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Load Zendesk Data to The Local Filesystem Using dlt in Python

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Zendesk is award-winning customer service software trusted by over 200K customers. It enables customer support through text, mobile, phone, email, live chat, and social media. This documentation will guide you on how to load data from Zendesk to The Local Filesystem using the open-source Python library dlt. The The Local Filesystem destination stores data in a local folder, allowing you to create data lakes easily. You can store data in formats such as JSONL, Parquet, or CSV. For further information about Zendesk, visit their website.

dlt Key Features

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go.
  • Incremental Loading: Learn how to do incremental loading in consecutive runs with dlt. The state of your incremental loads will be persisted in your selected destination and restored and used on each new load, making it very easy to keep your loaded dataset up to date with the source. Learn more
  • Similarity Searching with Qdrant: Conduct similarity searches on your vectorized Zendesk tickets data using Qdrant's vectorization and similarity searching capabilities. Learn more
  • Import Ticket Data to Weaviate: Import Zendesk ticket data to Weaviate for advanced analysis such as identifying common issues and sentiment analysis. Learn more
  • Advanced Deployment Options: Use dlt init with branches, local folders, or git repos to deploy your projects flexibly. Learn more

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 The Local Filesystem:

pip install "dlt[filesystem]"

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 The Local Filesystem. You can run the following commands to create a starting point for loading data from Zendesk to The Local Filesystem:

# create a new directory
mkdir zendesk_pipeline
cd zendesk_pipeline
# initialize a new pipeline with your source and destination
dlt init zendesk filesystem
# 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[filesystem]>=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.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 The Local Filesystem destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to a local directory, remove the [destination.filesystem.credentials] section from your secrets.toml and provide a local filepath as the bucket_url.

[destination.filesystem] # in ./dlt/secrets.toml
bucket_url="file://path/to/my/output"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem',
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='filesystem',
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='filesystem',
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='filesystem',
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 The Local Filesystem 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: Automate your deployment process using Github Actions. This guide provides step-by-step instructions to set up CI/CD for your dlt pipelines.
  • Deploy with Airflow: Leverage the power of Airflow and Google Composer to manage your dlt pipelines. Follow the detailed guide here.
  • Deploy with Google Cloud Functions: Use Google Cloud Functions to deploy your dlt pipelines. Check out the comprehensive guide here.
  • Explore Other Deployment Options: Discover various other methods to deploy your dlt pipelines by exploring the resources here.

The running in production section will teach you about:

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure it runs smoothly and efficiently. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified about any issues or important events in your dlt pipeline, helping you to react quickly and keep things running smoothly. Set up alerts
  • Set up tracing: Implement tracing to get detailed insights into the performance and behavior of your dlt pipeline, making it easier to debug and optimize. And set up tracing

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 NameWrite DispositionDescription
chatsmergeThis 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 NameWrite DispositionDescription
activitiesreplaceActivities in Zendesk are logs of all the changes and updates made to tickets.
automationsreplaceAutomations in Zendesk are business rules you define that run in the background of your support operations.
brandsreplaceBrands in Zendesk allow you to manage multiple brands in a single account.
custom_agent_rolesreplaceCustom agent roles in Zendesk allow you to customize the access and permissions of your agents.
dynamic_contentreplaceDynamic content in Zendesk allows you to write one version of an automated response and then translate it into other languages.
group_membershipsreplaceGroup memberships in Zendesk define the groups an agent belongs to.
groupsreplaceGroups in Zendesk are used to route tickets to the right people in your organization.
job_statusreplaceJob statuses in Zendesk provide information about the completion status of a job.
macrosreplaceMacros in Zendesk are predefined responses or sets of actions.
organization_fieldsreplaceOrganization fields in Zendesk are custom fields you can create for organizations.
organization_membershipsreplaceOrganization memberships in Zendesk define the organizations a user belongs to.
organizationsreplaceOrganizations in Zendesk are groups of your end-users.
recipient_addressesreplaceRecipient addresses in Zendesk are the email addresses associated with your support address.
requestsreplaceRequests in Zendesk are the tickets that end-users submit.
satisfaction_ratingsreplaceSatisfaction ratings in Zendesk are the ratings that your end-users can give to your support.
sharing_agreementsreplaceSharing agreements in Zendesk define how tickets are shared with other Zendesk instances.
skipsreplaceSkips in Zendesk are tickets that were skipped in a play queue.
sla_policiesreplaceSLA policies in Zendesk are the service level agreements you define for your tickets.
suspended_ticketsreplaceSuspended tickets in Zendesk are tickets that have been marked as spam or have been suspended for some other reason.
tagsreplaceTags in Zendesk are keywords or phrases that you can add to tickets.
targetsreplaceTargets in Zendesk are third-party applications or services that you can send notifications to.
ticket_eventsappendTicket events in Zendesk are the changes and updates made to a ticket.
ticket_fieldsreplaceTicket fields in Zendesk are the fields that a ticket form is composed of.
ticket_formsreplaceTicket forms in Zendesk are the different forms you can create for different types of tickets.
ticket_metric_eventsappendTicket metric events in Zendesk are the metrics associated with a ticket.
ticket_metricsreplaceTicket metrics in Zendesk are the metrics associated with a ticket.
ticketsmergeTickets in Zendesk are the means by which your end-users request help from your team.
triggersreplaceTriggers in Zendesk are business rules you define that automatically perform actions based on certain conditions.
user_fieldsreplaceUser fields in Zendesk are custom fields you can create for users.
usersreplaceUsers in Zendesk are anyone who interacts with the Zendesk product.
viewsreplaceViews 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 NameWrite DispositionDescription
addressesreplaceThis resource contains the addresses associated with Zendesk Talk.
agents_activityreplaceThis resource contains information about the activity of agents in Zendesk Talk.
callsreplaceThis resource contains information about the calls made in Zendesk Talk.
calls_incrementalmergeThis resource contains incremental information about the calls made in Zendesk Talk.
current_queue_activityreplaceThis resource contains information about the current activity in the queue of Zendesk Talk.
greeting_categoriesreplaceThis resource contains information about the categories of greetings in Zendesk Talk.
greetingsreplaceThis resource contains information about the greetings used in Zendesk Talk.
ivrsreplaceThis resource contains information about the Interactive Voice Response (IVR) systems in Zendesk Talk.
legs_incrementalmergeThis resource contains incremental information about the legs (or parts) of calls in Zendesk Talk.
linesreplaceThis resource contains information about the lines used in Zendesk Talk.
phone_numbersreplaceThis resource contains information about the phone numbers associated with Zendesk Talk.
settingsreplaceThis resource contains information about the settings configured in Zendesk Talk.

Additional pipeline guides

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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