analytics databases: unraveling the secrets of sisense elasticube design
TRANSCRIPT
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Unraveling the Secrets of Elasticube DesignInbar Rodan
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● How many product units did we sell in total?
● How many employees are there per region?
● How many units did we sell in each product category?
● What Percentage of whole food Category are grain products?
Foodies Goodies
Sales Dashboard Creation
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Business Question
Identify NeedsWhat Percentage of whole food Ctegory
are grain products?
KPI KPI KPI
Fact Dim Fact Dim Fact Dim
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Import Data ScopeFocus Only on whole food
Dim Table
Dim Table
Dim Table
Dim TableDim Table
Dim Table
Dim Table
Dim Table
Transactions
Dim Table
Dim Table
Dim Table
Dim Table
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Transform to a Centralized StructureTransform into product categroies
Dim TableDim Table
Dim Table
Fact Table
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Connect Unique DimensionsRepresente to each category
Dim Table
Dim Table
Dim Table
Dim Table Dim TableFact Table
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Validate the DataSo.. What is the grain food % ?
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Validate
Best Practices Overview
Identify Import Model Connect
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Excellent MistakesAND SECRETS
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● How many product units did we sell in total?
● How many employees are there per region?
● How many units did we sell in each product category?
● What Percentage of whole food Category are grain products?
Foodies Goodies
Sales Dashboard Creation
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Dim Table
Dim Table
Dim TableDim Table
Dim Table
Excellent Mistake #1“ Import all relationships and tables,
as in data warehouse “
Dim Table
Dim Table
Fact Table
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Excellent Mistake #1Data Warehouse Implications
● Incorrect Results Query takes alternative path
● Long Import Times
1. Simpler Relationship2. Shorter Route3. Random
Path Preference
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● Include only relevant tables, fields, relationships
● Consolidate by subject
Excellent Secret #1Solving Cycles
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Excellent Mistake #2“ Let the Many-to-Many be! ”
Dim Table
Dim Table
Dim Table
Fact Table
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● Incorrect Results How many units were sold per product?
● Performance Load Time Complexity of O(n2)
Excellent Mistake #2MtM Implications
Supplier Product ID
Pasta Buttini s.r.l. AA1
Tokyo Traders AA1
= 16Product ID Quantity
AA1 3
AA1 5
Product ID Quantity
AA1 3
AA1 5
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Consolidate When tables data share granularity
Bridge When tables data does not share granularity, query from bridge table
Excellent Secret #2Solving MtM
OrdersSuppliers Per
Product
TableTable
Products
Suppliers/Orders
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Excellent Secret #2Tracking MtM risks
1) MtM risk table in the Elasticube reflect the uniqueness of each table that has to be unique
2) MtM risk Widget
3) Add the KPI to Pulse
4) Let Pulse monitor your EC proactively
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● Incorrect Results something is just wrong
● Multiple Dim Fields same dim in different tables
● Unclear Naming Convention of cubes, tables, fields
Excellent Mistake #3“ Once I finished modeling the
elasticube, it is done ”
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● Verify Data
● Set duplicate fields as Invisible
● Pick Intuitive Naming Convention
Excellent Secret #3Solving Unfriendly Cube
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Best Practices Summary
ValidateIdentify Import Model Connect
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Questions?
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Thank You!
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Unraveling the Secrets of Elasticube DesignInbar Rodan