Este curso forma parte de Programa Especializado - Big Data
Big Data Modeling and Management Systems
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Acerca de este Curso
4.3
1,655 ratings
Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples. Systems and tools discussed include: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. This course provides techniques to extract value from existing untapped data sources and discovering new data sources.
At the end of this course, you will be able to:
* Recognize different data elements in your own work and in everyday life problems
* Explain why your team needs to design a Big Data Infrastructure Plan and Information System Design
* Identify the frequent data operations required for various types of data
* Select a data model to suit the characteristics of your data
* Apply techniques to handle streaming data
* Differentiate between a traditional Database Management System and a Big Data Management System
* Appreciate why there are so many data management systems
* Design a big data information system for an online game company
This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.
Hardware Requirements:
(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size.
Software Requirements:
This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
Habilidades que obtendrás
Data ModelBig DataData ModelingData Management
Programa - Qué aprenderás en este curso
1
Sección
Introduction to Big Data Modeling and Management
Welcome to this course on big data modeling and management. Modeling and managing data is a central focus of all big data projects. In these lessons we introduce you to the concepts behind big data modeling and management and set the stage for the remainder of the course.
14 videos (Total: 63 min), 8 readings
14 videos
Why is this a New Course in the Big Data Specialization?m
Summary of Introduction to Big Data (Part 1)5m
Summary of Introduction to Big Data (Part 2)5m
Summary of Introduction to Big Data (Part 3)5m
Big Data Management "Must-Ask Questions"1m
Data Ingestion4m
Data Storage3m
Data Quality2m
Data Operations3m
Data Scalability and Security2m
Energy Data Management Challenges at ConEd4m
Gaming Industry Data Management: Q&A with Apmetrix CTO Mark Caldwell7m
Flight Data Management at FlightStats: A Lecture by CTO Chad Berkley13m
8 lecturas
Slides: Summary of Introduction to Big Data10m
Slides: Big Data Management10m
Reading on Storage Systems10m
Slides: Energy Data Management Challenges at ConEd10m
Slides: Flight Data Management at FlightStats10m
Downloading and Installing the Cloudera VM Instructions (Windows)10m
Downloading and Installing the Cloudera VM Instructions (Mac)10m
Instructions for Downloading Hands On Datasets10m
2
Sección
Big Data Modeling
Modeling big data depends on many factors including data structure, which operations may be performed on the data, and what constraints are placed on the models. In these lessons you will learn the details about big data modeling and you will gain the practical skills you will need for modeling your own big data projects.
11 videos (Total: 52 min), 8 readings, 1 quiz
11 videos
Data Model Structures2m
Data Model Operations4m
Data Model Constraints4m
Introduction to CSV Data4m
What is a Relational Data Model?10m
What is a Semistructured Data Model?6m
Exploring the Relational Data Model of CSV Files4m
Exploring the Semistructured Data Model of JSON data3m
Exploring the Array Data Model of an Image3m
Exploring Sensor Data4m
8 lecturas
Slides: What Is A Data Model?10m
Introduction to CSV Data10m
Slides: What Is A Relational Data Model?10m
Slides: What is a Semistructured Data Model?10m
Exploring the Relational Data Model of Comma Separated Values (CSV)10m
Exploring the Semistructured Data Model of JSON data10m
Exploring the Array Data Model of an Image10m
Exploring Sensor Data10m
1 ejercicio de práctica
Practical Quiz for Week 2 Hands-On Lectures18m
3
Sección
Big Data Modeling (Part 2)
These lessons continue to shed light on big data modeling with specific approaches including vector space models, graph data models, and more.
5 videos (Total: 31 min), 5 readings, 1 quiz
5 videos
Graph Data Model7m
Other Data Models4m
Exploring the Lucene Search Engine's Vector Data Model4m
Exploring Graph Data Models with Gephi3m
5 lecturas
Slides: Vector Space Model10m
Slides: Graph Data Model10m
Slides: Other Data Models10m
Exploring Vector Data Models with Lucene10m
Exploring Graph Data Models with Gephi10m
1 ejercicio de práctica
Data Models Quiz18m
4
Sección
Working With Data Models
Data models deal with many different types of data formats. Streaming data is becoming ubiquitous, and working with streaming data requires a different approach from working with static data. In these lessons you will gain practical hands-on experience working with different forms of streaming data including weather data and twitter feeds.
6 videos (Total: 29 min), 7 readings, 1 quiz
6 videos
What is a Data Stream?5m
Why is Streaming Data different?7m
Understanding Data Lakes5m
Exploring Streaming Sensor Data4m
Exploring Streaming Twitter Data (Optional)4m
7 lecturas
Slides: Data Model vs. Data Format10m
Slides: What is a Data Stream?10m
Slides: Why is Streaming Data Different?10m
Slides: Understanding Data Lakes10m
Exploring Streaming Sensor Data10m
Instructions for Creating a Twitter App (Optional)10m
Exploring Streaming Twitter Data (Optional)10m
1 ejercicio de práctica
Data Formats and Streaming Data Quiz18m
4.3
48%
83%
Principales revisiones
por MP•Oct 17th 2017
Good Explanations of Concepts and Nice Tests. I got a trilling experience in completing the peer Assignments with keen observation and Analyzing of Concepts learned.Thanq for your course very much.
por VG•Mar 28th 2017
Nice course to describe the traditional data modeling (RDBMS) as well as various semi-structured and un-structured data modeling and management of the systems (Batch and Streaming data processing)
Instructores
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
Acerca de University of California San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Acerca del programa especializado Big Data
Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.
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Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.
Preguntas Frecuentes
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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