Big Data in Action
Master the Big Data ecosystem through 15 project-based sessions: Hadoop, HDFS, MapReduce, Hive, HBase, NiFi, Spark, Kafka and Spark MLlib. By the end of the course, you will be able to design and operate your own Big Data Platform to solve real business problems.
Instructor: Nguyễn Văn QuyếtLast updated 11/10/2026
About this course
Course description
Big Data Analytics is one of the breakthrough technologies of the 21st century and has become one of the most in-demand fields for talent today. Through big data analytics, businesses gain deep insight into their data and leverage its core value to make fast, accurate decisions, improve their products and optimize pricing. In Vietnam, big data is being applied across many key industries, and the demand for Big Data Analytics professionals, along with their salaries, is growing accordingly.
The "Big Data Analytics" course gives learners a solid foundation in Big Data frameworks to design and operate a Big Data Platform that solves real business problems. With a problem-based and project-based approach, the course helps learners build hands-on skills and tackle real-world challenges using Big Data techniques and tools.
Course format
The course consists of 14 sessions plus 1 final session for project presentations and career opportunities. Each session is built around a project and includes 1.5 hours of theory and 1.5 hours of hands-on practice.
Who this course is for
- IT, Data Science and Information Systems students aiming for a Data Engineer / Big Data Engineer career
- Software developers who want to move into the big data field
- Data analysts and BI specialists who need to work with large-scale data
- System engineers and database administrators who build and operate Big Data infrastructure
Prerequisites
- Programming experience (Java, Python or Scala is an advantage)
- Familiarity with Linux commands is an advantage
- A personal computer with Internet access, at least 8GB of free space, able to run 3 Ubuntu 20.04 LTS virtual machines connected over a LAN
Tools
Apache Hadoop, Apache Spark, Apache Sqoop, Apache NiFi, Apache Hive, Apache HBase, Apache Kafka, BIRT Report, Eclipse.
Learning outcomes
- Set up and operate a multi-node Hadoop/Spark cluster
- Build pipelines to ingest, store and process big data in both batch and streaming modes
- Apply machine learning on big data with Spark MLlib
- Complete a capstone project: design and deploy a Big Data Platform for a real-world problem
What you will learn
- Gain a solid understanding of Big Data fundamentals, Big Data Analytics, and their real-world applications.
- Install, configure, and proficiently use Apache Hadoop and the Hadoop Distributed File System (HDFS).
- Understand and apply the MapReduce programming model for parallel processing of large-scale datasets.
- Ingest and integrate large-scale data from multiple sources using Apache Sqoop and Apache NiFi.
- Store, manage, and query large-scale datasets using Apache Hive and Apache HBase.
- Process large-scale datasets using Apache Spark, including Spark Core, Spark SQL, and DataFrames.
- Perform real-time Big Data stream processing using Spark Streaming and Apache Kafka.
- Apply Apache Spark MLlib to association rule mining, classification, clustering, and recommendation systems.
- Design Data Lakes, Data Warehouses, and Extract, Transform, Load (ETL) architectures within Big Data Platforms.
- Visualize data and develop analytical reports using BIRT
Curriculum
15 lessons · 55 topics · 45 hours
Introduction to Big Data Analytics4 topics · 3 hours
Get familiar with big data concepts, its challenges, opportunities and the Hadoop ecosystem; set up your first Hadoop cluster.
- Introduction to big data30 min
- Challenges and opportunities of big data analytics30 min
- Big data analytics tools and technologies in the Hadoop ecosystem30 min
- Hands-on: Setting up a Hadoop cluster on virtual machines1 hr 30 min
Hadoop Distributed File System (HDFS)5 topics · 3 hours
Understand the HDFS architecture and manage big data through the Web Interface and Shell Commands.
- What is HDFS? The role of HDFS in a Big Data system20 min
- HDFS architecture30 min
- Managing files on HDFS via the Web Interface20 min
- Managing big data files on HDFS via Shell Commands20 min
- Hands-on: Working with data on HDFS1 hr 30 min
Big Data Ingestion Tools3 topics · 3 hours
Ingest data from multiple sources into a Big Data system using Apache NiFi.
- Introduction to Data Ingestion in Big Data systems45 min
- Multi-source Data Ingestion with Apache NiFi45 min
- Hands-on: Ingesting data from multiple sources with Apache NiFi1 hr 30 min
Big Data Processing with MapReduce3 topics · 3 hours
Learn the MapReduce programming model and process big data on HDFS.
