Database Management SystemsOLAP Operations

OLAP Operations: Multidimensional Data Analysis

LevelIntermediate

Duration75 mins

TopicOLAP Operations

3 / 5

Slice and Dice: Focused Multidimensional Analysis

Cutting Through the Data Cube

Imagine a three-dimensional cube of sales data: Time on one axis, Products on another, Geography on the third. The complete cube contains every combination of every time period, every product, and every location. But rarely do analysts need the entire cube. More often, they need focused views:

"Show me all product sales, but only for Q3 2024."

"Compare Electronics and Home Goods categories across regions, but exclude online sales."

"Analyze West region performance across all products and time periods."

These focused analyses require slice and dice operations—techniques for extracting meaningful subsets from the multidimensional data space. While roll-up and drill-down navigate along hierarchies, slice and dice filter the dimensional space, creating focused subcubes for detailed examination.

What You Will Learn

By the end of this page, you will understand the distinction between slice and dice operations, how they manipulate the data cube to create focused views, their SQL implementation, and how to combine these operations with roll-up and drill-down for comprehensive analytical workflows.

The Slice Operation: Cutting Through One Dimension

A slice operation creates a subcube by selecting a single value along one dimension, effectively reducing the cube's dimensionality by one. Think of it as taking a flat cross-section through the data cube.

Formal Definition:

Given an n-dimensional cube C with dimensions (D₁, D₂, ..., Dₙ), a slice on dimension Dᵢ with value v produces an (n-1)-dimensional subcube where Dᵢ is fixed at value v.

Visual Metaphor:

Imagine a physical block of ice representing a 3D data cube [Time × Product × Region]. A slice is literally slicing through the block with a knife:

Slice on Time = 'Q3' → You get a 2D slab showing [Product × Region] for Q3 only
Slice on Region = 'West' → You get a 2D slab showing [Time × Product] for West only
Slice on Product = 'Electronics' → You get a 2D slab showing [Time × Region] for Electronics only

slice_examples.sql
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-- SLICE OPERATION EXAMPLES
-- Each slice fixes one dimension to a single value
 
-- Slice on TIME dimension: Fix to Q3 2024
-- Result: 2D subcube of [Product × Region] for Q3
SELECT 
    p.category,
    s.region,
    SUM(f.sales_amount) as total_sales,
    SUM(f.quantity_sold) as units_sold
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE d.quarter = 'Q3' AND d.year = 2024  -- SLICE: Fix time to Q3 2024
GROUP BY p.category, s.region
ORDER BY s.region, total_sales DESC;
 
-- Slice on GEOGRAPHY dimension: Fix to West region
-- Result: 2D subcube of [Time × Product] for West
SELECT 
    d.quarter,
    p.category,
    SUM(f.sales_amount) as total_sales,
    AVG(f.unit_price) as avg_price
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE s.region = 'West'  -- SLICE: Fix geography to West
  AND d.year = 2024
GROUP BY d.quarter, p.category
ORDER BY d.quarter, p.category;
 
-- Slice on PRODUCT dimension: Fix to Electronics category
-- Result: 2D subcube of [Time × Region] for Electronics
SELECT 
    d.month_name,
    s.region,
    SUM(f.sales_amount) as total_sales,
    COUNT(DISTINCT f.store_key) as active_stores
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE p.category = 'Electronics'  -- SLICE: Fix product to Electronics
  AND d.year = 2024
GROUP BY d.month_name, d.month_num, s.region
ORDER BY d.month_num, s.region;

Key Characteristics of Slice:

Dimension Reduction: After slicing, the fixed dimension is no longer part of the result set—it's constant, so it doesn't need to appear in GROUP BY or output columns (though you might include it for context).
Single Value Selection: Classic slice selects exactly one member from the dimension. Selecting multiple values (e.g., Q2 and Q3) is a dice operation, not a slice.
Filtering Before Aggregation: Slice is applied in the WHERE clause, filtering fact rows before any aggregation occurs.
Hierarchy Level Flexibility: You can slice at any hierarchy level—slice by Year, Quarter, Month, or even Day.

