Loading Data from Aladtec to Azure Cloud Storage with dlt in Python

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Aladtec is a robust scheduling and workforce management system designed for public safety and healthcare organizations. It helps streamline employee scheduling, manage shift trades, and track work hours efficiently. With Aladtec, organizations can optimize staffing, reduce scheduling conflicts, and ensure compliance with labor laws. The platform offers features like automated scheduling, time-off requests, and detailed reporting to enhance productivity and operational efficiency.

This documentation provides guidance on loading data from Aladtec to Azure Cloud Storage using the open-source python library dlt. Azure Cloud Storage stores data on Microsoft Azure, enabling the creation of data lakes. You can upload data as JSONL, Parquet, or CSV. Further information about the source is available at Aladtec.

dlt Key Features

• Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data lineage. Learn more
• Schema Enforcement and Curation: Ensure data consistency and quality by enforcing and curating schemas. Learn more
• Schema Evolution: Proactively manage schema changes with alerts and notifications, helping maintain data integrity. Learn more
• Scaling and Finetuning: Scale up and fine-tune pipelines with parallel processing, thread pools, and memory buffer adjustments. Learn more
• Scalability via Iterators and Parallelization: Efficiently process large datasets by leveraging iterators, chunking, and parallelization techniques. Learn more

Getting started with your pipeline locally

OpenAPI Source Generator dlt-init-openapi

This walkthrough makes use of the dlt-init-openapi generator cli tool. You can read more about it here. The code generated by this tool uses the dlt rest_api verified source, docs for this are here.

0. Prerequisites

dlt and dlt-init-openapi requires Python 3.9 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 and dlt-init-openapi

First you need to install the dlt-init-openapi cli tool.

pip install dlt-init-openapi

The dlt-init-openapi cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.

# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you 
# can just hit Enter and all will be rendered.
dlt-init-openapi aladtec --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/aladtec.yaml --global-limit 2
cd aladtec_pipeline
# install generated requirements
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>=0.4.12

You now have the following folder structure in your project:

aladtec_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── rest_api/                # The rest api verified source
│   └── ...
├── aladtec/                
│   └── __init__.py          # TODO: possibly tweak this file
├── aladtec_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

1.1. Tweak aladtec/__init__.py

This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api source set up in a different way. The generated file for the aladtec source will look like this:

Click to view full file (380 lines)

from typing import List

import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig


@dlt.source(name="aladtec_source", max_table_nesting=2)
def aladtec_source(
        token: str = dlt.secrets.value,
        base_url: str = dlt.config.value,
) -> List[DltResource]:

    # source configuration
    source_config: RESTAPIConfig = {
        "client": {
            "base_url": base_url,
            "auth": {
                
            "type": "bearer",
            "token": token,

            },
            "paginator": {
                "type":
                    "offset",
                "limit":
                    100,
                "offset_param":
                    "offset",
                "limit_param":
                    "limit",
                "total_path":
                    "",
                "maximum_offset":
                    20,
            },
        },
        "resources": 
        [
        # Returns a list of all accrual banks.
        {
            "name": "accrual_bank",
            "table_name": "accrual_bank",
            "endpoint": {
                "data_selector": "data",
                "path": "/accrual-banks",
            }
        },
        # Returns Member Availability for the requested date/time range.
        {
            "name": "availability",
            "table_name": "availability",
            "endpoint": {
                "data_selector": "data",
                "path": "/availability",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter

                },
            }
        },
        # Returns the number of hours in each specified accrual bank for a list of provided members.
        {
            "name": "balance",
            "table_name": "balance",
            "endpoint": {
                "data_selector": "data",
                "path": "/accrual-banks/balances",
                "params": {
                    "member_ids": "FILL_ME_IN", # TODO: fill in required query parameter
                    "accrual_bank_ids": "FILL_ME_IN", # TODO: fill in required query parameter

