Loading Data from Adobe Analytics to Timescale with dlt in Python

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We will be using the dlt PostgreSQL destination to connect to Timescale. You can get the connection string for your timescale database as described in the Timescale Docs.

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Adobe Analytics is a tool for tracking and analyzing website and app traffic. It provides deep insights into user behavior, enabling businesses to optimize their digital marketing strategies, improve user experiences, and drive better decision-making through data-driven insights. This documentation explains how to load data from Adobe Analytics to Timescale using the open-source Python library called dlt. Timescale is engineered to handle demanding workloads, like time series, vector, events, and analytics data. Built on PostgreSQL, Timescale offers expert support at no extra charge. For more information on Adobe Analytics, visit here.

dlt Key Features

• Google Analytics Integration: Integrate with Google Analytics to track and analyze user engagement on your website or application. Learn more
• Matomo Integration: Utilize Matomo for detailed insights into website and application performance with features like visitor maps and real-time tracking. Learn more
• Asana Integration: Manage and organize your team's projects and tasks effectively with Asana. Learn more
• Facebook Ads Integration: Create and manage targeted ads on Facebook and its affiliated apps like Instagram and Messenger. Learn more
• Scalable Data Extraction: Extract large datasets efficiently using 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 adobe_analytics --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/adobe_analytics.yaml --global-limit 2
cd adobe_analytics_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:

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

1.1. Tweak adobe_analytics/__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 adobe_analytics source will look like this:

Click to view full file (325 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="adobe_analytics_source", max_table_nesting=2)
def adobe_analytics_source(
        base_url: str = dlt.config.value,
) -> List[DltResource]:

    # source configuration
    source_config: RESTAPIConfig = {
        "client": {
            "base_url": base_url,
            "paginator": {
                "type":
                    "page_number",
                "page_param":
                    "page",
                "total_path":
                    "",
                "maximum_page":
                    20,
            },
        },
        "resources": 
        [
        # A calculated metric response will always include these default items: *id, name, description, rsid, owner, polarity, precision, type*  Other attributes can be optionally requested through the 'expansion' field:  * *modified*: Date that the metric was last modified (ISO 8601) * *definition*: Calculated metric definition as JSON object * *compatibility*: Products that the metric is compatible with * *reportSuiteName*: Also return the friendly Report Suite name for the RSID * *tags*: Gives all existing tags associated with the calculated metric  For more information about calculated metrics go [here](https://github.com/AdobeDocs/analytics-2.0-apis/blob/master/calculatedmetrics.md)  
        {
            "name": "find_calculated_metrics",
            "table_name": "analytics_calculated_metric",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/calculatedmetrics",
                "params": {
                    # the parameters below can optionally be configured
                    # "rsids": "OPTIONAL_CONFIG",
                    # "ownerId": "OPTIONAL_CONFIG",
                    # "calculatedMetricFilter": "OPTIONAL_CONFIG",
                    # "locale": "en_US",
                    # "name": "OPTIONAL_CONFIG",
                    # "tagNames": "OPTIONAL_CONFIG",
                    # "limit": "10",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        # A calculated metric response will always include these default items: *id, name, description, rsid, owner, polarity, precision, type*  Other attributes can be optionally requested through the 'expansion' field:  * *modified*: Date that the metric was last modified (ISO 8601) * *definition*: Calculated metric definition as JSON object * *compatibility*: Products that the metric is compatible with * *reportSuiteName*: Also return the friendly Report Suite name for the RSID * *tags*: Gives all existing tags associated with the calculated metric  For more information about calculated metrics go [here](https://github.com/AdobeDocs/analytics-2.0-apis/blob/master/calculatedmetrics.md)  
        {
            "name": "find_one_calculated_metric",
            "table_name": "analytics_calculated_metric",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/calculatedmetrics/{id}",
                "params": {
                    "id": {
                        "type": "resolve",
                        "resource": "find_calculated_metrics",
                        "field": "id",
                    },
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_date_range",
            "table_name": "analytics_date_range",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/dateranges/{dateRangeId}",
                "params": {
                    "dateRangeId": {
                        "type": "resolve",
                        "resource": "get_date_ranges",
                        "field": "id",
                    },
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "dimensions_get_dimensions",
            "table_name": "analytics_dimension",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/dimensions",
                "params": {
                    "rsid": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "segmentable": "OPTIONAL_CONFIG",
                    # "reportable": "OPTIONAL_CONFIG",
                    # "classifiable": "false",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "dimensions_get_dimension",
            "table_name": "analytics_dimension",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/dimensions/{dimensionId}",
                "params": {
                    "dimensionId": {
                        "type": "resolve",
                        "resource": "dimensions_get_dimensions",
                        "field": "id",
                    },
                    "rsid": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_metric",
            "table_name": "analytics_metric",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/metrics/{id}",
                "params": {
                    "id": "FILL_ME_IN", # TODO: fill in required path parameter
                    "rsid": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "segments_get_segment",
            "table_name": "analytics_segment_response_item",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/segments/{id}",
                "params": {
                    "id": {
                        "type": "resolve",
                        "resource": "segments_get_segments",
                        "field": "id",
                    },
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        # Retrieves a list of all users for the company designated by the auth token.
        {
            "name": "find_all_users",
            "table_name": "analytics_user",
            "endpoint": {
                "data_selector": "$",
                "path": "/users",
                "params": {
                    # the parameters below can optionally be configured
                    # "limit": "10",

