Course Organization
Vlastislav Dohnal
PA220: Database systems for data analytics
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Course Overview
• Overview of data warehousing
• Planning a data warehouse
• Modelling your data for BI
• Querying your data
• Tuning and physical optimization
• ETL – getting your data into a data warehouse
• Case Study
• Novel technology (e.g., for real-time BI) – Apache Hive, Pig
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Course Organization
• Lectures:
• slides and video commentary – available for studying at anytime
• Assignments:
• 5 home assignments with optional online consultation
• at the time scheduled for lecture (Tuesday at 12am) – see the schedule in IS
• at least 4 must be submitted; grading of each will be announced later
• Exam:
• oral exam – 2-3 tasks to solve/discuss instantly
• Evaluation:
• composite of assignment result (weight 1/3) and oral exam
(weight 2/3)
• for passing – at least 50 % of total points
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Practice
• Postgresql
• www.postgresql.org
• may use you own installation or student’s DB@FI
https://www.fi.muni.cz/tech/unix/databases.html
• Microsoft Power BI
• https://powerbi.microsoft.com/en-us/desktop/
• install locally on your computer
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Sources
• Textbooks:
• Ralph Kimball et al.: The Data Warehouse Lifecycle Toolkit. Wiley Publishing, Inc.,
2008.
• William Inmon: Building the Data Warehouse. John Wiley and Sons, 1996.
• Christian Jensen et al.: Multidimensional Databases and Data Warehousing. Synthesis
Lectures on Data Management. Morgan & Claypool, 2010.
• Journal paper:
• Mark Levene and George Loizou: Why is the Snowflake Schema a Good Data
Warehouse Design? Information Systems, Elsevier, 2003.
• Courses:
• Data Warehousing – Jens Teubner, TU Dortmund
• Data Warehousing and Data Mining – Johann Gamper and
Mouna Kacimi, Univ. Bolzano
• Data Warehousing and Data Mining Techniques – Wolf-Tilo Balke,
TU Braunschweig
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