BIG DATA MANAGEMENT

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Credits
6
Types
  • MIRI: Specialization complementary (Data Science)
  • IT4BI: Compulsory
Requirements
This subject has not requirements

Department
ESSI
The main goal of this course is to analyze the technological and engineering needs of Big Data Management. The enabling technology for such a challenge is cloud services, which provide the elasticity needed to properly scale the infrastructure as the needs of the company grow. Thus, students will learn advanced data management techniques (i.e., NOSQL solutions) that also scale with the infrastructure. Being Big Data Management the evolution of Data Warehousing, such knowledge (see the corresponding subject in Data Science speciality for more details on its contents) is assumed in this course , which will specifically focus on the management of data Volume and Velocity.

On the one hand, to deal with high volumes of data, we will see how a distributed file system can scale to as many machines as necessary. Then, we will study different physical structures we can use to store our data in it. Such structures can be in the form of a file format at the operating system level, or at a higher level of abstraction. In the latter case, they take the form of either sets of key-value pairs, collections of semi-structured documents or column-wise stored tables. We will see that, independently of the kind of storage we choose, current highly parallelizable processing systems using funtional programming principles (typically based on Map and Reduce functions), whose processing framework can rely on temporal files (like Hadoop MapReduce) or in-memory structures (like Spark).

On the other hand, to deal with high velocity of data, we need some low latency system which processes either streams or micro-batches. However, nowadays, data production is already beyond processing technologies capacity. More data is being generated than we can store or even process on the fly. Thus, we will recognize the need of (a) some techniques to select subsets of data (i.e., filter out or sample), (b) summarize them maximizing the valuable information retained, and (c) simplify our algorithms to reduce their computational complexity (i.e., doing one single pass over the data) and provide an approximate answer.

Finally, the complexity of a Big Data project (combining all the necessary tools in a collaborative ecosystem), which typically involves several people with different backgrounds, requires the definition of a high level architecture that abstracts technological difficulties and focuses on functionalities provided and interactions between modules. Therefore, we will also analyse different software architectures for Big Data.

This course participates in a joint project conducted during the second semester together with VBP, SDM and CC. In VBP, the students will come up with a business idea related to Big Data Management and Analytics, which will be evaluated from a business perspective. In the three other courses the students have to implement a prototype meeting the business idea created. In CC, they will be introduced to the main concepts behind large-scale distributed computing based on a service-based model and will have to choose the right infrastructure for their prototype. In BDM and SDM, they will be introduced to specific data management techniques to deal with Volume and Velocity (BDM) and Variety (SDM). As final outcome, a working prototype must be delivered.

Teachers

Person in charge

  • Alberto Abello Gamazo ( )

Others

  • Sergi Nadal Francesch ( )

Weekly hours

Theory
2
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
5.33

Competences

Generic Technical Competences

Generic

  • CG2 - Capability to lead, plan and supervise multidisciplinary teams.
  • CG5 - Capability to apply innovative solutions and make progress in the knowledge to exploit the new paradigms of computing, particularly in distributed environments.

Transversal Competences

Teamwork

  • CTR3 - Capacity of being able to work as a team member, either as a regular member or performing directive activities, in order to help the development of projects in a pragmatic manner and with sense of responsibility; capability to take into account the available resources.

Basic

  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.

Technical Competences of each Specialization

Specific

  • CEC1 - Ability to apply scientific methodologies in the study and analysis of phenomena and systems in any field of Information Technology as well as in the conception, design and implementation of innovative and original computing solutions.
  • CEC2 - Capacity for mathematical modelling, calculation and experimental design in engineering technology centres and business, particularly in research and innovation in all areas of Computer Science.
  • CEC3 - Ability to apply innovative solutions and make progress in the knowledge that exploit the new paradigms of Informatics, particularly in distributed environments.

