Base Knowledge
Databases
Data Structures
SQL
Teaching Methodologies
- Final theoretical and practical exam – 30%
- Presentation and report of NoSQL/NewSQL pratical work- 30%
- BigData practical work with report – 30%
- Participation and continuous evaluation- 10%
Learning Results
On successful completion of this curricular unit, the student should be able to:
- Known and understand the principles and concepts of storage, processing and analysis of Big Data.
- Using NoSQL and NewSQL databases
- Evaluate the databases using benchmarks
- Identify and apply big data storage, processing, and analysis concepts and techniques to real-world Problems
- Select and use appropriate tools for storing, processing and analysing large volumes of data (Big Data)
Program
1. Introduction to Big Data
- A little of history
- What is Analytics?
- What is Big Data?
- Characteristics of Big Data
- Domain Specific Examples of Big Data
- Analytics Flow for Big Data
- Big Data Stack
2. What are NoSQL databases?
- What is wrong with the relational model?
- Big Data
- NoSQL
3. Characteristics of NoSQL databases
- NoSQL Architectures
- Data schemas
- Data Sharing and Sharing
- – Consistency
- – ACID and BASE models
4. Classification of the NoSQL databases
- Key-Value
- Document
- Column
- Graph
5. NewSQL databases
- Main features
- Functionalities
- Differences between SQL, NoSQL and NewSQL
6. Benchmarks for Database Evaluation
- Relevant properties of a NoSQL benchmark
- Benchmarks for Key-value databases
- Benchmarks for Document databases
- Benchmarks for Column databases
- Benchmarks for Graph databases
- Examples of practical works
7. Big Data – Introduction to Distributed Processing
- Limitations of traditional systems
- Features
- MapReduce and Hadoop
- Big Data platforms
8. Big Data – Storage
- Architecture
- HDFS – Hadoop Distributed File System
9. Big Data – Processing
- Tools and techniques
- Processing with Hadoop, Spark
- SQL on Hadoop, Hive
- Processing stream data
Internship(s)
NAO