                },
            }
        },
        {
            "name": "get_current_user",
            "table_name": "analytics_user",
            "endpoint": {
                "data_selector": "$",
                "path": "/users/me",
            }
        },
        # This returns the metrics list primarily for the Analytics product. The platform identity API Returns a list of all possible metrics for the supported systems.
        {
            "name": "get_metrics",
            "table_name": "metric",
            "endpoint": {
                "data_selector": "support",
                "path": "/metrics",
                "params": {
                    "rsid": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "segmentable": "false",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns all report suite types in a single collection.
        {
            "name": "get_collections",
            "table_name": "suite_collection_item",
            "endpoint": {
                "data_selector": "$",
                "path": "/collections/suites",
                "params": {
                    # the parameters below can optionally be configured
                    # "rsids": "OPTIONAL_CONFIG",
                    # "rsidContains": "OPTIONAL_CONFIG",
                    # "limit": "10",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        # Returns all report suite types in a single collection.
        {
            "name": "find_one",
            "table_name": "suite_collection_item",
            "primary_key": "rsid",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/collections/suites/{rsid}",
                "params": {
                    "rsid": "FILL_ME_IN", # TODO: fill in required path parameter
                    # the parameters below can optionally be configured
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        # This API allows users to store commonly used date ranges so that they can be reused throughout the product.
        {
            "name": "get_date_ranges",
            "table_name": "tag",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "tags",
                "path": "/dateranges",
                "params": {
                    # the parameters below can optionally be configured
                    # "locale": "en_US",
                    # "filterByIds": "OPTIONAL_CONFIG",
                    # "limit": "10",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "segments_get_segments",
            "table_name": "tag",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "tags",
                "path": "/segments",
                "params": {
                    # the parameters below can optionally be configured
                    # "includeType": "OPTIONAL_CONFIG",
                    # "rsids": "OPTIONAL_CONFIG",
                    # "segmentFilter": "OPTIONAL_CONFIG",
                    # "locale": "en_US",
                    # "name": "OPTIONAL_CONFIG",
                    # "tagNames": "OPTIONAL_CONFIG",
                    # "limit": "10",
                    # "expansion": "OPTIONAL_CONFIG",

                },
            }
        },
        # This API returns the usage and access logs for a given date range within a 3 month period. This API authenticates with an IMS user token.
        {
            "name": "get_usage_logs",
            "table_name": "usage_logs",
            "endpoint": {
                "data_selector": "$",
                "path": "/auditlogs/usage",
                "params": {
                    "startDate": "FILL_ME_IN", # TODO: fill in required query parameter
                    "endDate": "FILL_ME_IN", # TODO: fill in required query parameter
                    # the parameters below can optionally be configured
                    # "login": "OPTIONAL_CONFIG",
                    # "ip": "OPTIONAL_CONFIG",
                    # "rsid": "OPTIONAL_CONFIG",
                    # "eventType": "OPTIONAL_CONFIG",
                    # "event": "OPTIONAL_CONFIG",
                    # "limit": "10",

                },
                "paginator": {
                    "type":
                        "page_number",
                    "page_param":
                        "page",
                    "total_path":
                        "[*].totalPages",
                },
            }
        },
        ]
    }

    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.adobe_analytics]
# Base URL for the API
base_url = "https://analytics.adobe.io/api/{companyId}/"

generated secrets.toml

[sources.adobe_analytics]
# secrets for your adobe_analytics source
# example_api_key = "example value"

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 adobe_analytics_pipeline.py to postgres 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 Timescale 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 adobe_analytics_pipeline.py, as well as a folder adobe_analytics 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 adobe_analytics import adobe_analytics_source


if __name__ == "__main__":
    pipeline = dlt.pipeline(
        pipeline_name="adobe_analytics_pipeline",
        destination='duckdb',
        dataset_name="adobe_analytics_data",
        progress="log",
        export_schema_path="schemas/export"
    )
    source = adobe_analytics_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 adobe_analytics_pipeline.py

4. Inspecting your load result

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

dlt pipeline adobe_analytics_pipeline info

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

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline adobe_analytics_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 set up continuous integration and deployment for your dlt pipelines using GitHub Actions.
• Deploy with Airflow and Google Composer: Follow this guide to deploy your dlt pipeline using Airflow and Google Composer.
• Deploy with Google Cloud Functions: Explore how to deploy your dlt pipeline using Google Cloud Functions.
• Explore More Deployment Options: Check out additional methods for deploying your dlt pipelines in the deployment walkthroughs.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production by following the guidelines provided here.
• Set up alerts: Ensure you are promptly informed of any issues by setting up alerts for your dlt pipeline. Detailed instructions can be found here.
• Set up tracing: Implement tracing to gain insights into the performance and execution of your dlt pipeline. Find out how to set it up here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source Adobe Analytics

Adobe Analytics: Collects and analyzes user engagement, metrics, dimensions, and usage logs for informed decisions.

Resource Name	Write Disposition	Description
analytics_calculated_metric	append	Custom metrics derived from existing data to provide specific insights
analytics_date_range	append	Date ranges used for segmenting and analyzing data
suite_collection_item	append	Items collected from various suites for comprehensive analytics
usage_logs	append	Logs detailing the usage patterns and behaviors of users
analytics_dimension	append	Dimensions used to categorize and break down data for analysis
analytics_user	append	Information about users interacting with the website or app
analytics_metric	append	Standard metrics used to measure and analyze performance
tag	append	Tags used for organizing and categorizing data
metric	append	Basic performance metrics
analytics_segment_response_item	append	Segmented data responses for detailed analysis

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