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Course Description
Data Warehouse and Distributed Computing is a course that discusses the process of planning, collecting and transforming data to support data-driven decision making. Apart from that, this course discusses parallel computing and its application to data warehouses. Through this course, students are expected to have the ability to design data architecture to solve problems that support and are related to business strategy.
Program Objectives (PO)
- Mahasiswa mampu memahami fundamental Data Warehouse
- Mahasiswa dapat memahami konteks aplikasi Data Warehouse
- Mahasiswa memiliki kemampuan dasar operasional Data Warehouse
- Mahasiswa mampu memahami fundamental Komputasi Terdistribusi
- Mahasiswa dapat memahami prinsip integrasi ETL Datawarehouse dengan Komputasi Terdistribusi
- Mahasiswa memiliki kemampuan dasar implementasi ETL dengan pendekatan terdistribusi
Aktifitas Pembelajaran
-
Pertemuan 1 Konsep-konsep dasar data warehouse. -
Pertemuan 2 Konsep-konsep dasar data warehouse. -
Pertemuan 3 Desain data warehouse. -
Pertemuan 4 Konsep dan desain data warehouse. -
Pertemuan 5 Melakukan restoring database dengan Docker. -
Pertemuan 6 Operasi-operasi Online Analytical Processing (OLAP) -
Pertemuan 7 Multidimensional expressions (MDX) di dalam Atoti. -
Pertemuan 8 Ujian Tengah Semester -
Pertemuan 9 Multidimensional expressions (MDX) di dalam Atoti dengan beberapa datasource. -
Pertemuan 10 Pengantar komputasi terdistribusi. -
Pertemuan 11 Dan menerapkan message queue di dalam sistem terdistribusi. -
Pertemuan 12 Dan menerapkan message queue di dalam sistem terdistribusi. -
Pertemuan 13 Membuat proyek akhir data warehouse. -
Pertemuan 14 Membuat proyek akhir data warehouse. -
Pertemuan 15 Membuat proyek akhir data warehouse. -
Pertemuan 16 Ujian Akhir Semester
References
- Vaisman, A. and Zimányi, E., 2014. Data warehouse systems. Data-Centric Systems and Applications. Springer.
- Beaulieu, A., 2020. Learning SQL: Generate, manipulate, and retrieve data. O'Reilly Media.
- Bani, F.C.D. and Girsang, A.S., 2018. Implementation of database massively parallel processing system to build scalability on process data warehouse. Procedia Computer Science, 135, pp.68-79.
- Datta, A., VanderMeer, D., Ramamritham, K. and Moon, B., 1998. Applying parallel processing techniques in data warehousing and OLAP.
Lecturer
Difficult Things About Education.
$75$10
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