Data Engineering Lecture Notes

Instructor Background

  • Dr. Ahmed M. Anter

    • Associate Professor of Computer Science at Benisuef University, Egypt.

    • Adjunct Professor at Egypt-Japan University of Science and Technology (E-JUST), Egypt.

    • M.Sc. and Ph.D. from Mansoura University in 2010 and 2016 respectively.

    • Former roles include team leader in software development and lecturer at Jazan University, Saudi Arabia.

    • Post-doctoral fellow at Shenzhen University (2018-2021).

    • Over 80 scientific research publications. Research interests: pattern recognition, machine learning, medical image processing, and optimization.

Course Overview

  • Tentative Topics

    • Concepts of Data Engineering

    • XML and JSON formats

    • AJAX Platform

    • ETL pipeline

    • SQL and T-SQL structure

    • Relational and NoSQL databases

    • Advanced SQL techniques

    • SQL Injection and Security

    • Data Warehousing

    • Big Data and Cloud Computing

  • Learning Objectives

    • Understand data engineering applications.

    • Analyze data and communicate findings effectively.

    • Implement analytic algorithms and experiment with datasets.

    • Adapt different data formats for specific applications.

    • Critically review data engineering research.

    • Present a real-world data engineering project.

    • Develop and evaluate machine-learning models.

Course Materials

  • Textbooks

    • Paul Crickard, Data Engineering With Python, 2020.

    • Vincent Rainardi, Building A Data Warehouse With Examples In SQL Server, 2008.

    • Nathan Marz & James Warren, Big Data, Principles And Best Practices Of Scalable Real-time Data Systems, 2015.

    • Ted Malaska & Shivnath Babu, Rebuilding Reliable Data Pipelines Through Modern Tools, O’Reilly, 2019.

    • Chris Fregly & Antje Barth, Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines, 2021.

Academic Integrity

  • Collaboration on ideas is permitted; solutions must be independently written.

  • Code snippets from other sources must be cited appropriately.

Expected Commitment

  • Regular attendance and preparedness for classes.

  • Active participation and respect for peers and instructors.

  • Completion of weekly assignments on time; no late submissions allowed.

Data Engineering Insights

  • Roles

    • Data Engineer: Design, build, and maintain systems for data storage and processing.

    • Data Analyst: Interpret existing data to help guide business decisions.

    • Data Scientist: Analyze data and create predictive models.

  • Average Salaries

    • Data Analyst: $59,000/year

    • Data Engineer: $90,839/year

    • Data Scientist: $91,470/year

Specific Responsibilities of Data Engineers

  • Design and implement data storage solutions.

  • Create and maintain data pipelines (ETL processes).

  • Ensure data security and compliance with regulations.

  • Monitor data quality and optimize performance.

  • Collaborate with team members and remain updated with technology trends.

Tools Used in Data Engineering

  • Big Data Technologies

    • Apache Hadoop and Apache Spark for processing large datasets.

  • Programming Languages

    • Python and SQL for data manipulation and analysis.

  • Database Solutions

    • Relational (e.g., MySQL) and NoSQL (e.g., MongoDB) databases.

  • Cloud Platforms

    • AWS, Google Cloud, or Azure for data storage and processing.

Examples of Data Engineering Tasks

  • Building a data warehouse for centralized data storage.

  • Creating ETL pipelines using tools like Apache NiFi or Apache Airflow.

  • Implementing data quality checks and security protocols.

  • Integrating various data sources to provide a unified view.

Data Engineering Skill Development

  • Master programming and data management skills.

  • Gain experience with data processing frameworks and tools.

  • Seek certifications in relevant fields.

  • Engage in continuous learning to keep up with industry trends.

  • Dr. Ahmed M. Anter

    • Associate Professor of Computer Science at Benisuef University, Egypt.

    • Adjunct Professor at Egypt-Japan University of Science and Technology (E-JUST), Egypt.

    • M.Sc. and Ph.D. from Mansoura University in 2010 and 2016 respectively.

    • Former roles include team leader in software development and lecturer at Jazan University, Saudi Arabia.

    • Post-doctoral fellow at Shenzhen University (2018-2021).

    • Over 80 scientific research publications. Research interests: pattern recognition, machine learning, medical image processing, and optimization.

  • Tentative Topics

    • Concepts of Data Engineering: Overview of data engineering, its importance in the data lifecycle, and how it supports analytics and decision-making.

    • XML and JSON Formats: Understanding hierarchical data representation, comparing XML with JSON in terms of readability and use cases, and common libraries for processing both formats.

    • AJAX Platform: Role of AJAX in web development, how it allows asynchronous data loading, and implications for user experience.

    • ETL Pipeline: Stages of Extract, Transform, Load processes; tools for building ETL pipelines; data cleansing and enrichment importance.

    • SQL and T-SQL Structure: Fundamentals of SQL; advanced T-SQL features; database schema design principles; indexing and query optimization techniques.

    • Relational and NoSQL Databases: Differences between relational databases and NoSQL, types of NoSQL databases (document, key-value, column-family), and their suitable use cases.

    • Advanced SQL Techniques: Complex joins, subqueries, window functions, and stored procedures.

    • SQL Injection and Security: Understanding SQL injection vulnerabilities; best practices for writing secure SQL queries; importance of parameterized queries and prepared statements.

    • Data Warehousing: Concepts and architecture of data warehouses; dimensional modeling (star/snowflake schema); ETL considerations for data warehousing.

