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Data Warehousing

GATE DA pairs databases with data warehousing. These lessons cover how data is cleaned and transformed before analysis, how a warehouse is modelled with star and snowflake schemas, and how OLAP operations work, with SQL that runs on a real SQLite database when the site is built.

Start the first lesson

1Data Preparation

  1. Data Types and Data PreprocessingTypes of attributes (nominal, ordinal, interval, ratio; discrete and continuous), data cleaning (missing values, noise and outliers), and data transformation - min-max, z-score and decimal-scaling normalization, discretization by binning, sampling and data reduction and compression - for GATE DA.

2Data Warehouse Modelling

  1. Data Warehouse Modelling - Star, Snowflake and Fact Constellation SchemasWhat a data warehouse is, OLTP versus OLAP, the ETL process, the multidimensional model with fact tables, dimensions and measures, star, snowflake and fact constellation schemas, concept hierarchies, and distributive, algebraic and holistic measures, for GATE DA.
  2. The Data Cube and OLAP OperationsCuboids and the lattice of the data cube, counting cuboids with and without concept hierarchies, the OLAP operations roll-up, drill-down, slice, dice and pivot, cube materialization, and ROLAP, MOLAP and HOLAP servers, with SQL examples for GATE DA.

3GATE Practice

  1. Data Warehousing GATE Practice SetMixed GATE DA style questions on attribute types, normalization, discretization, sampling, warehouse schemas, measures, cuboids and OLAP operations, with answers and explanations.