Loading Data from Adobe Analytics to AlloyDB using dlt in Python
We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.
Join our Slack community or book a call with our support engineer Violetta.
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. AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. By leveraging the open-source Python library dlt, you can seamlessly load data from Adobe Analytics into AlloyDB. This integration allows you to harness the power of AlloyDB's enterprise-grade performance, reliability, and availability to maximize the value of your Adobe Analytics data. Further information about Adobe Analytics can be found here.
dlt Key Features
- Scalability via iterators, chunking, and parallelization:
dltoffers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Learn more - Implicit extraction DAGs:
dltincorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically, ensuring data consistency and integrity. Learn more - Automatic JSON normalization:
dltautomatically turns JSON returned by any source into a live dataset stored in the destination of your choice by extracting, normalizing, and loading the data. Learn more - Configurable normalization engine: The normalization engine in
dltrecursively unpacks nested structures into relational tables, making it ready to be loaded. It creates a schema that will automatically evolve with source data changes. Learn more - Idempotent and atomic loads:
dltuses idempotent, atomic loads ensuring that data safely ends up in your destination. It handles data size and interruptions gracefully, allowing safe retries without errors. Learn more
Getting started with your pipeline locally
dlt-init-openapi0. 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 postgres 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 AlloyDB 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 and deploy your
dltpipeline using GitHub Actions for CI/CD. Follow the guide here. - Deploy with Airflow and Google Composer: This guide walks you through deploying a
dltpipeline using Airflow and Google Composer. Check it out here. - Deploy with Google Cloud Functions: Discover how to deploy your
dltpipeline using Google Cloud Functions. Read the full instructions here. - Explore other deployment options: Find additional ways to deploy your
dltpipeline, including various cloud services and orchestration tools. More information can be found here.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to monitor your
dltpipeline effectively to ensure smooth operation and quick identification of issues. Read more here. - Set up alerts: Setting up alerts can help you stay informed about the status of your pipeline and quickly address any issues that arise. Find out how here.
- And set up tracing: Tracing allows you to track the flow of data through your pipeline, providing valuable insights into its performance and potential bottlenecks. Learn how to set up tracing 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 |
Additional pipeline guides
- Load data from Trello to Supabase in python with dlt
- Load data from MongoDB to Microsoft SQL Server in python with dlt
- Load data from Sentry to YugabyteDB in python with dlt
- Load data from Apple App-Store Connect to AlloyDB in python with dlt
- Load data from Attio to AWS S3 in python with dlt
- Load data from Zendesk to The Local Filesystem in python with dlt
- Load data from SAP HANA to Redshift in python with dlt
- Load data from Chess.com to Databricks in python with dlt
- Load data from SAP HANA to Google Cloud Storage in python with dlt
- Load data from GitHub to Google Cloud Storage in python with dlt