Python Guide: Loading Zendesk Data to BigQuery with `dlt`

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This technical documentation provides instructions on how to utilize the open-source Python library, dlt, to load data from Zendesk to BigQuery. Zendesk is an award-winning customer service software trusted by over 200K customers, providing multi-channel support through text, mobile, phone, email, live chat, and social media. On the other hand, BigQuery is a serverless, cost-effective enterprise data warehouse that operates across various cloud platforms and scales with your data. Using dlt, you can efficiently transfer data from Zendesk to BigQuery for further analysis and insights. More information about Zendesk can be found at https://www.zendesk.de.

`dlt` Key Features

Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage.
Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read more: Adjust a schema docs.
Schema Evolution: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, dlt notifies stakeholders, allowing them to take necessary actions. Read more about schema evolution.
Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines. This includes running extraction, normalization, and load in parallel, and fine-tuning the memory buffers, intermediary file sizes, and compression options. Read more about performance.
Community Support: dlt is a constantly growing library that supports many features and use cases needed by the community. Join our Slack to find recent releases or discuss what you can build with dlt.

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for BigQuery:

pip install "dlt[bigquery]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Zendesk to BigQuery. You can run the following commands to create a starting point for loading data from Zendesk to BigQuery:

# create a new directory
mkdir zendesk_pipeline
cd zendesk_pipeline
# initialize a new pipeline with your source and destination
dlt init zendesk bigquery
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt[bigquery]>=0.3.8

You now have the following folder structure in your project:

zendesk_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── zendesk/                   # folder with source specific files
│   └── ...
├── zendesk_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.zendesk.credentials]
subdomain = "subdomain" # please set me up!
email = "email" # please set me up!
password = "password" # please set me up!

[destination.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations

Read more about setting up the Zendesk source in our docs.
Read more about setting up the BigQuery destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at zendesk_pipeline.py, as well as a folder zendesk that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

import time
from typing import Any

import dlt
from dlt.common.time import timedelta
from zendesk import (
    pendulum,
    zendesk_chat,
    zendesk_talk,
    zendesk_support,
    make_date_ranges,
)


def incremental_load_all_default() -> Any:
    """
    Loads all possible tables for Zendesk Support, Chat, Talk
    """
    # FULL PIPELINE RUN
    pipeline = dlt.pipeline(
        pipeline_name="dlt_zendesk_pipeline",
        destination='bigquery',
        full_refresh=False,
        dataset_name="sample_zendesk_data3",
    )

    # zendesk support source function
    data_support = zendesk_support(load_all=True)
    # zendesk chat source function
    data_chat = zendesk_chat()
    # zendesk talk source function
    data_talk = zendesk_talk()
    # run pipeline with all 3 sources
    info = pipeline.run(data=[data_support, data_chat, data_talk])
    return info


def load_support_with_pivoting() -> Any:
    """
    Loads Zendesk Support data with pivoting. Simply done by setting the pivot_ticket_fields to true - default option. Loads only the base tables.
    """
    pipeline = dlt.pipeline(
        pipeline_name="zendesk_support_pivoting",
        destination='bigquery',
        full_refresh=False,
    )
    data = zendesk_support(load_all=False, pivot_ticket_fields=True)
    info = pipeline.run(data=data)
    return info


def incremental_load_all_start_date() -> Any:
    """
    Implements incremental load when possible to Support, Chat and Talk Endpoints. The default behaviour gets data since the last load time saved in dlt state or
    1st Jan 2000 if there has been no previous loading of the resource. With this setting, the sources will load data since the given data for all incremental endpoints.
    Last load time will still be updated.
    """

    # Choosing starting point for incremental load - optional, the default is the last load time. If no last load time
    # the start time will be the 1st day of the millennium
    # start time needs to be a pendulum datetime object
    start_date = pendulum.DateTime(year=2023, month=1, day=1).in_timezone("UTC")

    pipeline = dlt.pipeline(
        pipeline_name="dlt_zendesk_pipeline",
        destination='bigquery',
        full_refresh=False,
        dataset_name="sample_zendesk_data",
    )
    data = zendesk_support(load_all=True, start_date=start_date)
    data_chat = zendesk_chat(start_date=start_date)
    data_talk = zendesk_talk(start_date=start_date)
    info = pipeline.run(data=[data, data_chat, data_talk])
    return info


def incremental_load_with_backloading() -> Any:
    """Backload historic data in ranges. In this method we load all tickets so far created since Jan 1st 2023 but one week at a time
    and then switch to incrementally loading new tickets.
    This can useful to reduce the potiential failure window when loading large amounts of historic data.
    This approach can be used with all incremental Zendesk sources.
    """
    pipeline = dlt.pipeline(
        pipeline_name="dlt_zendesk_pipeline",
        destination='bigquery',
        full_refresh=False,
        dataset_name="sample_zendesk_data",
    )

