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https://github.com/bayoadejare/pipeline-ecommerce
E-commerce Data Pipeline
https://github.com/bayoadejare/pipeline-ecommerce
adf azure azure-sql clustering-analysis data-engineer data-engineering-pipeline data-warehouse database databricks machine-learning pandas power-bi powerbi python3 synapse
Last synced: 24 days ago
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E-commerce Data Pipeline
- Host: GitHub
- URL: https://github.com/bayoadejare/pipeline-ecommerce
- Owner: BayoAdejare
- License: mit
- Created: 2024-09-24T18:48:00.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-22T23:00:40.000Z (2 months ago)
- Last Synced: 2024-12-09T05:49:09.046Z (25 days ago)
- Topics: adf, azure, azure-sql, clustering-analysis, data-engineer, data-engineering-pipeline, data-warehouse, database, databricks, machine-learning, pandas, power-bi, powerbi, python3, synapse
- Language: Python
- Homepage:
- Size: 22.5 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# E-Commerce Data Pipeline
Welcome to the E-Commerce Data Pipeline project! This advanced system integrates and processes data from multiple sales channels and third-party data sources to provide comprehensive insights and recommendations for an e-commerce business.
## Table of Contents
- [Project Overview](#project-overview)
- [Data Sources](#data-sources)
- [Azure Architecture](#azure-architecture)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Setup and Configuration](#setup-and-configuration)
- [Usage](#usage)
- [Example: Sales Data Integration and Analysis](#example-sales-data-integration-and-analysis)
- [Example: Customer Behavior Segmentation](#example-customer-behavior-segmentation)
- [Example: Inventory and Pricing Optimization](#example-inventory-and-pricing-optimization)
- [CI/CD with Azure DevOps](#cicd-with-azure-devops)
- [License](#license)
## Project Overview
Our E-Commerce Data Pipeline is designed to handle data from various sales channels and external data sources to provide a unified view of the business. It includes data ingestion, processing, analysis, and reporting components to enhance decision-making and optimize operations.
Key features:
- Integration with multiple e-commerce platforms (Shopify, WooCommerce, Magento, etc.)
- Integration with high-quality third-party data sources for enhanced insights
- Real-time data ingestion and processing
- Scalable data processing using Azure Data Factory and Azure Databricks
- Comprehensive sales data analysis and reporting
- Customer behavior segmentation and personalized recommendations
- Inventory management and pricing optimization
- Integration with Azure Cognitive Services for natural language processing of customer feedback
## Data Sources
### Internal Data Sources
1. **E-commerce Platforms**
- Shopify API
- WooCommerce REST API
- Magento 2 REST API
2. **Customer Service**
- Zendesk API for customer support tickets
- Intercom API for customer communications
### External Data Sources
1. **Market Intelligence**
- **Nielsen Retail Measurement Services**: Access point-of-sale data and market share insights
- API Documentation: [Nielsen API Portal](https://engineeringportal.nielsen.com)
- Use for: Competitive analysis and market positioning
2. **Economic Indicators**
- **US Census Bureau Retail Trade API**: Monthly retail sales data
- API Endpoint: https://api.census.gov/data/timeseries/eits/marts
- Use for: Market trend analysis and forecasting
3. **Weather Data**
- **OpenWeatherMap API**: Historical and forecast weather data
- API Documentation: [OpenWeatherMap API](https://openweathermap.org/api)
- Use for: Analyzing weather impact on sales patterns
4. **Social Media Sentiment**
- **X API v2**: Real-time social media sentiment analysis
- API Documentation: [X API](https://developer.x.com/en/docs/x-api)
- Use for: Brand monitoring and trend detection
5. **Product Information**
- **Google Product Catalog API**: Enriched product data
- API Documentation: [Google Shopping Content API](https://developers.google.com/shopping-content/reference/rest)
- Use for: Product catalog enrichment and competitive pricing
### Data Integration Examples
```python
# Example: Integrating Nielsen market data
from nielsen_api import NielsenClient
from azure.storage.blob import BlobServiceClient
def ingest_nielsen_data():
