Loading Data from Adobe Analytics
to BigQuery
with dlt
in Python
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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. BigQuery
is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Using the open-source python library called dlt
, you can efficiently load data from Adobe Analytics
to BigQuery
. This integration allows you to leverage BigQuery
's powerful analytics capabilities to gain deeper insights from your Adobe Analytics
data. For more information about Adobe Analytics
, visit Adobe Analytics.
dlt
Key Features
- Automated maintenance:
dlt
offers schema inference and evolution, along with alerts, making maintenance simple with short declarative code. Learn more - Scalability: Leverages iterators, chunking, and parallelization for efficient processing of large datasets. Read about scalability
- Implicit extraction DAGs: Automatically handles dependencies between data sources and transformations. Understand extraction DAGs
- User-friendly interface: Designed to be accessible for beginners while empowering senior professionals. Getting started guide
- Community support: Join the
dlt
community on Slack or check out the code on GitHub.
Getting started with your pipeline locally
dlt-init-openapi
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
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
.
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
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 bigquery and supply the credentials as outlined in the destination doc linked below.
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 BigQuery
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 deploy your
dlt
pipelines using GitHub Actions for CI/CD. Github Actions - Deploy with Airflow and Google Composer: Follow this guide to deploy your
dlt
pipeline using Airflow and Google Composer. Airflow - Deploy with Google Cloud Functions: This tutorial explains how to deploy your
dlt
pipeline using Google Cloud Functions. Google cloud functions - Explore Other Deployment Options: Discover various other methods to deploy your
dlt
pipelines. and others...
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline to ensure smooth and efficient data processing. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified about any issues or anomalies in your
dlt
pipeline. Set up alerts - Set up tracing: Implement tracing to track the performance and execution of your
dlt
pipeline. And set up tracing
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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