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teaching:infoh419 [2018/09/01 10:37] ezimanyi [Software] |
teaching:infoh419 [2022/10/04 22:15] ezimanyi [Groups of the current year] |
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| * [[http://cs.ulb.ac.be/members/esteban/|Esteban Zimányi]] | * [[http://cs.ulb.ac.be/members/esteban/|Esteban Zimányi]] | ||
| * <ezimanyi@ulb.ac.be> | * <ezimanyi@ulb.ac.be> | ||
| - | * Room SU A 4.115 | ||
| - | |||
| ===== Volume ===== | ===== Volume ===== | ||
| Line 18: | Line 16: | ||
| * Master in Computer Sciences [INFO] | * Master in Computer Sciences [INFO] | ||
| * Erasmus Mundus Master in Big Data Management and Analytics (BDMA) | * Erasmus Mundus Master in Big Data Management and Analytics (BDMA) | ||
| + | |||
| + | ===== Schedule ===== | ||
| + | |||
| + | The course is given during the first semester | ||
| + | * Lectures on Mondays from 10 am to 12 pm at the room S.K.3.401 | ||
| + | * Exercises on Tuesdays from 2 pm to 4 pm at the room S.P4.1.17 | ||
| ===== Grading ===== | ===== Grading ===== | ||
| * Group project (30%) | * Group project (30%) | ||
| * Written exam (70%) | * Written exam (70%) | ||
| - | * the exam is open book; notes and books can be used. Laptops and other electronic devices are not allowed. | + | * the exam is open book; notes and books can be used. Laptops and other electronic devices are **not** allowed. Please prepare your paper material in advance, not the day before the examination to avoid any printing problems. |
| ===== Course Summary ===== | ===== Course Summary ===== | ||
| Relational and object-oriented databases are mainly suited for operational settings in which there are many small transactions querying and writing to the database. Consistency of the database (in the presence of potentially conflicting transactions) is of utmost importance. Much different is the situation in analytical processing where historical data is analyzed and aggregated in many different ways. Such queries differ significantly from the typical transactional queries in the relational model: | Relational and object-oriented databases are mainly suited for operational settings in which there are many small transactions querying and writing to the database. Consistency of the database (in the presence of potentially conflicting transactions) is of utmost importance. Much different is the situation in analytical processing where historical data is analyzed and aggregated in many different ways. Such queries differ significantly from the typical transactional queries in the relational model: | ||
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| * Analytical queries involve aggregations (min, max, avg, ...) over large subgroups of the data; | * Analytical queries involve aggregations (min, max, avg, ...) over large subgroups of the data; | ||
| * When analyzing data it is convenient to see it as multi-dimensional. | * When analyzing data it is convenient to see it as multi-dimensional. | ||
| - | \\ | + | |
| For these reasons, data to be analyzed is typically collected into a data warehouse with Online Analytical Processing support. Online here refers to the fact that the answers to the queries should not take too long to be computed. Collecting the data is often referred to as Extract-Transform-Load (ELT). The data in the data warehouse needs to be organized in a way to enable the analytical queries to be executed efficiently. For the relational model star and snowflake schemes are popular designs. Next to OLAP on top of a relational database (ROLAP), also native OLAP solutions based on multidimensional structures (MOLAP) exist. In order to further improve query answering efficiency, some query results can already be materialized in the database, and new indexing techniques have been developped. | For these reasons, data to be analyzed is typically collected into a data warehouse with Online Analytical Processing support. Online here refers to the fact that the answers to the queries should not take too long to be computed. Collecting the data is often referred to as Extract-Transform-Load (ELT). The data in the data warehouse needs to be organized in a way to enable the analytical queries to be executed efficiently. For the relational model star and snowflake schemes are popular designs. Next to OLAP on top of a relational database (ROLAP), also native OLAP solutions based on multidimensional structures (MOLAP) exist. In order to further improve query answering efficiency, some query results can already be materialized in the database, and new indexing techniques have been developped. | ||
| - | In the course, the main concepts of multidimensional databases will be covered and illustrated using the SQL Server tools. Complimentary to the course, IBM and Teradata will give invited lectures. | + | In the course, the main concepts of multidimensional databases will be covered and illustrated using the SQL Server tools. |
