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Principles of Data Management: An Introductory Course

This course covers the fundamental principles of data management, including database management systems (DBMS), data independence, efficient access, and data integrity. It also explores the history of databases, the evolving landscape of database systems, and the usage scenarios of key-value stores, row stores, column stores, and graph databases. Students will learn the basics of SQL and relational databases, as well as the importance of studying database management.

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Principles of Data Management: An Introductory Course

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  1. Principles of Data ManagementSyllabus & Intro

  2. Course website: • https://cis.temple.edu/~edragut/5516/Spr19/schedule.htm • Text Book(s) • Workload • Intended Schedule • Projects • Grading • Reading List

  3. Here Are Some DB Questions

  4. How about This Question?

  5. Wait. What?I am suppose to know that already?

  6. So, What Is This Class About Then?

  7. Having Some Fun..

  8. What Is a DBMS? • A very large, integrated collection of data. • Models real-world enterprise. • Entities (e.g., students, courses) • Relationships (e.g., Madonna is taking CS5516) • A Database Management System (DBMS)is a software package designed to store and manage data.

  9. Files vs. DBMS • Application must stage large datasets between main memory and secondary storage (e.g., buffering, page-oriented access, 32-bit addressing, etc.) • Special code for different queries • Must protect data from inconsistency due to multiple concurrent users • Crash recovery • Security and access control

  10. Why Use a DBMS? • Data independence and efficient access. • Reduced application development time. • Data integrity and security. • Uniform data administration. • Concurrent access, recovery from crashes.

  11. ? Why Study Databases?? • Shift from computation to information • at the “low end”: scramble to webspace (a mess!) • at the “high end”: scientific applications • Datasets increasing in diversity and volume. • Digital libraries, interactive video, Human Genome project, EOS project • ... need for DBMS exploding • DBMS encompasses most of CS • OS, languages, theory, AI, multimedia, logic

  12. A Brief DB History • Early 1970s • Many database systems • Incompatible, exposing many implementation details • Then Ted Codd came along • Relational model • and… • Donald D. Chamberlin and Raymond F. Boyce • Structured Query Language (SQL) • Implementation differences became irrelevant • A few major DB systems dominated the market

  13. A Brief DB History • James ("Jim") Nicholas Gray • Transactions and More Transactions (ACID) • System R • Michael Stonebraker • INGRES, Postgres INGRES and System R together helped to turn relational systems from a laboratory curiosity into the default choice for even the most demanding data processing applications.

  14. Then Web 2.0 & 3.0, Big Data Happen • What do you think happen? • Semi-structured data happen. • A lot of it and in many forms…

  15. Some Facts about Web x.0 and Big Data • Twitter: 255 million monthly active users and 500 million Tweets are sent per day, • Facebook: over 1 billion monthly users and faces 3 million message per 20 minute • Instagram: 200 Million Monthly Active Users and 1.6 Billion Likes and 60 Million Photos shared every day

  16. Database Systems Landscape Nowadays

  17. Somebody, Please, Bring Some Order to This Madness

  18. NoSQL Databases Somebody, Please, Bring Some Order to This Madness – Cont’d

  19. Different Interfaces Different hardware support Different application support Lack of Uniformity Somebody, Please, Bring Some Order to This Madness Source: http://www.infoq.com/articles/State-of-NoSQL

  20. And 2016…

  21. Database Evolution Timeline

  22. Additional Resources • Tutorial by C. Mohan, An In-Depth Look at Modern Database Systems • https://docs.google.com/file/d/0B7lNUaak0bK1encwYnBVUWZSWjA/edit

  23. Tables or Relations Relational Data

  24. Relational Database: Schemas

  25. Relational Database: Query Language • SQL - Structured Query Language • a declarative language designed for managing data held in a relational database management system • Tell what you want and from where • Do not tell: how to get the data

  26. Key-Value Store • Implemented as an associative array, map, symbol table, or dictionary abstract data type composed of a collection of (key, value) pairs such that each possible key appears at most once in the collection. • A simple put/get interface • Great properties: scalability, availability, reliability

  27. Key-Value Store Usage Scenarios • Increasingly popular within data centers and in P2P amazon.com LinkedIn Facebook Vuze uTorrent P2P Data center Voldemort Dynamo Cassandra Vuze DHT uTorrent DHT

  28. Row Store and Column Store • In row store data are stored in the disk tuple by tuple. • Where in column store data are stored in the disk column by column. • Column-stores are more I/O efficient for read-only queries as they read only those attributes which are accessed by a query. Source: Column-Oriented Database Systems, VLDB 2009. Tutorial; S. Harizopoulos, D. Abadi, P. Boncz

  29. So column stores are suitable for read-mostly, read-intensive, large data repositories Row Store and Column Store

  30. Graph Databases Ecological Network Social Network Biological Network Chemical Network Web Graph Program Flow

  31. Graph Databases: Query • Find all the restaurants my friends (in Facebook) like

  32. So, Why Study Relational DBs? • Jack Clark, The Register, 30 August 2013: “The tech world is turning back toward SQL, bringing to a close a possibly misspent half-decade in which startups courted developers with promises of infinite scalability and the finest imitation-Google tools available, and companies found themselves exposed to unstable data and poor guarantees.” • Google Spanner paper, October 2012: “We believe it is better to have application programmers deal with performance problems due to overuse of transactions as bottlenecks arise, rather than always coding around the lack of transactions.” • Sean Doherty in Wired, September 2013: “But don’t become unnecessarily distracted by the shiny, new-fangled, NoSQL red buttons just yet. Relational databases may not be hot or sexy but for your important data there is no substitute.”

  33. And, The Key Reason of All • Gartner estimates RDBMS market at $26B with about 9% annual growth, whereas Market Research Media Ltd expects NoSQL market to be at $3.5B by 2018. • Source: C Mohan’s tutorial • Can someone check it!

  34. Databases make these folks happy ... • End users and DBMS vendors • DB application programmers • E.g., smart webmasters • Database administrator (DBA) • Designs logical /physical schemas • Handles security and authorization • Data availability, crash recovery • Database tuning as needs evolve Must understand how a DBMS works!

  35. Summary • DBMS used to maintain, query large datasets. • Benefits include recovery from system crashes, concurrent access, quick application development, data integrity and security. • Levels of abstraction give data independence. • A DBMS typically has a layered architecture. • DBAs hold responsible jobs and are well-paid!  • DBMS R&D is one of the broadest, most exciting areas in CS.

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