Load Adobe Analytics
Data to CockroachDB
Using dlt
in Python
We will be using the dlt PostgreSQL destination to connect to CockroachDB. You can get the connection string for your CockroachDB database as described in the CockroachDB Docs.
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. This documentation covers how to load data from Adobe Analytics
into CockroachDB
, a distributed, cloud-native SQL database that is Kubernetes compatible and free up to 5GB and 1vCPU. The process uses the open-source python library dlt
. For more information on Adobe Analytics
, visit Adobe Analytics.
dlt
Key Features
- Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage. - Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more: Adjust a schema docs. - Schema Evolution:
dlt
enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations,dlt
notifies stakeholders, allowing them to take necessary actions, such as reviewing and validating the changes, updating downstream processes, or performing impact analysis. - Scaling and Fine-tuning:
dlt
offers several mechanisms and configuration options to scale up and fine-tune pipelines: running extraction, normalization, and load in parallel, writing sources and resources that are run in parallel via thread pools and async execution, and fine-tuning the memory buffers, intermediary file sizes, and compression options. Read more about performance. - Community and Support:
dlt
is a constantly growing library that supports many features and use cases needed by the community. Join our Slack to find recent releases or discuss what you can build withdlt
.
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 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 CockroachDB
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: Automate your deployment using Github Actions.
- Deploy with Airflow: Learn how to deploy a pipeline with Airflow and Google Composer.
- Deploy with Google Cloud Functions: Follow the guide to deploy using Google Cloud Functions.
- Explore other deployment options: Check out additional methods for deploying pipelines 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 quickly identify any issues. How to Monitor your pipeline - Set up alerts: Set up alerts to stay informed about the status of your
dlt
pipeline and get notified in case of any failures or important events. Set up alerts - Set up tracing: Implement tracing to keep track of your pipeline's execution and gather detailed information about each step for debugging and performance optimization. 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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