| ===== Books ===== | ===== Books ===== | ||
| - | * [[https://www.springer.com/9783642546549|Data Warehouse Systems: Design and Implementation]] by Alejandro A. Vaisman and Esteban Zimányi. Springer, 2014. | + | * [[https://link.springer.com/978-3-662-65167-4|Data Warehouse Systems: Design and Implementation]], second edition, Alejandro A. Vaisman and Esteban Zimányi. Springer, 2022. |
| * [[http://www.morganclaypool.com/doi/abs/10.2200/s00299ed1v01y201009dtm009|Multidimensional Databases and Data Warehousing]] by Cristian S. Jensen, Torben Bach Pedersen, and Christian Thomsen. Morgan & Claypool Publishers. | * [[http://www.morganclaypool.com/doi/abs/10.2200/s00299ed1v01y201009dtm009|Multidimensional Databases and Data Warehousing]] by Cristian S. Jensen, Torben Bach Pedersen, and Christian Thomsen. Morgan & Claypool Publishers. | ||
| * [[http://www.mcgraw-hill.co.uk/html/0071610391.html|Data Warehouse Design: Modern Principles and Methodologies]] by Matteo Golfarelli and Stefano Rizzi. McGraw-Hill, 2009 | * [[http://www.mcgraw-hill.co.uk/html/0071610391.html|Data Warehouse Design: Modern Principles and Methodologies]] by Matteo Golfarelli and Stefano Rizzi. McGraw-Hill, 2009 | ||
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| * {{teaching:infoh419:dw00-refresher.pdf|Refresher Databases}} | * {{teaching:infoh419:dw00-refresher.pdf|Refresher Databases}} | ||
| * {{teaching:infoh419:dw01-introduction.pdf|Introduction}} | * {{teaching:infoh419:dw01-introduction.pdf|Introduction}} | ||
| - | * {{teaching:infoh419:dw02-cubes.pdf|Cubes}} | + | * {{teaching:infoh419:database_explosion_report.pdf|Database explosion report}} |
| - | * {{teaching:infoh419:dw03-dfm.pdf|Dimension Fact Model}} | + | * {{teaching:infoh419:database_explosion.pdf|Database explosion}} |
| - | * {{teaching:infoh419:dw04-logicalmodel.pdf|Logical Model}} | + | * {{teaching:infoh419:dw02-dfm.pdf|Dimension Fact Model}} |
| - | * {{teaching:infoh419:dw05-dimensionchanges.pdf|Dimension Changes}} | + | * {{teaching:infoh419:dw03-logicalmodel.pdf|Logical Model}} |
| - | * {{teaching:infoh419:dw06-etl.pdf|ETL}} | + | * {{teaching:infoh419:dw04-dimensionchanges.pdf|Dimension Changes}} |
| - | * {{teaching:infoh419:dw07-viewmaterialization.pdf|View Materialization}} | + | * {{teaching:infoh419:dw05-etl.pdf|ETL}} |
| - | * {{teaching:infoh419:dw08-indexing.pdf|Indexing}} | + | * {{teaching:infoh419:dw06-viewmaterialization.pdf|View Materialization}} |
| - | * {{teaching:infoh419:dw09-aggregatecomputation.pdf|Aggregate Computation}} | + | * {{teaching:infoh419:dw07-indexing.pdf|Indexing}} |
| - | * {{teaching:infoh419:dw10-conclusion.pdf|Conclusion}} | + | * {{teaching:infoh419:dw08-aggregatecomputation.pdf|Aggregate Computation}} |
| + | * {{teaching:infoh419:dw09-conclusion.pdf|Conclusion}} | ||
| Line 73: | Line 78: | ||
| ===== Software ===== | ===== Software ===== | ||
| - | All software used in the course is available in the computer labs. Students who wish a personal copy of the software on their own computers, can get free copies of the software. Succinct instructions to acquire the software have been included below; in case additional help is required you can contact the sysadmin of our group: Arthur Lesuisse <alesuiss@ulb.ac.be> | + | All software used in the course is available in the computer labs. Students who wish a personal copy of the software on their own computers, can get free copies of the software. Succinct instructions to acquire the software have been included below; in case additional help is required you can contact the sysadmin of the department: Robin Choquet <Robin.Choquet@ulb.be> |
| * MS SQL Server Tools: can be downloaded for free from http://www.academicshop.be/msdnaa/ Register on this page with your ULB email address, and 'order' the free msdnaa. After verification you receive login credentials to download quite a few software packages for free. Select the SQL Server 2014 Enterprise edition. | * MS SQL Server Tools: can be downloaded for free from http://www.academicshop.be/msdnaa/ Register on this page with your ULB email address, and 'order' the free msdnaa. After verification you receive login credentials to download quite a few software packages for free. Select the SQL Server 2014 Enterprise edition. | ||
| - | * Indyco Builder can be downloaded from http://www.indyco.com/ . License keys for all students will be added soon. | ||
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| * [[teaching:infoh419:TP|Exercices Web page]] | * [[teaching:infoh419:TP|Exercices Web page]] | ||
| - | ===== Group assignment ===== | + | ===== Group Project ===== |
| + | |||
| + | [[http://www.tpc.org|TPC]] is a non-profit corporation that defines transaction processing and database benchmarks and disseminates objective, verifiable TPC performance data to the industry. Regarding data warehouses, two TPC benchmarks are relevant: | ||
