Welcome to a showcase of my data engineering projects. Below, you'll find a list of databases I've created in Snowflake, each designed to serve specific purposes. These projects involve managing, analyzing, and deriving insights from various types of data. Explore the details of each project below.
Before diving into the project, you'll need the following prerequisites:
Sales and Customer Database: Stored infromation about the cstomers, sales, transactions, products, and orders. Also it was used to track the customer details, purchase history, balance sheets, and cash flow statements.
Schema Name: SalesDB
Tables: Customer, Products, Orders, OrderDetails
Schema Purpose: Store information about customers, products, and sales transactions. Use normalized tables for better data integrity. Also, this database contains data about customers, sales, transactions, products, and orders. It is used to track customer details, purchase history, balance sheets, and cash flow statements.
Keep track of inventory levels, product details, and supplier information. Monitor stock levels, reorder points, and manage supply chain operations.
Schema Name: InventoryDB
Tables: Products, Suppliers, Inventory, Orders
Schema Purpose: Keep track of inventory levels, product details, and supplier information. Monitor stock levels, reorder points, and manage supply chain operations.
Support an e-commerce platform by storing product catalogs, customer profiles, and order histories. Implement features like recommendation engines and personalized shopping experiences.
Schema Purpose: Support an e-commerce platform by storing product catalogs, customer profiles, and order histories. Implement features like recommendation engines and personalized shopping experiences.
Tables: Products, Customers, Orders, Reviews
Monitor the movement of goods, shipments, and delivery schedules. Optimize supply chain processes, track logistics costs, and improve efficiency.
Schema Purpose: Monitor the movement of goods, shipments, and delivery schedules. Optimize supply chain processes, track logistics costs, and improve efficiency.
Tables: Shipments, Suppliers, Inventory, DeliveryRoutes
Collect and analyze data from social media platforms to gain insights into customer sentiment and behavior. Perform sentiment analysis, trend analysis, and engagement tracking.
Schema Purpose: Collect and analyze data from social media platforms to gain insights into customer sentiment and behavior. Perform sentiment analysis, trend analysis, and engagement tracking.
Tables: Posts, Users, SentimentAnalysis, EngagementMetrics
Centralize data from various sources for analytics and data science projects. Enable data exploration, reporting, and machine learning model development.
Schema Purpose: Centralize data from various sources for analytics and data science projects. Enable data exploration, reporting, and machine learning model development.
Tables: RawData, CleanedData, Models, Reports
Manage geospatial data, including location coordinates, maps, and spatial analytics. Support applications related to mapping, geolocation, and route optimization.
Schema Purpose: Manage geospatial data, including location coordinates, maps, and spatial analytics. Support applications related to mapping, geolocation, and route optimization.
Tables: Locations, Maps, Routes, SpatialData
Manage financial data, including income statements, balance sheets, and cash flow statements. Perform financial analysis, forecasting, and reporting.
Schema Name: FinanceDB
Tables: IncomeStatements, BalanceSheets, CashFlowStatements
Schema Purpose: Manage financial data, including income statements, balance sheets, and cash flow statements. Perform financial analysis, forecasting, and reporting.
This project is an end-to-end ETL (Extract, Transform, Load) pipeline designed to automate the extraction of data from a Spotify playlist and process it using various AWS services. The goal is to showcase proficiency in data engineering and AWS, with a focus on data extraction, transformation, and automated processing.
More details at Building an Automated Data Pipeline (ETL) for Spotify's Globally Famous Songs Dataset on AWS
This project is an end-to-end ETL (Extract, Transform, Load) pipeline designed to automate the extraction of data from a Spotify playlist and process it using various AWS services. The goal is to showcase proficiency in data engineering and AWS, with a focus on data extraction, transformation, and automated processing. I have successfully completed the Spotify ETL pipeline project on AWS.
More details at Data Engineering Project: ETL Pipeline from Spotify API to Snowflake Data Warehouse
