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Lecture
Learning Models over Relational Databases
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Related lectures (32)
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Data Modeling: Concepts and Applications
Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.
Semantic Modelling: Tabular Data and RDF
Introduces semantic modelling through tabular data and RDF, covering relational databases, schema migration, future-proof schemata, SPARQL querying, and metaknowledge limitations.
Data Wrangling with Hadoop: Storage Formats and Hive
Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.
SQL Client-Server Architecture + IDE
Covers the architecture of a typical DBMS and SQL interfaces like SQL Developer and DataGrip.
Handling Data: Intro to Pandas
Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.
Entity-Relationship Model
Introduces the Entity-Relationship model for designing databases with a focus on conceptual and relational aspects.
Query Processing: Relational Operations and Models
Discusses query processing models and relational operations, focusing on selections, projections, and joins in data-intensive systems.
Query Processing with Relational Operations
Covers query processing with relational operations, including query optimization and different join algorithms.
File Systems: Organization and Indexing Techniques
Discusses file systems, focusing on data organization, indexing techniques, and their impact on database performance.
Data Wrangling with Hadoop
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
