Stock Market Data Processing with Apache Spark & Deequ
A comprehensive big data processing project focused on ingesting and transforming historical stock market data, ensuring data quality through automated monitoring and validation.
Technologies Used
Key Features
- • Distributed Data Processing
- • Automated Quality Checks
- • Real-time Monitoring
Data Quality
- • Six Quality Dimensions
- • Automated Testing
- • Continuous Monitoring
Project Overview
In this big data processing project, I developed a scalable ETL pipeline for ingesting and transforming historical stock market data from Nasdaq. The objective was to ensure that stock price datasets were readable, reliable, and robust for downstream data science applications, including trading models and market analysis.
Key Objectives
- • Ingest and clean stock market data for time-sensitive ETL processes
- • Perform dataset profiling based on the six dimensions of data quality
- • Implement data quality tests using Apache Deequ to establish an automated data quality monitoring process
Implementation Details
1. Data Ingestion & Cleaning
- • Extracted raw stock market data from CSV files using Apache Spark for distributed processing
- • Applied common data engineering transformations, including parsing date formats and filtering invalid ticker symbols
- • Imputed missing values in price and volume columns using statistical methods
- • Ensured the ingestion pipeline was scalable and optimized for batch data processing
2. Data Profiling & Quality Assessment
- • Implemented data transformations for dataset exploration and enrichment
- • Profiled data using histograms and statistical summaries to analyze stock price distribution
- • Evaluated six key data quality dimensions:
- • Accuracy: Verified stock price changes aligned with historical patterns
- • Consistency: Ensured uniform column formats across all records
- • Timeliness: Checked timestamp freshness for real-time updates
- • Validity: Flagged incorrect data points outside expected ranges
- • Completeness: Identified and handled missing records
- • Uniqueness: Ensured non-duplicate ticker entries per trading day
3. Automated Data Quality Testing with Deequ
- • Integrated Apache Deequ to automate data quality checks on incoming stock data
- • Configured Deequ constraints to validate:
- • Null values in critical columns
- • Range checks on stock prices and trading volumes
- • Data drift detection to flag anomalies over time
- • Implemented a continuous monitoring system for real-time quality assurance
Outcome & Impact
This ETL pipeline ensured that stock market datasets met high-quality standards for use in financial modeling and trading applications. The automated data quality framework enhanced data reliability and reduced manual intervention, leading to improved efficiency in stock analysis. The project enhanced expertise in big data ETL, Apache Spark optimization, and automated data quality assurance.
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