- The MapReduce programming model45 min
- Monitoring Hadoop MapReduce jobs via the Web Interface45 min
- Hands-on: Processing big data on HDFS with Hadoop MapReduce1 hr 30 min
Storing and Querying Big Data with Hive3 topics · 3 hours
Use Apache Hive and Hive SQL to store and query large structured datasets.
- Introduction to Apache Hive45 min
- Working with structured big data using Hive SQL45 min
- Hands-on: Working with big data using Hive SQL1 hr 30 min
Storing and Accessing Big Data with HBase4 topics · 3 hours
Explore the column-oriented database Apache HBase and integrate HBase with Hive.
- Introduction to Apache HBase30 min
- Column-oriented data storage30 min
- Integrating HBase with Hive30 min
- Hands-on: Storing and accessing big data with HBase1 hr 30 min
Big Data Processing with Spark (1)4 topics · 3 hours
Overview and architecture of Apache Spark; working with Spark Core and its key APIs.
- Introduction to Spark30 min
- Spark architecture30 min
- Working with Spark Core30 min
- Hands-on: Using key Spark Core APIs1 hr 30 min
Big Data Processing with Spark (2)5 topics · 3 hours
Process structured data with Spark SQL and DataFrames, and query data in Hive.
- Introduction to Spark SQL20 min
- Introduction to Spark DataFrame20 min
- Basic operations on Spark DataFrames20 min
- Querying Hive data with Spark SQL30 min
- Hands-on: Processing big data with Spark and Hive1 hr 30 min
Streaming Big Data Analytics with Kafka & Spark (1)3 topics · 3 hours
Learn Apache Kafka: its role, architecture and components; install and operate a Kafka cluster.
- Introduction to Kafka and its role in Big Data systems45 min
- Kafka architecture, components and how it works45 min
- Hands-on: Installing and using a Kafka cluster1 hr 30 min
Streaming Big Data Analytics with Kafka & Spark (2)3 topics · 3 hours
Stream processing models and building streaming applications with Spark.
- Stream processing models45 min
- Working with the Spark Streaming API45 min
- Hands-on: Building a simple stream processing application with Spark1 hr 30 min
Frequent Itemset and Association Rule Mining on Big Data with Spark MLlib4 topics · 3 hours
Algorithms for frequent itemset and association rule mining, implemented with Spark MLlib.
- Introduction to frequent itemset and association rule mining30 min
- Frequent itemset and association rule mining algorithms30 min
- Frequent itemset and association rule mining techniques with Spark MLlib30 min
- Hands-on: Frequent itemset and association rule mining with Spark MLlib1 hr 30 min
Big Data Classification and Clustering with Spark MLlib4 topics · 3 hours
Apply classification and clustering algorithms to big data with Spark MLlib.
- Introduction to classification and clustering30 min
- Classification and clustering algorithms30 min
- Classification and clustering techniques with Spark MLlib30 min
- Hands-on: Classification and clustering with Spark MLlib1 hr 30 min
Big Data Recommender Systems4 topics · 3 hours
Methods for building recommender systems and implementing them with Spark MLlib.
- Introduction to recommender systems30 min
- Approaches to building recommender systems30 min
- Building recommender systems with Spark MLlib30 min
- Hands-on: Building a recommender system with Spark MLlib1 hr 30 min
Designing a Big Data Platform4 topics · 3 hours
Design a big data platform architecture, Data Lake and Data Warehouse, and estimate resources and investment costs.
- Designing a big data processing platform architecture30 min
- Designing a Data Lake and Data Warehouse30 min
- Resource sizing and investment estimation30 min
- Hands-on: Building a Big Data Platform1 hr 30 min
Final Project Report2 topics · 3 hours
Learners present their capstone projects, receive feedback from the instructor and explore career opportunities.
- Learner project presentations1 hr 30 min
- Instructor evaluation & discussion1 hr 30 min
Instructor
Nguyễn Văn Quyết
Big Data Analyst
Dr. Nguyen Van Quyet received his Ph.D. in Computer Science from Chonnam National University, South Korea, in 2019, his M.Sc. in Computer Science from Hanoi University of Science and Technology in 2013, and his B.Eng. in Information Technology from Hung Yen University of Technology and Education in 2009. His research interests include Big Data Analytics, Big Graph Processing, Machine Learning and the Internet of Things. He has more than 10 years of experience in Big Data and has taken part in many real-world projects building big data processing systems for organizations and enterprises in South Korea. He has taught over 10 Big Data Analytics courses for organizations and businesses in Vietnam. He has published numerous ISI and Scopus papers and serves on the program committees of many national and international Computer Science conferences and journals.