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πŸ“˜ Introduction: Data Cube Modeling Tutorials ​

Welcome to our comprehensive tutorial series on data cube modeling. This collection is designed to guide you step by step through the core concepts and techniques used in modeling complex analytical data structures. Whether you're building a business intelligence solution or working on an OLAP engine, a solid understanding of the data cube, catalogs, schemas, and OLAP elements is essential.

We start with the relational foundation – modeling catalogs, schemas, tables, and columns in a way that mirrors typical database systems. This layer serves as the cornerstone for everything that follows.

From there, we transition into the world of OLAP modeling: You’ll learn how to define cubes, and how to enrich them with dimensions, hierarchies, and levels that reflect your business structure and enable powerful multidimensional queries.

We strongly recommend beginning with the database tutorial, as it introduces the core data model that most of the other tutorials build upon. Before diving into advanced topics, it’s useful to revisit the introductions in each section, as they often highlight key transitions and modeling decisions.

The Tutorials in the unstructured section agre bare Mapping descriptions and example files. They are designed for advaned users who are already familiar with the basics of data cube modeling. These tutorials provide a deeper dive into specific topics, showcasing advanced techniques and best practices for creating efficient and effective data models. Feel free to add Tutorial description and structure to this Tutotials.

A recommended reading order is provided below to help you build your understanding progressively and systematically.

Database - Intro

Database - ColumnTypes

Database - SQL Expression Column

Database - Schema

Database - Table

Database - SqlView

Database - InlineTable

Cube - Minimal

Measure - Basic Aggregators

Measure - Multiple Measures

Measure - Datatypes

Measure - Formats

Measure - MeasureGroups

Measure - Bit Aggragators

Measure - Percentile Aggragator

Measure - Text Aggregator

Dimension - Introduction

Hierarchy - Query - seperate Tables, Fact and Dimension

Hierarchy - Query - 1 Table, 2 Levels

Hierarchy - Query - all in 1 Table

Hierarchy - Query - 1 Join

Hierarchy - Query - 2 Joins, 3 Levels

Hierarchy - HasAll-Level

Level - MemberProperties Intro

Cube - CalculatedMembers Intro

Kpi - Introduction

Kpi - parent ring

Schulwesen

Writeback_without_dimension

tutorial_for_writeback_with_fact_view

tutorial_for_writeback

tutorial_for_writeback_with_fact_InlineTable

Cube_with_virtual_cube_with_un_visible_reference_cube

Minimal_Virtual_Cubes_With_Measures_only

Cube_with_virtual_cube

Cube_with_virtual_cube_with_calculatedMember

Minimal_Virtual_Cubes_With_Measures

Minimal_Cube_with_Time_Dimension

Cube_with_NamedSet

Minimal_Cubes_With_MeasureExpression

Minimal_Single_Hierarchy_Hidden_Members_with_IfParentsName

Minimal_Single_Hierarchy_Hidden_Members_with_IfBlankName

Cube_with_virtual_cube_with_kpi

Minimal_Cubes_With_KPI_all_Properties

CubeOneMeasureInlineTable

Cube_with_share_dimension_with hierarchy_with_view_reference

Cube_with_dimension_with hierarchy_with_inner_table

Minimal_Cube_with_DrillThroughAction

Minimal_Cube_with_cube_dimension_smallInt_boolean_level

Minimal_Cube_with_cube_dimension_level_with_closure_table

Minimal_Parent_Child_Hierarchy

Minimal_Cube_with_cube_dimension_level_with_expressions

Minimal_Parent_Child_With_Closure

Minimal_Cube_with_cube_dimension_level_attribute

Minimal_Cube_with_cube_dimension_with_functional_dependency_optimizations

Minimal_Cube_With_Measures_CellFormatter

Cube_with_Aggregate_tables

Cube_with_table_reference_with_AggExclude

Cube_with_access_all_dimension

Cube_with_access_all_dimension_cube1_access_to_A_only

Cube_with_access_database_schema

Cube_with_access_all_dimension_cube1_access_only

Cube_with_role_access_all_none_custom

Released under the Eclipse Public License 2.0