                },
            }
        },
        # Members clocked in at the time of the request.
        {
            "name": "clocked_in_member",
            "table_name": "clocked_in_member",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-clock-time/clocked-in-members",
            }
        },
        # Returns a list of configuration settings.
        {
            "name": "configuration",
            "table_name": "configuration",
            "endpoint": {
                "data_selector": "$",
                "path": "/configuration",
            }
        },
        # Returns all customer created Member Database attribute definitions.
        {
            "name": "custom_attribute",
            "table_name": "custom_attribute",
            "endpoint": {
                "data_selector": "data",
                "path": "/members/custom-attributes",
            }
        },
        # Returns all employee types. These can be customized per Aladtec system. Examples: part time, full time, volunteer.
        {
            "name": "employee_type",
            "table_name": "employee_type",
            "endpoint": {
                "data_selector": "data",
                "path": "/members/employee-types",
            }
        },
        # Events for requested date/time range.
        {
            "name": "event",
            "table_name": "event",
            "endpoint": {
                "data_selector": "data",
                "path": "/events",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "daily_split_time": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns extra hours ranges for a specified period of time in the past.
        {
            "name": "extra_hour",
            "table_name": "extra_hour",
            "endpoint": {
                "data_selector": "data",
                "path": "/extra-hours",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "schedule_ids": "OPTIONAL_CONFIG",
                    # "member_ids": "OPTIONAL_CONFIG",
                    # "position_qualification_ids": "OPTIONAL_CONFIG",
                    # "time_type_ids": "OPTIONAL_CONFIG",
                    # "statuses": "approved",
                    # "daily_split_time": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns a list of unique, active Kiosks used to clock in and out.
        {
            "name": "kiosk",
            "table_name": "kiosk",
            "primary_key": "kiosk_id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-clock/kiosks",
            }
        },
        # Returns members and the members' associated Member Database attributes. Attributes must be accessible through the API or the value will be null. Contact Aladtec Support (support@aladtec.com, 888.749.5550) to make attributes accessible through the API.
        {
            "name": "member",
            "table_name": "member",
            "primary_key": "member_id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "data",
                "path": "/members",
                "params": {
                    # the parameters below can optionally be configured
                    # "member_ids": "OPTIONAL_CONFIG",
                    # "include_inactive": "false",
                    # "attribute_ids": "OPTIONAL_CONFIG",

                },
            }
        },
        # Schedule and position for each member scheduled at the time of the request.
        {
            "name": "members_scheduled_now",
            "table_name": "members_scheduled_now",
            "endpoint": {
                "data_selector": "data",
                "path": "/scheduled-time/members-scheduled-now",
                "params": {
                    # the parameters below can optionally be configured
                    # "schedule_ids": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns Schedule Notes grouped by calendar date. Up to one year can be retrieved in a single request.
        {
            "name": "note",
            "table_name": "note",
            "endpoint": {
                "data_selector": "data",
                "path": "/scheduled-time/notes",
                "params": {
                    "range_start_date": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_date": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "schedule_ids": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns time ranges where no member is scheduled. Typically used for finding shifts needing coverage. Up to one month can be retrieved in a single request.
        {
            "name": "open_time",
            "table_name": "open_time",
            "endpoint": {
                "data_selector": "data",
                "path": "/scheduled-time/open-time",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "schedule_ids": "OPTIONAL_CONFIG",
                    # "position_ids": "OPTIONAL_CONFIG",
                    # "include_only_shift_time": "true",
                    # "daily_split_time": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns a list of unique, active Paycodes which can be applied to time clock time.
        {
            "name": "paycode",
            "table_name": "paycode",
            "primary_key": "paycode_id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-clock/paycodes",
            }
        },
        # Runs the Payroll Report export.  <strong>Pagination is required</strong> to export all records within the requested range. 
        {
            "name": "payroll",
            "table_name": "payroll",
            "endpoint": {
                "data_selector": "data",
                "path": "/reports/payroll",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "member_ids": "OPTIONAL_CONFIG",
                    # "time_categories": "OPTIONAL_CONFIG",
                    # "next_token": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns a list of unique position qualifications.
        {
            "name": "position_qualification",
            "table_name": "position_qualification",
            "endpoint": {
                "data_selector": "data",
                "path": "/schedules/position-qualifications",
            }
        },
        # Schedule configuration defined in an Aladtec system. Data is sorted by the order defined on the Setup -> Schedules page.
        {
            "name": "schedule",
            "table_name": "schedule",
            "primary_key": "schedule_id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "data",
                "path": "/schedules",
                "params": {
                    # the parameters below can optionally be configured
                    # "include_archived": "false",

                },
            }
        },
        # Returns scheduled time ranges for a specified period of time
        {
            "name": "scheduled_time",
            "table_name": "scheduled_time",
            "endpoint": {
                "data_selector": "data",
                "path": "/scheduled-time",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "schedule_ids": "OPTIONAL_CONFIG",
                    # "member_ids": "OPTIONAL_CONFIG",
                    # "position_ids": "OPTIONAL_CONFIG",
                    # "time_type_ids": "OPTIONAL_CONFIG",
                    # "daily_split_time": "OPTIONAL_CONFIG",

                },
            }
        },
        # Shift Labels for requested date range.
        {
            "name": "shift_label",
            "table_name": "shift_label",
            "endpoint": {
                "data_selector": "data",
                "path": "/shift-labels",
                "params": {
                    "range_start_date": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_date": "FILL_ME_IN", # TODO: fill in required query parameter

                },
            }
        },
        # Time Clock records for the requested date/time range. If a member is clocked in at the time of the request, the time clock record will be excluded.
        {
            "name": "time_clock_time",
            "table_name": "time_clock_time",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-clock-time",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter

