This source returns a sequence of dltResources that correspond to the endpoints.

@dlt.source(name="workable")
def workable_source(
    access_token: str = dlt.secrets.value,
    subdomain: str = dlt.config.value,
    start_date: Optional[DateTime] = None,
    load_details: bool = False,
) -> Iterable[DltResource]:
   ...

access_token: Authenticate the Workable API using the token specified in ".dlt/secrets.toml".

subdomain: Your Workable account name, specified in ".dlt/config.toml".

start_date: Optional. Sets a data retrieval start date; defaults to January 1, 2000.

load_details: A boolean parameter. Set to true to load dependent endpoints with main ones ("jobs"
& "candidates").

Resource `candidate_resource`#

This function is used to retrieve "candidates" endpoints.

@dlt.resource(name="candidates", write_disposition="merge", primary_key="id")
def candidates_resource(
    updated_at: Optional[Any] = dlt.sources.incremental(
        "updated_at", initial_value=workable.start_date_iso
    )
) -> Iterable[TDataItem]:
   ...

updated_at: Uses the dlt.sources.incremental method. Defaults to the function's start_date or Jan
1, 2000 if undefined.

Customization#

Create your own pipeline#

If you wish to create your own pipelines, you can leverage source and resource methods from this verified source.

To create your data pipeline using single loading and incremental data loading (only for the Candidates endpoint), follow these steps:

Configure the pipeline by specifying the pipeline name, destination, and dataset as follows:

pipeline = dlt.pipeline(
     pipeline_name="workable", # Use a custom name if desired
     destination="duckdb", # Choose the appropriate destination (e.g., duckdb, redshift, post)
     dataset_name="workable_data" # Use a custom name if desired
)

To load all data:
```
load_data = workable_source()
load_info = pipeline.run(load_data)
print(load_info)
```
Note: In the run, the "candidates" endpoint loads incrementally via 'merge' mode using 'updated_by'. All other endpoints load in 'replace' mode.
To load data from a specific date, including dependent endpoints:
```
load_data = workable_source(start_date=pendulum.DateTime(2022, 1, 1), load_details=True)
load_info = pipeline.run(load_data)
print(load_info)
```
For instance, the above loads data from January 1, 2022, with corresponding details.

Note: Set the "load_details" parameter to True to load dependent endpoints. Otherwise, use False.
To load custom endpoints “candidates” and “members”:
```
load_info = pipeline.run(load_data.with_resources("candidates", "members"))
# print the information on data that was loaded
print(load_info)
```
Note: "candidates" loads incrementally in merge mode, while "members" uses replace mode.

To load data from the “jobs” endpoint and its dependent endpoints like "activities" and "application_form":

load_data = workable_source(start_date=pendulum.DateTime(2022, 2, 1), load_details=True)
# Set the load_details as True to load all the dependent endpoints.
load_info = pipeline.run(load_data.with_resources("jobs","jobs_activities","jobs_application_form"))
print(load_info)

Note: "load_details" parameter is set to True.

To use incremental loading for the candidates endpoint, maintain the same pipeline and destination dataset names. The pipeline name helps retrieve the state of the last run, essential for incremental data loading. Changing these names might trigger a “dev_mode”, disrupting metadata tracking for incremental data loading.

Workable#

Default endpoints#

Dependent endpoints#

Setup guide#

Grab API credentials#

Initialize the verified source#

Add credentials#

Run the pipeline#

Sources and resources#

Source workable_source#

Resource candidate_resource#

Customization#

Create your own pipeline#

Workable#

Default endpoints#

Dependent endpoints#

Setup guide#

Grab API credentials#

Initialize the verified source#

Add credentials#

Run the pipeline#

Sources and resources#

Source workable_source#

Resource candidate_resource#

Customization#

Create your own pipeline#

Source `workable_source`#

Resource `candidate_resource`#

Source `workable_source`#

Resource `candidate_resource`#