/prophecy_analytics_dais_2024

Primary LanguagePython

Table of Contents

Overview

Prophecy Analytics DAIS 2024 orchestrates a data analytics pipeline, leveraging Prophecy for streamlined workflow management. It structures data from LinkedIn, Google Ads, and Amazon sources into bronze, silver, and gold models for advanced analytics insights. By defining schemas, cleaning datasets, and generating reports, the project facilitates targeted marketing campaigns and empowers data-driven decision-making.

Features

Feature Description
⚙️ Architecture The project follows a modular architecture for efficient data processing. It leverages Prophecy for analytics pipelines, separating data sources and models into distinct modules.
🔩 Code Quality The codebase maintains good quality and adheres to a consistent style throughout. Clear code structure and thoughtful variable naming enhance readability.
📄 Documentation The project includes detailed YAML configuration files outlining project structures and data schemas. It lacks extensive documentation beyond configuration details.
🔌 Integrations Key integrations include SQL for database interactions and YAML for configuration files. The codebase relies on these dependencies for data processing.
🧩 Modularity The codebase demonstrates good modularity by organizing data sources, models, and transformations into separate modules. This promotes code reusability and easy maintenance.
🧪 Testing Testing frameworks and tools are not explicitly mentioned in the provided details. Additional information on testing practices would be beneficial for ensuring code reliability.
⚡️ Performance The project showcases efficient data processing with a focus on optimizing analytics workflows. The use of Prophecy and structured data models contributes to enhanced performance.
🛡️ Security Data protection measures and access controls are not explicitly discussed in the provided details. Further insights into security implementations would be valuable for ensuring data integrity.
📦 Dependencies Key external libraries and dependencies include SQL, YAML, and Prophecy for data analytics functionality. These dependencies play a crucial role in data processing tasks.

Repository Structure

└── prophecy_analytics_dais_2024/
    ├── README.md
    ├── dbt_project.yml
    ├── macros
    │   └── README.md
    ├── models
    │   ├── README.md
    │   ├── bronze
    │   ├── gold
    │   └── silver
    ├── pbt_project.yml
    ├── prophecy-sources
    │   ├── sources.yml
    │   └── unreferencedSources.yml
    ├── seeds
    │   ├── README.md
    │   ├── amazon
    │   ├── google_ads
    │   └── linkedin
    ├── snapshots
    │   └── README.md
    └── tests
        └── README.md