    # Load ranges of dates to load between January 1st 2023 and today
    min_start_date = pendulum.DateTime(year=2023, month=1, day=1).in_timezone("UTC")
    max_end_date = pendulum.today()
    # Generate tuples of date ranges, each with 1 week in between.
    ranges = make_date_ranges(min_start_date, max_end_date, timedelta(weeks=1))

    # Run the pipeline in a loop for each 1 week range
    for start, end in ranges:
        print(f"Loading tickets between {start} and {end}")
        data = zendesk_support(start_date=start, end_date=end).with_resources("tickets")
        info = pipeline.run(data=data)
        print(info)

    # Backloading is done, now we continue loading with incremental state, starting where the backloading left off
    print(f"Loading with incremental state, starting at {end}")
    data = zendesk_support(start_date=end).with_resources("tickets")
    info = pipeline.run(data)
    print(info)


if __name__ == "__main__":
    # simple run where everything is loaded
    start = time.time()
    load_info = incremental_load_all_default()
    end = time.time()
    print(load_info)
    print(f"Time taken: {end-start}")

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python zendesk_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline dlt_zendesk_pipeline info

You can also use streamlit to inspect the contents of your BigQuery destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline dlt_zendesk_pipeline show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

Deploy with GitHub Actions: Use the dlt deploy command to prepare your pipeline for deployment using GitHub Actions. This CI/CD runner allows you to schedule your pipeline using cron schedule expressions.
Deploy with Airflow and Google Composer: Google Composer is a managed Airflow environment provided by Google. dlt can create an Airflow DAG for your pipeline script, making the deployment process straightforward.
Deploy with Google Cloud Functions: dlt can also be deployed using Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
Other Deployment Methods: There are other methods you can use to deploy your dlt pipeline. Check out the other deployment methods for more information.

The running in production section will teach you about:

Monitor Your Pipeline: dlt provides the tools needed to effectively monitor your pipeline. You can easily keep track of your pipeline's status, inspect load packages, and view schema changes. Learn more about monitoring your pipeline here.
Set Up Alerts: With dlt, you can set up alerts to be notified of any significant events or changes in your pipeline. This feature helps you to quickly respond to any issues and keep your pipeline running smoothly. Find out how to set up alerts here.
Set Up Tracing: dlt also allows you to set up tracing for your pipeline. With tracing, you can gain insights into the execution of your pipeline, making it easier to debug and optimize. Learn more about setting up tracing here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source `zendesk_chat`

Zendesk Chat source provides detailed chat session data including user information and messages.

Resource Name	Write Disposition	Description
`chats`	`merge`	This resource includes details about the chats in Zendesk. It includes information such as whether the chat has been deleted, the id, message, details about the session (browser, city, country code, country name, end date, id, ip, platform, region, start date, user agent), timestamp, type of chat, whether it is unread, update timestamp, and details about the visitor (email, id, name, notes, phone).

Source `zendesk_support`

The source 'zendesk_support' provides comprehensive data on customer service interactions, including ticket details, user information, and organizational metrics.

Resource Name	Write Disposition	Description
`activities`	`replace`	Activities in Zendesk are logs of all the changes and updates made to tickets.
`automations`	`replace`	Automations in Zendesk are business rules you define that run in the background of your support operations.
`brands`	`replace`	Brands in Zendesk allow you to manage multiple brands in a single account.
`custom_agent_roles`	`replace`	Custom agent roles in Zendesk allow you to customize the access and permissions of your agents.
`dynamic_content`	`replace`	Dynamic content in Zendesk allows you to write one version of an automated response and then translate it into other languages.
`group_memberships`	`replace`	Group memberships in Zendesk define the groups an agent belongs to.
`groups`	`replace`	Groups in Zendesk are used to route tickets to the right people in your organization.
`job_status`	`replace`	Job statuses in Zendesk provide information about the completion status of a job.
`macros`	`replace`	Macros in Zendesk are predefined responses or sets of actions.
`organization_fields`	`replace`	Organization fields in Zendesk are custom fields you can create for organizations.
`organization_memberships`	`replace`	Organization memberships in Zendesk define the organizations a user belongs to.
`organizations`	`replace`	Organizations in Zendesk are groups of your end-users.
`recipient_addresses`	`replace`	Recipient addresses in Zendesk are the email addresses associated with your support address.
`requests`	`replace`	Requests in Zendesk are the tickets that end-users submit.
`satisfaction_ratings`	`replace`	Satisfaction ratings in Zendesk are the ratings that your end-users can give to your support.
`sharing_agreements`	`replace`	Sharing agreements in Zendesk define how tickets are shared with other Zendesk instances.
`skips`	`replace`	Skips in Zendesk are tickets that were skipped in a play queue.
`sla_policies`	`replace`	SLA policies in Zendesk are the service level agreements you define for your tickets.
`suspended_tickets`	`replace`	Suspended tickets in Zendesk are tickets that have been marked as spam or have been suspended for some other reason.
`tags`	`replace`	Tags in Zendesk are keywords or phrases that you can add to tickets.
`targets`	`replace`	Targets in Zendesk are third-party applications or services that you can send notifications to.
`ticket_events`	`append`	Ticket events in Zendesk are the changes and updates made to a ticket.
`ticket_fields`	`replace`	Ticket fields in Zendesk are the fields that a ticket form is composed of.
`ticket_forms`	`replace`	Ticket forms in Zendesk are the different forms you can create for different types of tickets.
`ticket_metric_events`	`append`	Ticket metric events in Zendesk are the metrics associated with a ticket.
`ticket_metrics`	`replace`	Ticket metrics in Zendesk are the metrics associated with a ticket.
`tickets`	`merge`	Tickets in Zendesk are the means by which your end-users request help from your team.
`triggers`	`replace`	Triggers in Zendesk are business rules you define that automatically perform actions based on certain conditions.
`user_fields`	`replace`	User fields in Zendesk are custom fields you can create for users.
`users`	`replace`	Users in Zendesk are anyone who interacts with the Zendesk product.
`views`	`replace`	Views in Zendesk are collections of tickets based on certain criteria.

