Loading Data from `Adobe Analytics` to `Neon Serverless Postgres` with `dlt` in Python

tip

We will be using the dlt PostgreSQL destination to connect to Neon Serverless Postgres. You can get the connection string for your Neon Serverless Postgres database as described in the Neon Serverless Postgres 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 Neon Serverless Postgres using the dlt open source python library. The process ensures reliable and scalable data integration, allowing you to leverage the strengths of Neon Serverless Postgres for your analytics needs. For more information on Adobe Analytics, visit here.

`dlt` Key Features

Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data vaulting. Read more
Schema Enforcement and Curation: Enforce and curate schemas to ensure data consistency and quality by defining the structure of normalized data. Read more
Scalability via Iterators, Chunking, and Parallelization: Efficiently process large datasets by breaking them down into manageable chunks and leveraging parallel processing capabilities. Read more
Implicit Extraction DAGs: Automatically handle dependencies between data sources and their transformations via implicit extraction DAGs. Read more
Schema Evolution Alerts: Proactively govern data by alerting users to schema changes, allowing them to review and validate modifications. Read 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 Neon Serverless Postgres 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 Neon Serverless Postgres 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 pipeline using GitHub Actions for CI/CD automation. Read more
Deploy with Airflow: Discover how to deploy your pipeline with Airflow and Google Composer for managed workflow orchestration. Read more
Deploy with Google Cloud Functions: Find out how to deploy your dlt pipeline using Google Cloud Functions for serverless execution. Read more
Explore other deployment options: Check out additional guides for deploying dlt pipelines using various methods and platforms. Read more

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 and reliable data processing. How to Monitor your pipeline
Set up alerts: Configure alerts to stay informed about the status and any issues in your dlt pipeline, allowing for proactive management and quick resolution of problems. Set up alerts
And set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipeline, helping you diagnose and troubleshoot issues efficiently. 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

Loading Data from `Adobe Analytics` to `Neon Serverless Postgres` with `dlt` in Python

`dlt` Key Features

Getting started with your pipeline locally

0. Prerequisites

1. Install dlt and dlt-init-openapi

1.1. Tweak adobe_analytics/init.py

2. Configuring your source and destination credentials

generated config.toml

generated secrets.toml

2.1. Adjust the generated code to your usecase

3. Running your pipeline for the first time

4. Inspecting your load result

5. Next steps to get your pipeline running in production

The Deploy section will show you how to deploy your pipeline to

The running in production section will teach you about:

Available Sources and Resources

Source `Adobe Analytics`

Additional pipeline guides

DHelp

Ask a question

Loading Data from Adobe Analytics to Neon Serverless Postgres with dlt in Python

dlt Key Features​

Getting started with your pipeline locally​

0. Prerequisites​

1. Install dlt and dlt-init-openapi​

1.1. Tweak adobe_analytics/__init__.py​

2. Configuring your source and destination credentials​

generated config.toml​

generated secrets.toml​

2.1. Adjust the generated code to your usecase​

3. Running your pipeline for the first time​

4. Inspecting your load result​

5. Next steps to get your pipeline running in production​

The Deploy section will show you how to deploy your pipeline to​

The running in production section will teach you about:​

Available Sources and Resources​

Source Adobe Analytics​

Additional pipeline guides​

DHelp

Ask a question

Loading Data from `Adobe Analytics` to `Neon Serverless Postgres` with `dlt` in Python

`dlt` Key Features

Getting started with your pipeline locally

0. Prerequisites

1. Install dlt and dlt-init-openapi

1.1. Tweak adobe_analytics/init.py

2. Configuring your source and destination credentials

generated config.toml

generated secrets.toml

2.1. Adjust the generated code to your usecase

3. Running your pipeline for the first time

4. Inspecting your load result

5. Next steps to get your pipeline running in production

The Deploy section will show you how to deploy your pipeline to

The running in production section will teach you about:

Available Sources and Resources

Source `Adobe Analytics`

Additional pipeline guides