nielsen_client = NielsenClient(api_key=os.environ["NIELSEN_API_KEY"])
# Fetch market share data
market_data = nielsen_client.get_market_share(
category="electronics",
time_period="last_month"
)
# Store in Azure Blob Storage
blob_service_client = BlobServiceClient.from_connection_string(os.environ["AZURE_STORAGE_CONNECTION_STRING"])
container_client = blob_service_client.get_container_client("market-data")
blob_client = container_client.get_blob_client(f"nielsen_data_{datetime.now().strftime('%Y%m%d')}.json")
blob_client.upload_blob(json.dumps(market_data))
# Example: Integrating weather data with sales analysis
def analyze_weather_impact():
weather_client = OpenWeatherMapClient(api_key=os.environ["OPENWEATHER_API_KEY"])
# Fetch historical weather data
weather_data = weather_client.get_historical_weather(
city="New York",
start_date="2024-01-01",
end_date="2024-03-31"
)
# Join weather data with sales data in Spark
weather_df = spark.createDataFrame(weather_data)
sales_df = spark.read.parquet("abfss://[email protected]/sales_data/")
weather_sales_analysis = sales_df.join(weather_df, "date") \
.groupBy("weather_condition") \
.agg(avg("daily_sales").alias("avg_sales_by_weather"))
return weather_sales_analysis
```
## Azure Architecture
Our pipeline utilizes the following Azure services:
- Azure Data Factory: Orchestrates and automates the data movement and transformation
- Azure Blob Storage: Stores raw and processed data
- Azure Databricks: Performs complex data processing and runs machine learning models
- Azure SQL Database: Stores structured data and analysis results
- Azure Analysis Services: Creates semantic models for reporting
- Power BI: Provides interactive dashboards and reports
- Azure Key Vault: Securely stores secrets and access keys
- Azure Monitor: Monitors pipeline performance and health
## Project Structure
```
ecommerce-data-pipeline/
│
├── adf/
│ ├── pipeline/
│ │ ├── ingest_shopify_data.json
│ │ ├── process_sales_data.json
│ │ ├── analyze_customer_behavior.json
│ │ └── optimize_inventory_pricing.json
│ ├── dataset/
│ │ ├── shopify_orders.json
│ │ ├── shopify_products.json
│ │ └── processed_sales_data.json
│ └── linkedService/
│ ├── AzureBlobStorage.json
│ ├── AzureDataLakeStorage.json
│ └── AzureDatabricks.json
│
├── databricks/
│ ├── notebooks/
│ │ ├── sales_data_analysis.py
│ │ ├── customer_segmentation.py
│ │ └── inventory_pricing_optimization.py
│ └── libraries/
│ └── ecommerce_utils.py
│
├── sql/
│ ├── schema/
│ │ ├── sales_metrics.sql
│ │ ├── customer_segments.sql
│ │ └── inventory_optimization.sql
│ └── stored_procedures/
│ ├── calculate_sales_kpis.sql
│ ├── cluster_customers.sql
│ └── optimize_inventory_pricing.sql
│
├── power_bi/
│ ├── SalesDashboard.pbix
│ ├── CustomerInsights.pbix
│ └── InventoryManagement.pbix
│
├── tests/
│ ├── unit/
│ └── integration/
│
├── scripts/
│ ├── setup_azure_resources.sh
│ └── deploy_adf_pipelines.sh
│
├── .azure-pipelines/
│ ├── ci-pipeline.yml
│ └── cd-pipeline.yml
│
├── requirements.txt
├── .gitignore
└── README.md
```
## Setup and Configuration
1. Clone the repository:
```
git clone https://github.com/your-org/ecommerce-data-pipeline.git
cd ecommerce-data-pipeline
```
2. Set up Azure resources:
```
./scripts/setup_azure_resources.sh
```
3. Configure Azure Data Factory pipelines:
```
./scripts/deploy_adf_pipelines.sh
```
4. Set up Azure Databricks workspace and upload notebooks from the `databricks/notebooks/` directory.
5. Create Azure SQL Database schema and stored procedures using scripts in the `sql/` directory.
6. Import Power BI reports from the `power_bi/` directory and configure data sources.
7. Configure Shopify (or other e-commerce platform) integration settings in the `adf/linkedService/` directory.
### Local install
Make sure you have the virtual environment configured:
```
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
For requirements, this can be installed from the project directory via pip's setup command:
`pip install -r requirements.txt # =< python3.12 `
## Usage
1. Monitor and manage Azure Data Factory pipelines through the Azure portal or using Azure Data Factory SDK.
2. Schedule pipeline runs or trigger them manually based on your requirements.
3. Access Databricks notebooks for custom analysis and model training.
4. View reports and dashboards in Power BI for insights into sales, customer behavior, and inventory/pricing optimization.