Source `zendesk_talk`

"Zendesk_talk" provides data on call activity, agent activity, settings, greetings, and queue status.

Resource Name	Write Disposition	Description
`addresses`	`replace`	This resource contains the addresses associated with Zendesk Talk.
`agents_activity`	`replace`	This resource contains information about the activity of agents in Zendesk Talk.
`calls`	`replace`	This resource contains information about the calls made in Zendesk Talk.
`calls_incremental`	`merge`	This resource contains incremental information about the calls made in Zendesk Talk.
`current_queue_activity`	`replace`	This resource contains information about the current activity in the queue of Zendesk Talk.
`greeting_categories`	`replace`	This resource contains information about the categories of greetings in Zendesk Talk.
`greetings`	`replace`	This resource contains information about the greetings used in Zendesk Talk.
`ivrs`	`replace`	This resource contains information about the Interactive Voice Response (IVR) systems in Zendesk Talk.
`legs_incremental`	`merge`	This resource contains incremental information about the legs (or parts) of calls in Zendesk Talk.
`lines`	`replace`	This resource contains information about the lines used in Zendesk Talk.
`phone_numbers`	`replace`	This resource contains information about the phone numbers associated with Zendesk Talk.
`settings`	`replace`	This resource contains information about the settings configured in Zendesk Talk.

Python Guide: Loading Zendesk Data to BigQuery with `dlt`

`dlt` Key Features

Getting started with your pipeline locally

0. Prerequisites

1. Install dlt

2. Configuring your source and destination credentials

generated config.toml

generated secrets.toml

2.1. Adjust the generated code to your usecase

3. Running your pipeline for the first time

4. Inspecting your load result

5. Next steps to get your pipeline running in production

The Deploy section will show you how to deploy your pipeline to

The running in production section will teach you about:

Available Sources and Resources

Source `zendesk_chat`

Source `zendesk_support`

Source `zendesk_talk`

Additional pipeline guides

DHelp

Ask a question

Python Guide: Loading Zendesk Data to BigQuery with dlt

dlt Key Features​

Getting started with your pipeline locally​

0. Prerequisites​

1. Install dlt​

2. Configuring your source and destination credentials​

generated config.toml​

generated secrets.toml​

2.1. Adjust the generated code to your usecase​

3. Running your pipeline for the first time​

4. Inspecting your load result​

5. Next steps to get your pipeline running in production​

The Deploy section will show you how to deploy your pipeline to​

The running in production section will teach you about:​

Available Sources and Resources​

Source zendesk_chat​

Source zendesk_support​

Source zendesk_talk​

Additional pipeline guides​

DHelp

Ask a question

Python Guide: Loading Zendesk Data to BigQuery with `dlt`

`dlt` Key Features

Getting started with your pipeline locally

0. Prerequisites

1. Install dlt

2. Configuring your source and destination credentials

generated config.toml

generated secrets.toml

2.1. Adjust the generated code to your usecase

3. Running your pipeline for the first time

4. Inspecting your load result

5. Next steps to get your pipeline running in production

The Deploy section will show you how to deploy your pipeline to

The running in production section will teach you about:

Available Sources and Resources

Source `zendesk_chat`

Source `zendesk_support`

Source `zendesk_talk`

Additional pipeline guides