Objectives

  1. Understand the differences and benefits of in-memory data management.
    Related competences: CG5, CB7, CEC1, CEC2, CEC3,
  2. Understand the execution flow of a distributed query.
    Related competences: CG5, CB7, CEC1, CEC2, CEC3,
  3. Identify the difficulties of scalability and parallelization.
    Related competences: CG5, CB7, CEC1, CEC2, CEC3,
  4. Design a distributed database using NoSQL tools.
    Related competences: CG2, CG5, CB7, CEC1, CEC2, CEC3, CTR3,
  5. Produce a functional program to process Big Data in a Cloud environment.
    Related competences: CG2, CG5, CB7, CEC1, CEC2, CEC3, CTR3,
  6. Manage and process a Data Stream.
    Related competences: CG2, CG5, CB7, CEC1, CEC2, CEC3, CTR3,
  7. Design the architecture of a Big Data management system.
    Related competences: CG2, CG5, CB7, CEC1, CEC2, CEC3, CTR3,

Contents

  1. Introduction
    Big Data, Cloud Computing, Scalability
  2. Big Data Design
    Polyglot systems; Schemaless databases; Key-value stores; Wide-column stores; Document-stores
  3. Distributed Data Management
    Transparency layers; Distributed file systems; File formats; Fragmentation; Replication and synchronization; Sharding; Consistent hash; LSM-Trees
  4. In-memory Data Management
    NUMA architectures; Columnar storage; Late reconstruction; Light-weight compression
  5. Distributed Data Processing
    Distributed Query Processing; Sequential access; Pipelining; Parallelism; Synchronization barriers; Multitenancy; MapReduce; Resilient Distributed Datasets; Spark
  6. Stream management and processing
    One-pass algorithms; Sliding window; Stream to relation operations; Micro-batching; Sampling; Filtering; Sketching
  7. Big Data Architectures
    Centralized and Distributed functional architectures of relational systems; Data Wareshousing architectures; Service Oriented Architecture; Lambda architecture

Activities

Theoretical lectures

In these activities, the lecturer will introduce the main theoretical concepts of the subject. Besides lecturing, cooperative learning techniques will be used. These demand the active participation of the students, and consequently will be evaluated.
Theory
28
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
28
Objectives: 2 1 3 5 6 7
Contents:

Projects

The students will be asked to define a BI project either using services or making decisions on a service, and prepare a demo of it in front of the classmates and a jury. Also, there will be some deliverables along the course.
Theory
0
Problems
0
Laboratory
9
Guided learning
0
Autonomous learning
57
Objectives: 2 3 4 5 6 7
Contents:

Exam

Written exam of the theoretical concepts introduced along the course.
Theory
2
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
8
Objectives: 2 1 3 4 5 6 7
Contents:

Lab

Students will use different NOSQL tools in a sandbox environment.
Theory
0
Problems
0
Laboratory
6
Guided learning
0
Autonomous learning
12
Objectives: 2 3 4 5 6
Contents:

Teaching methodology

The course comprises theory, problems, lab sessions and a project.

Theory: The theory classes comprise the teacher's explanations and constitute the main part of the course. The students will also have some contents to be read and prepared outside the classroom, and will be asked to participate in cooperative learning activities to solve some problems.

Lab: There will be some lab sessions to introduce some of the technologies and a cluster execution environment.

Project: The students will have to implement a proof of concept of an analytical service, which will share the topic with the project in other sibling subjects: BIP, SEAIT, VBP, SDM, CC, and WS (for those students taking them).

Evaluation methodology

Final mark = 35% C + 30% Pr + 5%P + 30%E

C = Weighted average of collaborative activities and labs
Pr = Project
P = Peer evaluation
E = Exam

Calculation of C:
1) Multiply the mark of each activity/lab by a weight equal to 1, 2, 4 or 6 (depending on the content of the corresponding activity/lab)
2) Divide the sum of these values by the sum of weights assigned minus 4

Calculation of P: Students will have multiple mates in the activities during the semester and they will evaluate them at the end. Based on these evaluations, the teacher will assign a mark to each student.

Project: Students will implement a proof of concept of the project and will present it in front of their classmates and a jury. Based on that presentation, and the delivered materials during the course the teacher will assign a mark to each student.

Bibliografy

Basic:

Complementary:

Previous capacities

Being Big Data Management the evolution of Data Warehousing, such knowledge is assumed in this course. Thus, general knowledge is expected on: Relational database desing; Database management system architecture; ETL and OLAP

Specifically, knowledge is expected on:
- Multidimensional modeling (i.e, star schemas)
- Querying relational databases
- Physical design of relational tables (i.e., partitioning)
- Hash and B-tree indexing
- External sorting algorithms (i.e., merge-sort)
- ACID transactions