Slice vs. Filter

In SQL terms, a slice is simply a WHERE clause that restricts one dimension to a single value. The cube terminology emphasizes the multidimensional perspective: you're not just 'filtering rows'—you're taking a cross-sectional cut through a geometric data structure. This mental model helps analysts reason about complex queries.

The Dice Operation: Selecting a Sub-Cube

A dice operation creates a subcube by selecting specific values (or ranges) along two or more dimensions. Unlike slice (which produces a lower-dimensional result), dice maintains the original dimensionality but with restricted scope on each dimension.

Formal Definition:

Given an n-dimensional cube C, a dice operation specifies selection criteria for multiple dimensions:

D₁ ∈ {v₁₁, v₁₂, ...}
D₂ ∈ {v₂₁, v₂₂, ...}
...

The result is an n-dimensional subcube containing only the specified members.

Visual Metaphor:

If slice is cutting a flat slab from the cube, dice is carving out a smaller cube from within the larger cube. You're constraining multiple edges:

Time: Only Q3 and Q4
Product: Only Electronics and Home
Region: Only West and Central

The result is still a 3D cube, just smaller: 2 quarters × 2 categories × 2 regions = 8 cells instead of potentially hundreds.

Slice Characteristics

•Fixes ONE dimension to ONE value
•Reduces dimensionality by 1
•Result is a cross-section (n-1 dims)
•Example: Region = 'West'
•Metaphor: Cutting a slab from cube

Dice Characteristics

•Constrains MULTIPLE dimensions
•Maintains dimensionality
•Result is a subcube (still n dims)
•Example: Region IN ('West','East') AND Quarter IN ('Q3','Q4')
•Metaphor: Carving smaller cube from larger

dice_examples.sql

-- DICE OPERATION: Constrain multiple dimensions simultaneously
-- Maintains all dimensions but with restricted scope
 
-- Dice: Time (Q3, Q4) × Product (Electronics, Home) × Region (West, Central)
SELECT 
    d.quarter,
    p.category,
    s.region,
    SUM(f.sales_amount) as total_sales,
    SUM(f.quantity_sold) as units_sold,
    COUNT(*) as transaction_count
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE d.year = 2024
  AND d.quarter IN ('Q3', 'Q4')              -- Dice on Time: 2 quarters
  AND p.category IN ('Electronics', 'Home')   -- Dice on Product: 2 categories
  AND s.region IN ('West', 'Central')         -- Dice on Region: 2 regions
GROUP BY d.quarter, p.category, s.region
ORDER BY d.quarter, s.region, p.category;
 
-- Result: 2×2×2 = 8 cells (compare to potentially hundreds in full cube)

Dice for Comparative Analysis

Dice is particularly powerful for side-by-side comparisons. Want to compare Q3 vs Q4 for two product categories across three regions? That's a dice creating a 2×2×3 subcube. The constrained scope makes patterns visible that would be lost in a full cube view.

Combining OLAP Operations

Real analytical workflows combine all OLAP operations fluidly. Understanding how they interact enables sophisticated analysis:

Operation Ordering:

Dice/Slice First: Restrict to the subcube of interest
Roll-up/Drill-down: Navigate hierarchy levels within that subcube
Repeat: Further slice/dice as focus narrows

Example Analytical Workflow:

Starting point: Full 3D cube [Year × Category × Region]

Slice on Year = 2024 → Now analyzing just 2024 data
Drill-down Year to Quarter → See Q1, Q2, Q3, Q4 breakdown
Dice on Quarters (Q3, Q4) and Regions (West, Central) → Focus on second-half Western performance
Drill-down Category to Brand → See brand-level detail within Electronics and Home
Slice on Q3 only → Final focus: Q3 brand performance in Western regions

combined_operations.sql
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-- COMBINED OLAP OPERATIONS
-- Analytical workflow from high-level to detailed insight
 