5. Adjust integration settings and data sources as needed to accommodate changes in the e-commerce platform landscape.
## Example: Sales Data Integration and Analysis
In this example, we'll use Azure Data Factory and Databricks to integrate sales data from Shopify and perform comprehensive analysis.
```python
# In Azure Databricks notebook
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, count, to_date, date_format
# Initialize Spark session
spark = SparkSession.builder.appName("SalesDataAnalysis").getOrCreate()
# Read sales data from Azure Data Lake
sales_data = spark.read.parquet("abfss://[email protected]/sales_data/")
# Convert order date to date column
sales_data = sales_data.withColumn("order_date", to_date(col("order_timestamp")))
# Calculate sales metrics by date and product
daily_sales = sales_data.groupBy("order_date", "product_id") \
.agg(sum("quantity").alias("total_units_sold"),
sum("total_amount").alias("total_revenue"),
avg("order_amount").alias("avg_order_value"))
# Calculate overall sales KPIs
total_revenue = sales_data.agg(sum("total_amount").alias("total_revenue")).collect()[0]["total_revenue"]
total_units_sold = sales_data.agg(sum("quantity").alias("total_units_sold")).collect()[0]["total_units_sold"]
avg_order_value = sales_data.agg(avg("order_amount").alias("avg_order_value")).collect()[0]["avg_order_value"]
# Write sales metrics to Azure SQL Database
daily_sales.write \
.format("jdbc") \
.option("url", "jdbc:sqlserver://yourserver.database.windows.net:1433;database=yourdatabase") \
.option("dbtable", "daily_sales_metrics") \
.option("user", "yourusername") \
.option("password", "yourpassword") \
.mode("overwrite") \
.save()
# Write overall sales KPIs to Azure SQL Database
spark.createDataFrame([(total_revenue, total_units_sold, avg_order_value)], ["total_revenue", "total_units_sold", "avg_order_value"]) \
.write \
.format("jdbc") \
.option("url", "jdbc:sqlserver://yourserver.database.windows.net:1433;database=yourdatabase") \
.option("dbtable", "sales_kpis") \
.option("user", "yourusername") \
.option("password", "yourpassword") \
.mode("overwrite") \
.save()
```
In this example, we:
1. Read sales data from Azure Data Lake, which includes order details from Shopify and other sales channels.
2. Convert the order timestamp to a date column for easier analysis.
3. Calculate daily sales metrics, such as total units sold, total revenue, and average order value, grouped by order date and product.
4. Calculate overall sales KPIs, including total revenue, total units sold, and average order value.
5. Write the sales metrics and KPIs to separate tables in the Azure SQL Database for reporting and further analysis.
These sales data insights can be used to identify top-selling products, monitor daily and weekly sales performance, and understand customer purchasing behavior.
## Example: Customer Behavior Segmentation
In this example, we'll use Azure Databricks to analyze customer behavior and segment customers based on their purchasing patterns.
```python
# In Azure Databricks notebook
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, count, datediff, to_date
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeansModel
# Initialize Spark session
spark = SparkSession.builder.appName("CustomerSegmentation").getOrCreate()
# Read customer and sales data from Azure Data Lake
customer_data = spark.read.parquet("abfss://[email protected]/customer_data/")
sales_data = spark.read.parquet("abfss://[email protected]/sales_data/")
# Join customer and sales data
customer_behavior = customer_data.join(sales_data, "customer_id")
# Calculate customer behavior features
customer_features = customer_behavior.groupBy("customer_id") \
.agg(count("order_id").alias("total_orders"),
avg("order_amount").alias("avg_order_value"),
datediff(max("order_timestamp"), min("order_timestamp")).alias("lifetime_days"))
# Prepare feature vector for clustering
featurizer = VectorAssembler(inputCols=["total_orders", "avg_order_value", "lifetime_days"], outputCol="features")
customer_data_for_clustering = featurizer.transform(customer_features)
# Train customer segmentation model
customer_segmentation_model = KMeansModel.train(customer_data_for_clustering, 5)
# Assign customers to segments
customer_segments = customer_segmentation_model.transform(customer_data_for_clustering)
# Write customer segments to Azure SQL Database
customer_segments.write \
.format("jdbc") \
.option("url", "jdbc:sqlserver://yourserver.database.windows.net:1433;database=yourdatabase") \
.option("dbtable", "customer_segments") \
.option("user", "yourusername") \
.option("password", "yourpassword") \
.mode("overwrite") \
.save()
```
In this example, we:
