EazyDI Marketing Data Bridge for Databricks

**EazyDI Marketing Data Bridge for Databricks**
*Unify Your Marketing Data in Minutes — Built for the Databricks Lakehouse*

The EazyDI Marketing Data Bridge for Databricks is a free, ready-to-deploy solution designed to streamline digital marketing data ingestion into the Databricks Lakehouse. It enables marketing and data teams to quickly centralize advertising performance data without building and maintaining complex custom pipelines.

**🚀 What’s Included (Free Connectors)**
*The free version supports ingestion from:*

- Snapchat Ads
- LinkedIn Ads
- Meta Ads (Instagram)
- TikTok Ads
- Pinterest Ads

These connectors automatically extract campaign, ad group, creative, spend, impressions, clicks, and performance metrics and load them into your Databricks environment in a structured, analytics-ready format.

**🔥 Why Use the Marketing Data Bridge?**

**Unified Marketing View**
Consolidate paid media data across platforms into a single Lakehouse layer for performance analysis, attribution modeling, and executive reporting.

**Lakehouse-Native Architecture**
Built specifically for Databricks, leveraging scalable ingestion, transformation pushdown, and structured medallion architecture compatibility (Bronze/Silver/Gold).

**Faster Time to Insight**
Eliminate manual exports, CSV uploads, and fragile scripts. Automate ingestion and standardization across channels.

**Scalable & Extendable**
Designed to grow with your marketing stack.

**➕ Additional Channels Available**
Beyond the free connectors, EazyDI supports integration with additional marketing and digital channels upon request, including:

- Google Ads
- Google Analytics
- YouTube Ads
- Microsoft Ads
- Amazon Advertising
- Shopify
- Salesforce
- HubSpot
- And many more ad, CRM, and ecommerce platforms

Our team can quickly enable new connectors based on your environment and business needs.

**⚙️ Key Capabilities**

- Automated data extraction via secure API integrations
- Structured, analytics-ready output for Databricks
- Centralized campaign performance modeling
- Scalable orchestration and monitoring
- Extensible architecture for future marketing sources

**🎯 Ideal For**

1. Marketing analytics teams
2. Data engineering teams supporting marketing
3. Growth and performance marketing teams
4. Organizations centralizing marketing data into Databricks

**Get Started Today**
Download the EazyDI Marketing Data Bridge for Databricks for free and start unifying your paid media data into your Lakehouse environment.

For additional connectors, enterprise support, or custom integrations, contact the EazyDI team to extend the solution to your full marketing ecosystem.

📦 **Example 1 Data Source: EazyDI – TikTok Databricks Pipeline**

📖 **Description**

This product deploys and runs a data pipeline for TikTok Ads on Databricks using:

1. Python Wheel (eazydi_databricks-1.0.0-py3-none-any.whl)
2. Spark SQL (DDL included in the wheel)
3. EazyDI external APIs
4. Databricks Secrets for secure configuration

**The pipeline:**
- Creates the necessary tables (DDL)
- Authenticates against the EazyDI API
- Queryes archived jobs
- Restarts a batch
- Executes Spark transformations and generates final tables

This notebook does not run the pipeline directly. Execution is performed using a Databricks Job configured in the Marketplace Listing, using the wheel.

🧱 **Architecture**
**Marketplace Listing**

- Notebook (file)
- Databricks
- Python Wheel Task
- Library: eazydi_databricks-1.0.0.whl
- Entry point: main()

🔐 **Secrets Requirements (REQUIRED)**

Credential Configuration (Marketplace Version)
This version requires a secrets.json file located in the same directory as the notebook.

And define the following keys:

- username
- password
- tenant_id
- batch_id

This file contains the credentials necessary to run the pipeline.

⚙️ **How to run the pipeline**

1. Install the product from the Databricks Marketplace
2. Configure the secrets mentioned above
3. Run the job associated with the publication

**For more information, please refer to the official EazyDI documentation attached to this publication.**