-- Step 1: SLICE (Year = 2024) + ROLL-UP (to Quarterly level)
SELECT d.quarter, SUM(f.sales_amount) as sales
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
WHERE d.year = 2024  -- SLICE
GROUP BY d.quarter;  -- Rolled up to Quarter
 
-- Step 2: DICE (Q3, Q4 × West, Central) - maintaining quarterly view
SELECT 
    d.quarter,
    s.region,
    SUM(f.sales_amount) as sales
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE d.year = 2024
  AND d.quarter IN ('Q3', 'Q4')       -- DICE: two quarters
  AND s.region IN ('West', 'Central') -- DICE: two regions
GROUP BY d.quarter, s.region;
 
-- Step 3: DRILL-DOWN on Product (Category → Brand) within diced subcube
SELECT 
    d.quarter,
    s.region,
    p.category,
    p.brand,                          -- DRILL-DOWN: added brand
    SUM(f.sales_amount) as sales,
    RANK() OVER (
        PARTITION BY d.quarter, s.region, p.category 
        ORDER BY SUM(f.sales_amount) DESC
    ) as brand_rank
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN store_dim s ON f.store_key = s.store_key
JOIN product_dim p ON f.product_key = p.product_key
WHERE d.year = 2024
  AND d.quarter IN ('Q3', 'Q4')
  AND s.region IN ('West', 'Central')
  AND p.category IN ('Electronics', 'Home')  -- DICE: added category constraint
GROUP BY d.quarter, s.region, p.category, p.brand;
 
-- Step 4: SLICE Q3 only, focus on top brands per category
WITH ranked_brands AS (
    SELECT 
        d.quarter,
        s.region,
        p.category,
        p.brand,
        SUM(f.sales_amount) as sales,
        RANK() OVER (
            PARTITION BY s.region, p.category 
            ORDER BY SUM(f.sales_amount) DESC
        ) as brand_rank
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN store_dim s ON f.store_key = s.store_key
    JOIN product_dim p ON f.product_key = p.product_key
    WHERE d.year = 2024
      AND d.quarter = 'Q3'                -- SLICE: only Q3 now
      AND s.region IN ('West', 'Central')
      AND p.category IN ('Electronics', 'Home')
    GROUP BY d.quarter, s.region, p.category, p.brand
)
SELECT * FROM ranked_brands 
WHERE brand_rank <= 5                     -- Top 5 brands per region+category
ORDER BY region, category, brand_rank;

OLAP Operations Summary
Operation	Action	Result	SQL Mechanism
Roll-up	Aggregate up hierarchy	Coarser granularity	GROUP BY at higher level
Drill-down	Decompose down hierarchy	Finer granularity	GROUP BY at lower level + WHERE filter
Slice	Fix one dimension	Reduce dimensionality	WHERE dim = single_value
Dice	Constrain multiple dimensions	Smaller subcube, same dimensionality	WHERE with multiple IN clauses
Pivot	Rotate dimensions	Different perspective, same data	CASE expressions or PIVOT clause

Dynamic Filtering Patterns

Real-world BI applications need dynamic slice and dice capabilities where filter values come from user selections. This requires parameterized query patterns and thoughtful handling of optional filters.

Pattern 1: Parameterized Filters

Queries accept filter values as parameters:

SELECT ...
FROM sales_fact f ...
WHERE ($region IS NULL OR s.region = $region)          -- Optional slice
  AND ($category IS NULL OR p.category = $category)     -- Optional slice
  AND ($start_date IS NULL OR d.full_date >= $start_date)
  AND ($end_date IS NULL OR d.full_date <= $end_date)

