Big Data Modeling and Management Systems
Master big data collection, storage, and organization techniques using specialized platforms for hands-on learning.
Master big data collection, storage, and organization techniques using specialized platforms for hands-on learning.
This intermediate course focuses on collecting, storing, and organizing big data using appropriate management solutions. You'll learn to identify different data elements and design effective Big Data Infrastructure Plans for various scenarios. The curriculum covers different data models suited for specific data characteristics and teaches techniques for handling streaming data. You'll understand the key differences between traditional Database Management Systems and Big Data Management Systems, exploring platforms like AsterixDB, HP Vertica, Impala, Neo4j, Redis, and SparkSQL. Through guided hands-on tutorials with real-time and semi-structured data examples, you'll gain practical experience with various data genres and management tools. The course culminates with a practical project designing a big data information system for an online game company, allowing you to apply your knowledge to a realistic business scenario.
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What you'll learn
Recognize different data elements in your work and everyday problems
Explain why teams need a Big Data Infrastructure Plan and Information System Design
Identify frequent data operations required for various types of data
Select appropriate data models to suit specific data characteristics
Apply techniques to effectively handle streaming data
Differentiate between traditional DBMS and Big Data Management Systems
Skills you'll gain
This course includes:
3.2 Hours PreRecorded video
4 assignments
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FullTime access
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There are 6 modules in this course
This comprehensive course delves into both theoretical and practical aspects of big data modeling and management systems. Students first learn about fundamental concepts including data ingestion, storage, quality, operations, scalability, and security. The curriculum then explores different data models such as relational, semistructured, vector space, and graph data models, with hands-on exercises using real datasets. Special attention is given to streaming data, which requires different approaches than static data analysis. The course introduces various Big Data Management Systems (BDMS) including key-value stores like Redis and Aerospike, semistructured data systems like AsterixDB, text management tools like Solr, and relational systems like Vertica. Throughout the modules, case studies from industries such as energy, gaming, and flight data management provide real-world context. The course culminates with a practical project designing a BDMS for a fictional online game.
Introduction to Big Data Modeling and Management
Module 1 · 2 Hours to complete
Big Data Modeling
Module 2 · 3 Hours to complete
Big Data Modeling (Part 2)
Module 3 · 1 Hours to complete
Working With Data Models
Module 4 · 1 Hours to complete
Big Data Management: The "M" in DBMS
Module 5 · 2 Hours to complete
Designing a Big Data Management System for an Online Game
Module 6 · 1 Hours to complete
Fee Structure
Instructors
Distinguished Data Science Leader and Scientific Workflow Pioneer
Dr. Ilkay Altintas serves as Chief Data Science Officer at the San Diego Supercomputer Center (SDSC) at UC San Diego, where she has established herself as a leading innovator in scientific workflows and data science since 2001. After earning her Ph.D. from the University of Amsterdam focusing on workflow-driven collaborative science, she founded the Workflows for Data Science Center of Excellence and has led numerous cross-disciplinary projects funded by NSF, DOE, NIH, and the Moore Foundation. Her contributions include co-initiating the open-source Kepler Scientific Workflow System and developing the Biomedical Big Data Training Collaborative platform. Her research impact spans scientific workflows, provenance, distributed computing, and software modeling, earning her the SDSC Pi Person of the Year award in 2014 and the IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers in 2015. As Division Director for Cyberinfrastructure Research, Education, and Development, she oversees numerous computational data science initiatives while serving as a founding faculty fellow at the Halıcıoğlu Data Science Institute and maintaining active research collaborations across multiple scientific domains
Expert in Cosmology and Scientific Computing
Andrea Zonca leads the Scientific Computing Applications group at the San Diego Supercomputer Center, combining his cosmology expertise with advanced computing skills. His academic foundation includes extensive work analyzing Cosmic Microwave Background data from the Planck Satellite during his Ph.D. and postdoctoral research. At SDSC, he has developed significant expertise in supercomputing, particularly in parallel computing with Python and C++, and maintains widely used community software packages like healpy and PySM. His current role involves leading efforts to help research groups optimize their data analysis pipelines for national supercomputers. He has also built specialized knowledge in cloud computing, particularly in deploying services on platforms like Jetstream using Kubernetes and JupyterHub. As a certified Software Carpentry instructor, he teaches essential computational skills to scientists, including automation with bash, version control with git, and Python programming. His research contributions have been significant, with his work on the healpy package becoming a crucial tool for data analysis on spherical surfaces in Python, garnering widespread use in the scientific community.
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