Data W _ Bigdata1.pptx - CS7079 Data Warehousing and Big Data Narayan Chalise Senior Software Engineer/Lecturer [email protected] • Module title

Data W _ Bigdata1.pptx - CS7079 Data Warehousing and Big...

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CS7079 Data Warehousing and Big Data
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Narayan Chalise Senior Software Engineer/Lecturer [email protected]
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Module title: Data Warehousing and Big Data Module level: Masters Credit rating for module: 20
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Coursework: 60% Examination: 40%
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Module summary Database Database Management system Enterprise level database development Data models Internal data representation System architecture
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Data Processing using DBMS Data Dictionary Data definition and data manipulation using SQL Data warehousing Data transformation Data analysis
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Real world data warehouse application Big data management based on Apache Hadoop platform (HDFS) Process and manage massive and complex data Relational database
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Bibliography Connolly, T.M., Begg, C.E., (2009) Database Systems – A Practical Approach to Design, Implementation and Management, 5thdition, Addison Wesley; ISBN10: 0321523067 Ponniah, P., (2001) Data warehousing fundamentals, Joh Wiley & Sons; ISBN: 0471412546 [CORE] Rainardi, V., (2007) Building a data warehouse with examples in SQL server, Apress; ISBN13: 9781590599310 White, T., (2015) Hadoop: The definite Guide. Sebastopol: O’Reilly & Associates. ISBN10: 1491901632 [CORE] Ryza, S., Laserson, U., Owen, S., and Wills, J., (2015) – Advanced analytics with Spark. ISBN10: 1491912766
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Syllabus 1. Introduction of database models and system architecture 2. Data processing using DBMS; data definition and manipulation using SQL 3. Introduction of Data warehouse concepts and analytical foundations. 4. Data warehouse development; system architecture and data transformation; investigation of techniques for distributing and mining data
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5. Big Data technology stack, emerging trends and use cases where Big Data outperforms traditional data warehouse. 6. Big Data technology stack including open source tools like Hadoop, HDFS, MapReduce, Yarn, Spark, Storm, Hive for massively parallel ondisk data processing.
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7. An overview of batch and realtime data ingestion patterns using Apache Flume and Kafka, data transformation techniques and generation of summary statistics using Apache Spark 8. Data Analytics on Hadoop platform using Apache Spark for data analysis on HDFS
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Your responsibilities Understand the lecture slides and do assignments Uphold academic honesty in completing your assignments, projects, and exams.
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Plagiarism, cheating , and other forms of academic dishonesty will not be tolerated. Class discipline Mobile Switch OFF
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No written assignment solutions or reports will be accepted late (extreme emergencies will be dealt with on a case by case basis). Attendance
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Introduction
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Data vs. Information Data: Raw facts; building blocks of information Unprocessed information Information: Data processed to reveal meaning
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Accurate, relevant, and timely information is key to good decision making Good decision making is the key to survival in a global environment
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Database and the DBMS Database—shared, integrated computer structure that stores: End user data (raw facts) Metadata (data about data)
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