Platforms for Big Data Processing

DATA 603 Platforms for Big Data Processing

A graduate level course on the subject

Last updated: May 10, 2026

Course Description

(from UMBC website, https://dil.umbc.edu/courses/data-603-platforms-for-big-data-processing/)

The goal of this course is to introduce methods, technologies, and computing platforms for performing data analysis at scale. Topics include the theory and techniques for data acquisition, cleansing, aggregation, management of large heterogeneous data collections, processing, information and knowledge extraction. Students are introduced to map-reduce, streaming, and external memory algorithms and their implementations using Hadoop and its eco-system (HBase, Hive, Pig and Spark). Students will gain practical experience in analyzing large existing databases.

Note: The current syllabus reflects modern practices in Big Data and does not follow the exact guidelines.

Prerequisites:

DATA 601. Enrollment in the Data Science Program. Other students may be admitted with instructor permission.

Course Learning Outcomes:

Upon completion, students will:

* have the ability to analyze large datasets using modern tools,

* be able to analyze big data architectures for data science applications,

* be familiar with how to use the public domain big data tooling pipeline such as Spark and its eco system,

* know the fundamental concepts, strategies and pitfalls of processing data at scale.

Syllabus (Tentative)

Week 1 – Introduction to the Course, Book, and Tools

Week 2 – Big Data and Databricks

Week 3 – Hadoop HDFS and MapReduce

Week 4 – AWS Cloud Foundations – I

Week 5 – AWS Cloud Foundations – II

Week 6 – Apache Spark’s Structured APIs

Week 7 – Structured API – Spark SQL and DataFrames – I

Week 8 – Structured API – Spark SQL and DataFrames – II

Week 9 – Structured Streaming

Week 10 – Low-Level APIs – Spark RDDs

Week 11 – Spark MLLib – I

Week 12 – Spark MLLib - II

Week 13 – MongoDB

Week 14 – Cassandra DB

Week 15 – Project presentations