BBA/B.Com Introduction to Big Data Technologies JUNE 2024

Description

Introduction to Big Data Technologies

June 2024 Examination

 

 

1. Data engineering plays a pivotal role in enabling organizations to derive actionable insights from the vast amounts of data they generate and collect.

A leading player in the healthcare industry plans to deploy a Hadoop system for capitalizing on its data assets to create a completely automated solution using AI.

What will be the different potential data sources available, data points gathered through each source and the affiliate Hadoop technologies required to treat each of the data types. State your rationale while explaining the technology as well.

Share the process flow as to how the company can achieve its goal of building automated solution and the decisions, which can be taken through these.  (10 Marks)

Ans 1.

Data engineering is a critical component in leveraging big data for actionable insights, especially in industries like healthcare. As organizations aim to capitalize on their data assets, deploying technologies like Hadoop becomes pivotal. For a leading player in the healthcare sector, the plan to deploy a Hadoop system for building a completely automated solution using AI highlights a strategic move towards efficient data utilization.

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2. State 3 use-cases of Big Data applications with different computing methods, highlighting the advantages/ challenges of each over others in the provided use-case. (Use-case must cover different computing methods).

Explain how social media applications are able to detect your potential friends/ networks and suggest these in real-time scenario. Share the Big-Data technologies and Analytic techniques which have enabled this facility.  (10 Marks)

Ans 2.

Introduction

Big data applications have revolutionized various industries by enabling organizations to analyze large volumes of data quickly and efficiently. In this context, three distinct use-cases of big data applications with different computing methods will be discussed, highlighting their advantages and challenges. Firstly, sentiment analysis in social media, utilizing batch processing, allows for deep analysis of historical data but may suffer from latency. Secondly, fraud detection in financial transactions, employing stream processing, enables real-time analysis but requires handling high data velocity and ensuring algorithm accuracy. Lastly, personalized

 

3. In this case study, we explore a project implemented at a leading e-commerce company, showcasing the application of data engineering skills and technologies in a real-world scenario.

Project Scope

The e-commerce company aimed to enhance its operational efficiency by optimizing its data processing and analysis capabilities. The project’s primary objectives included:

• Building a robust data pipeline for the ingestion, processing, and storage of various types of data, including customer transactions, website interactions, and inventory updates.
• Implementing data warehousing solutions to efficiently aggregate and store structured and unstructured data for reporting and analytics purposes.
• Developing real-time monitoring and reporting systems for better decision-making based on up-to-date data insights.

Tools and Technologies Utilized

The project leveraged a range of tools and technologies commonly used in  data engineering, including:

• Apache Spark and Apache Kafka for real-time data processing and stream analytics.
• Amazon Redshift for scalable data warehousing and analytical reporting.
• Python and SQL for data transformation, querying, and pipeline orchestration.
• Oozie for workflow management and scheduling of data tasks.
• Tableau for creating interactive and insightful data visualizations.
• Cloud computing services on AWS for scalable and cost-effective infrastructure provisioning.

Implementation

The implementation of the project followed best practices for DE project management, focusing on modularity, automation, and scalability. The team designed and built modular data pipelines to ensure repeatability and maintainability, in accordance with the principles of DataOps.

The data engineering team automated data ingestion, and ETL process using Oozie, ensuring the efficient orchestration of data workflows. They adhered to coding standards and maintained the desired documentation to facilitate collaboration and knowledge transfer within the team.

The project also incorporated security and governance policies for database management, ensuring the protection of sensitive customer and business data.

Results and Impact

The successful implementation of the data engineering project resulted in significant improvements in the e-commerce company’s operational efficiency and decision-making processes.  The  company gained the ability  to  perform  real-time  monitoring  of key business metrics and trends, leading to more informed decision-making across various departments, from marketing to supply chain management.

1. How can the e-commerce company ensure the security and data governance while dealing with the sensitive data? How does the provided infrastructure enable the security, scalability and repeatability in process workflow? (5 Marks)

Ans 3a.

In today’s digital landscape, where data is the lifeblood of businesses, ensuring security and data governance is paramount, especially for e-commerce companies dealing with sensitive customer information and transaction data. Implementing robust security measures and adhering to data governance practices not only protect sensitive data but also help companies comply with