1. Read customer data and sales data from Azure Data Lake.
2. Join the customer and sales data to create a comprehensive customer behavior dataset.
3. Calculate customer behavior features, such as total orders, average order value, and lifetime days.
4. Prepare a feature vector using the VectorAssembler to feed into the clustering model.
5. Train a KMeans clustering model to segment customers into 5 distinct groups.
6. Assign customers to their respective segments and write the results to an Azure SQL Database table.
These customer behavior segments can be used to create personalized marketing campaigns, offer targeted promotions, and improve overall customer experience.
## Example: Inventory and Pricing Optimization
In this example, we'll use Azure Databricks to analyze sales data and optimize inventory and pricing strategies.
```python
# In Azure Databricks notebook
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, min, max, lag, lead
from pyspark.ml.regression import LinearRegression
# Initialize Spark session
spark = SparkSession.builder.appName("InventoryPricingOptimization").getOrCreate()
# Read sales and product data from Azure Data Lake
sales_data = spark.read.parquet("abfss://[email protected]/sales_data/")
product_data = spark.read.parquet("abfss://[email protected]/product_data/")
# Join sales and product data
sales_product_data = sales_data.join(product_data, "product_id")
# Calculate sales velocity and profit margin
sales_product_metrics = sales_product_data.groupBy("product_id") \
.agg(sum("quantity").alias("total_units_sold"),
avg("total_amount").alias("avg_revenue_per_unit"),
min("price").alias("min_price"),
max("price").alias("max_price"))
# Train linear regression model to predict optimal price
price_optimizer = LinearRegression(labelCol="avg_revenue_per_unit", featuresCol="total_units_sold")
price_model = price_optimizer.fit(sales_product_metrics)
# Generate pricing and inventory recommendations
product_recommendations = sales_product_metrics.withColumn("optimal_price", price_model.predict(col("total_units_sold"))) \
.select("product_id", "total_units_sold", "min_price", "max_price", "optimal_price")
# Write recommendations to Azure SQL Database
product_recommendations.write \
.format("jdbc") \
.option("url", "jdbc:sqlserver://yourserver.database.windows.net:1433;database=yourdatabase") \
.option("dbtable", "inventory_pricing_recommendations") \
.option("user", "yourusername") \
.option("password", "yourpassword") \
.mode("overwrite") \
.save()
```
In this example, we:
1. Read sales data and product data from Azure Data Lake.
2. Join the sales and product data to create a comprehensive dataset.
3. Calculate sales metrics for each product, such as total units sold, average revenue per unit, and price range.
4. Train a LinearRegression model to predict the optimal price for each product based on its sales velocity (total units sold).
5. Generate pricing and inventory recommendations, including the optimal price, and write them to an Azure SQL Database table.
These recommendations can be used to adjust pricing strategies, manage inventory levels, and optimize profitability for the e-commerce business.
## CI/CD with Azure DevOps
We use Azure DevOps for continuous integration and deployment. Our pipeline includes:
1. **Continuous Integration (CI)**
- Triggered on every push and pull request to the `main` branch
- Validates Azure Data Factory pipeline definitions
- Runs unit tests for Databricks notebooks and custom modules
- Lints SQL scripts and validates database objects
2. **Continuous Deployment (CD)**
- Triggered on successful merges to the `main` branch
- Deploys Azure Data Factory pipelines to a staging environment
- Runs integration tests
- Upon approval, deploys to the production environment
To view and modify these pipelines, check the `.azure-pipelines/` directory.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.