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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)
Aktifitas Pembelajaran
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Pertemuan 1 Konsep-konsep dasar data warehouse. -
Pertemuan 2 Konsep dasar basis data. -
Pertemuan 3 Karakteristik dan struktur data warehouse. -
Pertemuan 4 Jenis -jenis arsitektur data warehouse. -
Pertemuan 5 Mengenal beberapa konsep teknologi yang digunakan di dalam data warehouse. -
Pertemuan 6 Konsep multidimensional modeling, hypercube, konseptual modeling, dan logical data. -
Pertemuan 7 Siklus pengembangan projek dan analisis data di dalam data warehouse. -
Pertemuan 8 Ujian Tengah Semester -
Pertemuan 9 Konsep dasar data warehouse terdistribusi. -
Pertemuan 10 Konsep dasar komputasi paralel. -
Pertemuan 11 Komputasi paralel pada data warehouse. -
Pertemuan 12 Waktu nyata data warehouse untuk aplikasi data streaming. -
Pertemuan 13 Membuat dasboard yang memvisualisasikan data di dalam warehouse. -
Pertemuan 14 Merancang sistem data warehouse untuk menyelesaikan permasalahan-permasalahan lingkup industri. -
Pertemuan 15 Merancang sistem data warehouse untuk menyelesaikan permasalahan-permasalahan lingkup industri. -
Pertemuan 16 Ujian Akhir Semester
References
- Inmon, W.H., 2005. Building the data warehouse. John wiley & sons.
- 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.
- Wolohan, J., 2020. Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code. Simon and Schuster.
- Taniar, D., Leung, C.H., Rahayu, W. and Goel, S., 2008. High-performance parallel database processing and grid databases. John Wiley & Sons.
- 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.
- Scott, D., Gamov, V. and Klein, D., 2022. Kafka in Action. Simon and Schuster.
- Kumar, M. and Singh, C., 2017. Building Data Streaming Applications with Apache Kafka. Packt Publishing Ltd.
Lecturer
Difficult Things About Education.
$75$10
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