Pattern 2: Dynamic Member Lists

Handle multi-select filters where users choose multiple values:

dynamic_filters.sql

-- DYNAMIC SLICE AND DICE PATTERNS
 
-- Pattern 1: Optional filters with NULL checks
-- All parameters optional - NULL means "all values"
CREATE OR REPLACE FUNCTION get_sales_cube(
    p_year INT DEFAULT NULL,
    p_quarter TEXT DEFAULT NULL,
    p_category TEXT DEFAULT NULL,
    p_region TEXT DEFAULT NULL
) RETURNS TABLE (
    quarter TEXT,
    category TEXT,
    region TEXT,
    total_sales NUMERIC,
    units_sold BIGINT
) AS $$
BEGIN
    RETURN QUERY
    SELECT 
        d.quarter,
        p.category,
        s.region,
        SUM(f.sales_amount)::NUMERIC as total_sales,
        SUM(f.quantity_sold)::BIGINT as units_sold
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE (p_year IS NULL OR d.year = p_year)         -- Optional year slice
      AND (p_quarter IS NULL OR d.quarter = p_quarter) -- Optional quarter slice
      AND (p_category IS NULL OR p.category = p_category)
      AND (p_region IS NULL OR s.region = p_region)
    GROUP BY d.quarter, p.category, s.region;
END;
$$ LANGUAGE plpgsql;
 
-- Usage examples:
SELECT * FROM get_sales_cube(2024, NULL, NULL, 'West');  -- Year + Region slice
SELECT * FROM get_sales_cube(2024, 'Q3', 'Electronics', NULL);  -- Three slices
 
-- Pattern 2: Multi-value dice with arrays (PostgreSQL)
CREATE OR REPLACE FUNCTION get_sales_diced(
    p_years INT[] DEFAULT NULL,
    p_quarters TEXT[] DEFAULT NULL,
    p_categories TEXT[] DEFAULT NULL,
    p_regions TEXT[] DEFAULT NULL
) RETURNS TABLE (...) AS $$
BEGIN
    RETURN QUERY
    SELECT ...
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE (p_years IS NULL OR d.year = ANY(p_years))
      AND (p_quarters IS NULL OR d.quarter = ANY(p_quarters))
      AND (p_categories IS NULL OR p.category = ANY(p_categories))
      AND (p_regions IS NULL OR s.region = ANY(p_regions))
    GROUP BY d.quarter, p.category, s.region;
END;
$$ LANGUAGE plpgsql;
 
-- Usage: Dice on specific lists
SELECT * FROM get_sales_diced(
    ARRAY[2023, 2024],                    -- Two years
    ARRAY['Q3', 'Q4'],                    -- Two quarters
    ARRAY['Electronics', 'Appliances'],   -- Two categories
    ARRAY['West', 'Central', 'East']      -- Three regions
);

Performance with Dynamic Filters

Dynamic filters can lead to suboptimal query plans because the optimizer doesn't know filter values at planning time. Consider: (1) Using statement-level hints, (2) Forcing query re-planning with plan_cache_mode=force_custom_plan, (3) Creating separate optimized queries for common filter patterns, (4) Using materialized filtered views for frequent filter combinations.

Optimizing Slice and Dice Performance

Efficient slice and dice operations require thoughtful indexing, partitioning, and data organization. Here are key optimization strategies:

1. Dimension Key Indexing:

Create indexes on dimension foreign keys in fact tables for fast filtering:

-- Composite index for common slice/dice patterns
CREATE INDEX idx_sales_dims ON sales_fact(date_key, product_key, store_key);

-- Partial indexes for frequently-sliced values
CREATE INDEX idx_sales_recent ON sales_fact(product_key, store_key)
WHERE date_key >= (SELECT MIN(date_key) FROM time_dim WHERE year >= 2023);

2. Partition Pruning:

When slicing on partitioned columns (typically time), the database can skip entire partitions:

-- If sales_fact is partitioned by year-month
-- Slice on year = 2024 only scans 2024 partitions
SELECT ... WHERE d.year = 2024;  -- Prunes 2023 and earlier

