Big Data Overview
This course provides an in-depth overview of the choices you have in processing Big
Data. It introduces Big Data, the types of data you might have, approaches to working on
and processing the data, and the capabilities, strengths, and weaknesses of those
Topics covered include:
- NewSQL Databases
- NoSQL Overview
- Hadoop and MapReduce
- Apache Pig and Hive
- Apache Storm and Spark
- MongoDB, HBase
This course is suitable for: Data engineers, analysts, architects, data scientist, software
engineers, and technical managers who want a quick introduction to big data processing
tools and choices.
Some knowledge of database technologies is helpful.
Upon completing this course, students will be able to:
- Understand what Big Data is
- Know the difference between “data-at-rest” and “data-in-motion”
- Understand what map-reduce / Hadoop is, and what it can do
- Be aware of query technologies for easily querying with Hadoop (e.g. Hive, Pig, and
- Understand what NoSQL databases are and what they can do
- Become familiar with the choices in the NoSQL landscape
- Understand the strengths and weaknesses of different NoSQL technologies
- Be well-informed on your choices in Big Data processing, and evaluate them for your
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Register 21 days before class start date and save $250! Enter discount code EARLY250 during registration.
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.