    • Big Data and Cloud Computing: Characteristics of big data (volume, velocity, variety); cloud services for data storage and processing (IaaS, PaaS, SaaS); overview of cloud platforms (AWS, Azure, Google Cloud).

  • Learning Objectives

    • Understand applications in data engineering across industries.

    • Analyze data thoroughly and communicate findings effectively to stakeholders.

    • Implement analytic algorithms using statistical and machine learning techniques; experiment with datasets to extract insights.

    • Adapt data formats (XML, JSON, CSV) for specific applications while ensuring data integrity.

    • Critically review existing data engineering research papers to understand trends and methodologies.

    • Present a well-structured real-world data engineering project, showcasing application of theoretical concepts.

    • Develop and evaluate machine-learning models using frameworks (e.g., TensorFlow, Scikit-learn).

  • Textbooks

    • Paul Crickard, Data Engineering With Python, 2020: Covers data management, frameworks, and data pipeline creation with Python.

    • Vincent Rainardi, Building A Data Warehouse With Examples In SQL Server, 2008: Focus on practical applications in SQL server environments.

    • Nathan Marz & James Warren, Big Data, Principles And Best Practices Of Scalable Real-time Data Systems, 2015: Insights into big data architectures and processing techniques.

    • Ted Malaska & Shivnath Babu, Rebuilding Reliable Data Pipelines Through Modern Tools, O’Reilly, 2019: Principles of building and maintaining data pipelines using current tools.

    • Chris Fregly & Antje Barth, Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines, 2021: Real-case applications of deploying data science projects in AWS.

  • Collaboration on ideas to enhance understanding is encouraged; however, all solutions and writings must be independently developed.

  • When using code snippets or data from external sources, proper citation is required to maintain academic honesty and integrity.

  • Regular attendance to classes is essential for success; preparedness helps in deeper understanding of topics.

  • Active participation in discussions is expected; respecting peers and instructors fosters a positive learning environment.

  • Weekly assignments are to be completed on time; unexcused late submissions are not accepted due to the structured nature of the coursework.

Data Engineering Insights

  • Roles

    • Data Engineer: Responsible for designing, building, and maintaining infrastructure for data storage and processing, focusing on performance and scalability.

    • Data Analyst: Analyzes data sets to draw business insights, helps in the decision-making process through data interpretation.

    • Data Scientist: Engages in advanced analytics, including predictive modeling and statistical analysis, often using machine learning techniques.

  • Average Salaries

    • Data Analyst: Approximately $59,000/year; generally entry-level positions with opportunities for advancement.

    • Data Engineer: Around $90,839/year; higher demand due to the growing reliance on data infrastructure.

    • Data Scientist: Approximately $91,470/year; often involves complex statistical analysis and requires a higher level of expertise.

Specific Responsibilities of Data Engineers

  • Design and implement robust data storage solutions tailored to organizational needs while ensuring reliability and performance.

  • Create, maintain, and optimize ETL processes for data integration from multiple sources to a centralized repository.

  • Ensure compliance with data security regulations (e.g., GDPR, HIPAA); conduct regular audits and data quality checks.

  • Monitor and enhance data quality to ensure accurate and timely data analytics.

  • Collaborate with cross-functional teams (e.g., data scientists, analysts) to align data strategy with business objectives; stay updated with emerging technology trends.

Tools Used in Data Engineering

  • Big Data Technologies

    • Apache Hadoop: Framework for distributed storage and processing of large datasets. Supports data-intensive applications through its HDFS and MapReduce functionalities.

    • Apache Spark: Unified analytics engine for large-scale data processing, with powerful APIs for parallel processing and real-time analytics.

  • Programming Languages

    • Python: Widely used for data manipulation, offers libraries such as Pandas and NumPy for data analysis.

    • SQL: Essential language for interacting with databases; includes querying, updating, and managing databases effectively.

  • Database Solutions

    • Relational Databases (e.g., MySQL, PostgreSQL): Structured databases that use SQL to manage data organized in tables.

    • NoSQL Databases (e.g., MongoDB, Cassandra): Flexible databases designed for unstructured data, providing scalability and speed for data handling.

  • Cloud Platforms

    • AWS (Amazon Web Services): Offers a suite of services for hosting databases, data warehousing, and big data applications like Redshift and EMR.

    • Google Cloud Platform: Provides tools for big data and machine learning, such as BigQuery and Dataflow.

    • Microsoft Azure: Cloud services for building, deploying, and managing applications with tools like Azure SQL Database and Azure Data Lake.

Examples of Data Engineering Tasks

  • Building a data warehouse for centralized data storage, ensuring it supports reporting and data analysis functions.

  • Creating ETL pipelines using tools like Apache NiFi, Apache Airflow, or custom scripts for automating data flows.

  • Implementing comprehensive data quality checks and security protocols to safeguard data integrity.

  • Integrating diverse data sources to create a unified view, which involves dealing with data inconsistencies and merging strategies.

Data Engineering Skill Development

  • Master programming skills (particularly in Python) and gain proficiency in data management practices and frameworks.

  • Gain experience with modern data processing frameworks (Apache Spark, Apache Kafka) through hands-on projects.

  • Seek certifications in relevant fields (e.g., AWS Certified Data Analytics, Google Professional Data Engineer) to enhance employability.

  • Engage in continuous learning, staying informed about industry trends and new tech developments to maintain a competitive edge in the field.