Modules

.
File Summary
pbt_project.yml This pbt_project.yml file in the prophecy_analytics_dais_2024 repository serves as a configuration file defining the project structure and details for a Prophecy-based data analytics pipeline. It outlines the project name, version, author, language, functions, and dependencies. The file plays a critical role in orchestrating the data processing tasks and defining the necessary components for the analytics workflow within the repositorys architecture.
dbt_project.yml Defines project structure, specifies file paths for cleaning, seeds, models, macros, and targets. Sets project details like name, version, and profile for the prophecy_analytics_dais_2024 repository in the dbt_project.yml.
prophecy-sources
File Summary
unreferencedSources.yml Identifies and lists unreferenced data sources within the prophecy-sources module. Empty unreferencedSources array in unreferencedSources.yml signals no unresolved dependencies for effective data modeling.
sources.yml Defines data schemas for LinkedIn and Google sources within the analytics database. Specifies tables, columns, and data types essential for analytics data processing. Enables seamless data extraction and analysis for targeted marketing campaigns.
models.bronze.amazon
File Summary
br_am_campaign_history.sql Transforms Amazon campaign history data into a formatted table for prophecy_analytics_dais_2024s bronze models. Selects latest records for each campaign, capturing details like budget, serving status, and more, aiding in data analysis.
schema.yml Defines Amazon Ads campaign data schema with detailed columns for bronze models in the repository. Describes campaign, profile, and history entities for analytics processing.
br_am_campaign_level_report.sql Extracts and structures Amazon campaign metrics data from the bronze model to enable analytics insights in the Amazon source within the repository's data transformation architecture.
br_am_profile.sql Refactors** dt_am_profile data, extracting fields and standardizing data types for Amazon's Bronze-level profiles in the analytics pipeline.
models.bronze.google_ads
File Summary
schema.yml Defines Google Ads schema models for daily performance and ad history. Describes account stats, device usage, costs, and ad details. Facilitates structured data organization within the analytics platform.
br_go_account_stats.sql Refactor model to cleanse Google Ads account stats data from source, organizing details like customer ID, date, ad network, device, clicks, spend, and impressions for better analytics insights in the bronze layer.
br_go_ad_history.sql Generates reformatted Google Ads campaign history data from the raw dataset for downstream analytics in the bronze model layer of the repository.
br_go_account_history.sql Extracts most recent Google Ads account history data, organizing and cleaning for analytics use. Identifies latest record for each account.
models.bronze.linkedin
File Summary
schema.yml Defines LinkedIn campaign schemas, including details like account, budget, status, and targeting. Captures campaign history and daily ad performance for insightful analytics. Enhances data modeling within the bronze layer of the repositorys analytics infrastructure.
br_li_account_history.sql Derive LinkedIn account history data from the linkedin source for analysis, reconstructing account details like ID, name, currency, and timestamps to facilitate further insights within the parent repository's analytics architecture.
br_li_campaign_history.sql Defines structured LinkedIn campaign history data transformation, filtering latest versions. Categorizes key details and flags latest versions for analysis. Enhances analysis with relevant campaign, budget, and timestamp information for the parent repositorys analytics architecture.
br_li_ad_analytics_by_campaign.sql Transforms LinkedIn ad analytics by campaign data for bronze modeling, extracting source, date, campaign ID, clicks, impressions, and cost.
models.silver.amazon
File Summary
sv_am_account_report.sql Analyzes Amazon advertising campaign data to generate a summary report. Joins campaign history, profile, and campaign level data. Aggregates total clicks, impressions, and cost by profile for reporting.
models.silver.google_ads
File Summary
sv_go_account_report.sql Derive total metrics for Google Ads accounts. Integrate history and stats to calculate and limit top 100 account metrics based on clicks, impressions, spend, and other attributes.
models.silver.linkedin
File Summary
sv_li_account_report.sql Generates comprehensive campaign performance insights by merging ad analytics, campaign history, and account data, providing a summarized view of clicks, impressions, and costs per account.
models.gold.all
File Summary
gl_all_account_report.sql Generates a unified account report by combining cleaned data from Amazon, Google Ads, and LinkedIn sources. Organizes account metrics for analytics purposes within the repositorys data modeling structure.

Getting Started

System Requirements:

  • SQL: version x.y.z

Installation

From source

  1. Clone the prophecy_analytics_dais_2024 repository:
$ git clone https://github.com/several27/prophecy_analytics_dais_2024
  1. Change to the project directory:
$ cd prophecy_analytics_dais_2024
  1. Install the dependencies:
$ > INSERT-INSTALL-COMMANDS

Usage

From source

Run prophecy_analytics_dais_2024 using the command below:

$ > INSERT-RUN-COMMANDS

Tests

Run the test suite using the command below:

$ > INSERT-TEST-COMMANDS

Project Roadmap

  • ► INSERT-TASK-1
  • ► INSERT-TASK-2
  • ► ...

Contributing

Contributions are welcome! Here are several ways you can contribute:

Contributing Guidelines
  1. Fork the Repository: Start by forking the project repository to your github account.
  2. Clone Locally: Clone the forked repository to your local machine using a git client. 
    git clone https://github.com/several27/prophecy_analytics_dais_2024
  3. Create a New Branch: Always work on a new branch, giving it a descriptive name. 
    git checkout -b new-feature-x
  4. Make Your Changes: Develop and test your changes locally.
  5. Commit Your Changes: Commit with a clear message describing your updates. 
    git commit -m 'Implemented new feature x.'
  6. Push to github: Push the changes to your forked repository. 
    git push origin new-feature-x
  7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.
  8. Review: Once your PR is reviewed and approved, it will be merged into the main branch. Congratulations on your contribution!
Contributor Graph

License

This project is protected under the SELECT-A-LICENSE License. For more details, refer to the LICENSE file.

Acknowledgments

  • List any resources, contributors, inspiration, etc. here.

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