| + | * [[http://www.tpc.org/tpcds/|TPC-DS]], the Decision Support Benchmark, which models the decision support functions of a retail product supplier. | ||
| + | * [[http://www.tpc.org/tpcdi/|TPC-DI]], the Data Integration Support Benchmark, which models a typical ETL process that loads a data warehouse. | ||
| + | |||
| + | The project of the course consist of 2 parts: | ||
| + | * Part I: Implement the TPC-DS benchmark (deadline 1/11/2022) | ||
| + | * Part II: Implement the TPC-DI benchmark (deadline 24/12/2022) | ||
| + | You have free choice to use the tools on which the two benchmarks will be implemented. For example, the TPC-DS benchmark could be implemented on SQL Server Analysis Services, Pentaho Analysis Services (aka Mondrian), etc. Similarly, the TPC-DI benchmark could be implemented on SQL Server Integration Services, Pentaho Data Integration, Talend Data Studio, SQL scripts, etc., which then load the data warehouse on a DBMS such as SQL Server, Oracle, PostgreSQL, etc. | ||
| + | |||
| + | Furthermore, both benchmarks must be implemented with several scale factors, which determine the size of the resulting data warehouse. You DO NOT need to use the scale factors mentioned in the TPC requirements. The pedagogical objectives aimed at is that you learn how to properly perform a benchmark. Therefore, you need to estimate the biggest scale factor that you can put on your own computer: this will be your reference scale factor, say 1.0, and then you will need to have 3 smaller scale factors, e.g., at 0.1, 0.2, and 0.5 of the full size in order to see the evolution of the performance. | ||
| + | |||
| + | The project is carried out in groups of 3-4 persons, which will be the same for the two parts. Before you can submit part I of the project, you will have to register in a group. For this, please send an email to the lecturer with the information about your group by 1/10/2022 at the latest. The submission deadlines for parts I and II are strict. | ||
| + | |||
| + | The deliverables expected for each part of the project are the following: | ||
| + | * A report in pdf explaining the essential aspects of your implementation, and | ||
| + | * A zip file containing the code of your implementation, with all necessary instructions to be able to replicate your implementation by the lecturer in standard computing infrastructure. | ||
| - | The assignment is carried out in groups of 3 to 4 people. Before you can submit assignment part I, you will have to register in a group. The link to register a group is included below. Please to select your group before or on 25/10/2018. | + | The project evaluation will count for 30% of your total grade. This may seem undervalued, however, putting effort in the project will definitely help you in achieving a better understanding of the course material which will result in a better score in the paper exam which amounts for 70% of the grade. |
| - | The assignment consist of 2 parts: | + | ===== Tools of the previous year ===== |
| - | * Part I: Create a conceptual model and translate to a logical schema (deadline 15/11/2018) | + | SQL Server, PostgreSQL, mySQL, Oracle, SQLite, mariadb, Spark SQL, DB2/Airflow, Microsoft Azure SQL, Citus, AWS Aurora, Google BigQuery, Impala |
| - | * Part II: (deadline 20/12/2018) | + | |
| - | * Creating ETL scripts for updating the database in SSIS, | + | |
| - | * Predicting how the size of the data warehouse will grow over time, | + | |
| - | * Deploy a data cube on top of the data warehouse and create a report. | + | |
| - | Assignment part I will be available on 25/10. For the next parts, assignment II will become available right after the submission deadline of assignment part I. The submission deadlines for parts I and II are strict. | + | ===== Groups of the current year ===== |
| - | The assignment evaluation will count for 30% of your total grade. This may seem undervalued, however, putting effort in the assignment will definitely help you in achieving a better understanding of the course material which will result in a better score in the paper exam which amounts for 70% of the grade. | + | * Spark SQL: Luis Alfredo Leon, Satria Bagus Wicaksono, Jezuela Gega, Isabella Forero |
| + | * MySQL: Ali AbuSaleh, Liliia Aliakberova, Muhammad Rizwan Khalid, Mariana Mayorga Llano | ||
| + | * PostgreSQL: Mir Wise Khan, Rishika Gupta, Ahmad, Chidiebere Ogbuchi | ||
| + | * Oracle: Sayyor Yusupov, Nikola Ivanović, Bogdana Živković, Jose Antonio Lorencio Abril | ||
| + | * MariaDB: Prashant Gupta, Abd Alrhman Abu Sbeit, Maren, TBD. | ||
| + | * Citus: Manar El Amrani, Maxime Renversez, Alexandre Chapelle, Nicolas Dardenne | ||
| + | * Google BigQuery: Koumudi Ganepola, Adina Bondoc, Zyad Alazazi, Alaa Almutawa | ||
| + | * SQL Server: Arina Gepalova, Tianheng Zhou, You Xu, Marie Giot | ||
| + | * Microsoft Azure SQL: Evguéniy Starygin, Gauthier Roger France, Mathieu Pardon | ||
| ===== Examinations from Previous Years ===== | ===== Examinations from Previous Years ===== | ||