LibGuides: DDS-8530: Module 2

Lesson 3 Required Resources

Data Extraction: Key Techniques, Tools, and Real-Time Benefits
Rivery. (2024, December 14). Data extraction: Key techniques, tools, and real-time benefits. Rivery. Retrieved from https://rivery.io/data-learning-center/data-extraction/

This resource explores essential data extraction techniques, tools, and the benefits of real-time data processing. It will help you understand how businesses automate and streamline data collection for analytics and decision-making.
What is Data Extraction? Data Extraction Tools & Techniques
Stitch. (n.d.). What is data extraction? Data extraction tools & techniques. Stitch. Retrieved from https://www.stitchdata.com/resources/what-is-data-extraction/

This article provides an overview of data extraction and the tools used to pull data from various sources. It will give you a foundational understanding of how organizations collect and integrate data for business intelligence.
Guide to Data Extraction: Definition, How It Works & Examples
Matillion. (n.d.). Guide to data extraction: Definition, how it works & examples. Matillion. Retrieved from https://www.matillion.com/blog/what-is-data-extraction-everything-you-need-to-know

This guide explains the data extraction process, including its definition, methods, and real-world applications. It will help you grasp how businesses leverage data extraction to enhance reporting, analytics, and operational efficiency.
Real-World Evidence in the Cloud: Tutorial on Developing an End-to-End Data and Analytics Pipeline Using Amazon Web Services Resources
Anderson, W., Bhatnagar, R., Scollick, K., Schito, M., Walls, R., & Podichetty, J. T. (2024). Real-world evidence in the cloud: Tutorial on developing an end-to-end data and analytics pipeline using Amazon Web Services resources. Clinical and Translational Science, 17, e70078. https://doi.org/10.1111/cts.70078

This tutorial provides a step-by-step guide to building a data and analytics pipeline in the cloud using AWS. It will help you understand how real-world data is processed, stored, and analyzed for clinical and translational research.
A Study on Intelligent Document Processing Using AWS
Sukrutha, K. S., Harini, S., & Kusuma, M. V. (2023). A study on intelligent document processing using AWS. International Journal for Multidisciplinary Research, 5(4), 1–5. Retrieved from https://www.ijfmr.com/papers/2023/4/4308.pdf

This study explores how AWS-powered intelligent document processing automates data extraction, classification, and analysis. It will help you understand how businesses leverage AI-driven tools to streamline document workflows and enhance efficiency.

Lesson 4 Required Resources

An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management
Nambiar, A., & Mundra, D. (2022). An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management. Big Data and Cognitive Computing, 6(4), 1-25. https://doi.org/10.3390/bdcc6040132

This paper provides a comparative analysis of data warehouses and data lakes, highlighting their roles in enterprise data management. It will help you understand the differences, use cases, and benefits of each approach in handling big data.

Data Engineering for Generative AI in the Cloud: Building Robust Data Pipelines for Scalable AI Models
James, C. (2024). Data engineering for generative AI in the cloud: Building robust data pipelines for scalable AI models. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 5(2), 1-5. Retrieved from ResearchGate

This paper explores best practices for designing data pipelines that support scalable generative AI models in cloud environments. It will help you understand how data engineering principles ensure efficient data flow, storage, and processing for AI-driven applications.
Architecture of Data Lake
Singh, A., & Ahmad, S. (2019). Architecture of data lake. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 5(2), 411–414. Retrieved from ResearchGate

This paper presents an overview of data lakes, discussing their benefits and architecture. It explores how data lakes serve as centralized repositories that can store structured and unstructured data, providing scalability and flexibility for advanced analytics in organizations.
MLOps: Emerging Trends in Data, Code, and Model Operationalization
Karamitsos, I., Barone, P., & Ghanbari, H. (2024). MLOps: Emerging trends in data, code, and model operationalization. International Journal of Business Intelligence Research, 15(1), 1–16. Retrieved from ResearchGate

This paper examines the latest developments in MLOps, focusing on the integration and operationalization of data, code, and machine learning models. It discusses best practices and tools that enhance the deployment, monitoring, and maintenance of ML systems in production environments.