#7

Big Data Pipeline for E-commerce Personalization for Amazon

45 Hours

Build a big data pipeline that processes customer data at scale to deliver personalized product recommendations on Amazon. Use Apache Hadoop for distributed data storage and Spark for data processing. Implement recommendation algorithms using machine learning libraries in Spark and integrate the output into Amazon S3 for fast retrieval.

#8

Real-Time Financial Data Processing for Goldman Sachs

45 Hours

Develop a high-frequency trading data pipeline for processing and analyzing real-time stock market data. Use Apache Kafka for capturing real-time data streams from financial markets, apply Apache Flink for complex event processing (CEP), and store the processed data in AWS Redshift for real-time analysis. Provide insights to assist in trading strategies.

#1

Sales Data ETL Pipeline for Nike

35 hours

Develop an ETL pipeline to process Nike’s sales data from multiple sources, including online and in-store transactions. Use Apache Airflow to orchestrate the ETL process, ensuring that data is consistently and reliably extracted, transformed, and loaded into a cloud data warehouse. Leverage PySpark for data transformation tasks such as aggregating sales by region and product category. Store the processed data in AWS Redshift to enable advanced analytics and reporting.

#2

Customer Journey Analysis for Netflix

40 hours

Create a data pipeline to analyze the customer journey for Netflix, tracking interactions from browsing to subscription and viewing behavior. Use Apache Kafka to stream real-time data from Netflix’s user activity logs. Process the streaming data with Apache Flink to handle high-throughput and real-time data processing. Store the processed data in Google BigQuery, which allows for scalable and fast SQL queries to uncover insights into user behavior and engagement.

#3

Inventory Management Optimization for Home Depot

30 hours

Design a data pipeline to optimize inventory levels at Home Depot. Utilize Apache NiFi to ingest data from various sources such as point-of-sale systems and supplier databases. Process and analyze inventory data with PySpark to identify trends and optimize stock levels. Develop a visualization dashboard in Tableau to provide real-time insights into inventory levels, helping Home Depot make data-driven decisions to improve stock management and reduce excess inventory.

#4

Traffic Data Analytics for Uber

22 Hours

Build a pipeline to analyze real-time traffic data for Uber’s ride-hailing service. Stream traffic and GPS data using Apache Kafka to capture and process location-based information. Use PySpark to process and analyze this data in real-time, calculating metrics such as average travel times and traffic congestion. Store the processed data in Azure Synapse Analytics for comprehensive analysis and visualization, helping Uber optimize routing and improve customer experience.

#5

Building a Real-Time Ad Analytics Platform for Facebook

40 Hours

Create a real-time data pipeline to analyze Facebook ad performance. Use Apache Kafka to collect streaming ad data, process it using PySpark Streaming for real-time metrics, and store the processed data in Google BigQuery for deeper analysis. Build an interactive dashboard using Tableau for monitoring ad performance across various demographics and geographies.

#6

Fraud Detection Data Pipeline for PayPal

40 hours

Create a data pipeline to detect and prevent fraudulent transactions for PayPal. Use Apache Kafka to stream transaction data in real-time. Process this data with Apache Flink to apply fraud detection algorithms and identify suspicious patterns. Store the results in Google BigQuery, where you can perform detailed analysis and generate alerts for further investigation. This pipeline will help PayPal enhance its fraud detection capabilities and secure financial transactions

#7

Big Data Pipeline for E-commerce Personalization for Amazon

45 Hours

Build a big data pipeline that processes customer data at scale to deliver personalized product recommendations on Amazon. Use Apache Hadoop for distributed data storage and Spark for data processing. Implement recommendation algorithms using machine learning libraries in Spark and integrate the output into Amazon S3 for fast retrieval.

#8

Real-Time Financial Data Processing for Goldman Sachs

45 Hours

Develop a high-frequency trading data pipeline for processing and analyzing real-time stock market data. Use Apache Kafka for capturing real-time data streams from financial markets, apply Apache Flink for complex event processing (CEP), and store the processed data in AWS Redshift for real-time analysis. Provide insights to assist in trading strategies.