                },
            }
        },
        # Approved and pending Time Off ranges for the requested date/time range. Note: By default, only approved Time Off ranges are included in the response. 
        {
            "name": "time_off",
            "table_name": "time_off",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-off",
                "params": {
                    "range_start_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    "range_stop_datetime": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "statuses": "approved",
                    # "daily_split_time": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns a list of unique Time Types which can be applied to scheduled time.
        {
            "name": "time_type",
            "table_name": "time_type",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-types",
            }
        },
        # Returns all active Time Off Types.
        {
            "name": "type",
            "table_name": "type",
            "endpoint": {
                "data_selector": "data",
                "path": "/time-off/types",
            }
        },
        # Returns all work groups. Work groups are used for putting members into groups if they follow the same schedule and work limit rules. Work groups can be customized per Aladtec system.
        {
            "name": "work_group",
            "table_name": "work_group",
            "endpoint": {
                "data_selector": "data",
                "path": "/work-groups",
            }
        },
        ]
    }

    return rest_api_source(source_config)

2. Configuring your source and destination credentials

info

dlt-init-openapi will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml.

• If you know your API needs authentication, but none was detected, you can learn more about adding authentication to the rest_api here.
• OAuth detection currently is not supported, but you can supply your own authentication mechanism as outlined here.

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

[runtime]
log_level="INFO"

[sources.aladtec]
# Base URL for the API
base_url = "https://..."  # Replace with API base URL

generated secrets.toml

[sources.aladtec]
# secrets for your aladtec source
token = "FILL ME OUT"  # TODO: fill in your credentials

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations

At this time, the dlt-init-openapi cli tool will always create pipelines that load to a local duckdb instance. Switching to a different destination is trivial, all you need to do is change the destination parameter in aladtec_pipeline.py to filesystem and supply the credentials as outlined in the destination doc linked below.

• Read more about setting up the rest_api source in our docs.
• Read more about setting up the Azure Cloud Storage destination in our docs.

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 aladtec_pipeline.py, as well as a folder aladtec 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 dlt

from aladtec import aladtec_source


if __name__ == "__main__":
    pipeline = dlt.pipeline(
        pipeline_name="aladtec_pipeline",
        destination='duckdb',
        dataset_name="aladtec_data",
        progress="log",
        export_schema_path="schemas/export"
    )
    source = aladtec_source()
    info = pipeline.run(source)
    print(info)

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

python aladtec_pipeline.py

4. Inspecting your load result

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

dlt pipeline aladtec_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 aladtec_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 GitHub Actions to automate your deployment process. Follow the guide here.
• Deploy with Airflow and Google Composer: Leverage Airflow and Google Composer for managing your dlt deployments. Learn more here.
• Deploy with Google Cloud Functions: Utilize Google Cloud Functions for serverless deployment of your dlt pipelines. Detailed instructions can be found here.
• Explore Other Deployment Options: Discover various other methods to deploy your dlt pipelines by visiting this comprehensive guide 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 smooth operation and quick issue resolution. How to Monitor your pipeline
• Set up alerts: Set up alerts to get notified about important events or issues in your dlt pipeline, allowing for proactive management. Set up alerts
• And set up tracing: Implement tracing to get detailed insights into the execution of your dlt pipeline, including timing and configuration information. And set up tracing

Available Sources and Resources

For this verified source the following sources and resources are available

Source Aladtec

Aladtec source for workforce management data including schedules, time clocks, payroll, and employee details.

Resource Name	Write Disposition	Description
time_clock_time	append	Tracks the time entries for employees clocking in and out.
time_off	append	Manages employee time-off requests and approvals.
availability	append	Records the availability of employees for scheduling purposes.
position_qualification	append	Details the qualifications required for specific positions.
schedule	append	Contains the scheduling information for all employees.
clocked_in_member	append	Lists employees currently clocked in.
note	append	Stores notes related to scheduling and workforce management.
open_time	append	Tracks open time slots that need to be filled.
accrual_bank	append	Manages the accruals of employee benefits like vacation or sick time.
payroll	append	Contains payroll-related data for employees.
work_group	append	Defines groups of employees working together.
extra_hour	append	Tracks extra hours worked by employees.
members_scheduled_now	append	Lists members who are currently scheduled to work.
event	append	Records events related to scheduling and workforce management.
time_type	append	Categorizes different types of time entries (e.g., regular, overtime).
kiosk	append	Manages data related to kiosk interactions for clocking in/out.
paycode	append	Defines pay codes used in payroll processing.
scheduled_time	append	Contains details of scheduled work times for employees.
shift_label	append	Labels used to categorize different shifts.
custom_attribute	append	Stores custom attributes for employees or scheduling.
balance	append	Tracks the balance of various accruals for employees.
type	append	Defines different types of entities within the system.
employee_type	append	Categorizes employees by type (e.g., full-time, part-time).
configuration	append	Stores system configuration settings.
member	append	Contains detailed information about each employee.

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