Python Guide: Loading Zendesk Data to Azure Cloud Storage with dlt
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This documentation provides an overview of how to use the open-source Python library, dlt
, to load data from Zendesk
, a renowned customer service software used by over 200,000 customers, to Azure Cloud Storage
, a filesystem that enables the creation of data lakes on Microsoft Azure. The data can be uploaded in various formats including JSONL, Parquet, or CSV. Detailed information about Zendesk
can be found at https://www.zendesk.de. By using dlt
, you can seamlessly integrate data from Zendesk
into your Azure Cloud Storage
, thereby enhancing your data management and analytics capabilities.
dlt
Key Features
- Zendesk Verified Source: This Zendesk verified source allows you to easily extract data from Zendesk's APIs and load it into your desired destination.
- Pipeline Metadata:
dlt
pipelines leverage metadata to provide robust governance support. This includes load IDs for tracking data loads and facilitating data lineage and traceability. Read more about it here. - Schema Enforcement and Curation:
dlt
allows users to enforce and curate schemas, ensuring data consistency and quality. Learn more about it here. - Similarity Searching with Qdrant: Learn how to use
dlt
to store your vectorized Zendesk tickets data in thedlt
destination: Qdrant. This guide provides a step-by-step tutorial on how to perform similarity searches on your tickets data. - Import Ticket Data from Zendesk API to Weaviate: This guide shows you how to import ticket data from Zendesk API to Weaviate, a vector database, for advanced analysis.
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 Azure Cloud Storage
:
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 Azure Cloud Storage
. You can run the following commands to create a starting point for loading data from Zendesk
to Azure Cloud Storage
:
# 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
The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials]
section in your secrets.toml
.
[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"
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 Azure Cloud Storage
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: Learn how to deploy your pipeline using GitHub Actions. This guide provides detailed steps on setting up a GitHub repository, ensuring your pipeline works, and configuring GitHub Actions to run your pipeline at scheduled intervals.
- Deploy with Airflow: Follow this guide to deploy your pipeline using Airflow. It covers setting up an Airflow environment and configuring your pipeline to run as an Airflow DAG.
- Deploy with Google Cloud Functions: This guide explains how to use Google Cloud Functions to deploy your pipeline. It includes instructions on setting up your Google Cloud environment and deploying your pipeline as a cloud function.
- Explore other deployment options: Discover various other methods to deploy your pipeline by visiting the deployment walkthroughs section. This includes guides for different environments and tools.
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. Read more - Set up alerts: Set up alerts to stay informed about the status and performance of your
dlt
pipeline, helping you to quickly address any issues that arise. Read more - And set up tracing: Implement tracing in your
dlt
pipeline to get detailed insights into the data flow and the performance of each pipeline component. Read more
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. |
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