Index Strategies for Slice/Dice Operations
Filter Pattern	Recommended Index	Benefit
Single dimension slice	B-tree on dimension key	Fast equality lookup
Range slice (dates)	B-tree on date column	Range scan efficiency
Multi-value dice (IN list)	B-tree on dimension key	Multiple point lookups
Low-cardinality dimension	Bitmap index (if supported)	Fast bitwise filtering
Composite slice+dice	Multi-column index	Cover all filter columns
Frequent filter combination	Partial/filtered index	Smaller, targeted index

slice_dice_optimization.sql
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-- SLICE/DICE PERFORMANCE OPTIMIZATION
 
-- 1. Bitmap indexes for low-cardinality dimensions (Oracle-style, concept)
-- In PostgreSQL, regular B-tree works; bitmap scans happen automatically
 
-- 2. Covering indexes to avoid table lookups
CREATE INDEX idx_sales_covering ON sales_fact(date_key, product_key, store_key)
INCLUDE (sales_amount, quantity_sold);
 
-- Now this slice query may use index-only scan:
SELECT SUM(f.sales_amount)
FROM sales_fact f
WHERE f.date_key BETWEEN 20240701 AND 20240930;
 
-- 3. Statistics for accurate cardinality estimates
ANALYZE sales_fact;
ANALYZE time_dim;
ANALYZE product_dim;
ANALYZE store_dim;
 
-- 4. Materialized views for common dice patterns
CREATE MATERIALIZED VIEW mv_quarterly_regional_sales AS
SELECT 
    d.year,
    d.quarter,
    p.category,
    s.region,
    SUM(f.sales_amount) as total_sales,
    SUM(f.quantity_sold) as total_units,
    COUNT(*) as transaction_count
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
GROUP BY d.year, d.quarter, p.category, s.region;
 
-- Index the materialized view for common slices
CREATE INDEX idx_mv_quarter ON mv_quarterly_regional_sales(year, quarter);
CREATE INDEX idx_mv_region ON mv_quarterly_regional_sales(region);
 
-- Dice query can now use the materialized view
SELECT * FROM mv_quarterly_regional_sales
WHERE year = 2024 
  AND quarter IN ('Q3', 'Q4')
  AND region IN ('West', 'Central');
 
-- 5. Partition-based optimization
-- Create list partitions by commonly-sliced dimension
CREATE TABLE sales_fact_partitioned (
    LIKE sales_fact INCLUDING ALL
) PARTITION BY LIST (region_key);
 
CREATE TABLE sales_west PARTITION OF sales_fact_partitioned
    FOR VALUES IN (1, 2, 3);  -- West region store keys
CREATE TABLE sales_east PARTITION OF sales_fact_partitioned
    FOR VALUES IN (4, 5, 6);  -- East region store keys
 
-- Slice on region automatically prunes to relevant partition

Aggregate-Aware Slice/Dice

When slice/dice constraints align with aggregate table granularity, the query can use pre-aggregated data. A slice on Year=2024 with quarterly aggregates can read from quarterly_agg table with year=2024 filter—no need to scan the full fact table. Design aggregate tables based on common slice patterns.

Slice and Dice in Business Intelligence Tools

Modern BI tools abstract slice and dice operations behind intuitive interfaces. Understanding how these tools implement multidimensional operations helps in designing effective analytical applications.

Common BI Interface Patterns:

1. Filter Panels:

Dedicated UI areas with dropdown/multi-select controls for each dimension. Selecting values creates slice or dice conditions:

┌─────────────────────────┐
│ FILTERS                 │
├─────────────────────────┤
│ Year: [2024 ▼]          │  ← Slice: year = 2024
│ Quarter: [Q3] [Q4]      │  ← Dice: quarter IN (Q3,Q4)
│ Region: [All ▼]         │  ← No filter (all regions)
│ Category: [Electronics] │  ← Slice: category = 'Electronics'
└─────────────────────────┘

2. Slicer Tiles:

Visual tiles showing dimension members; clicking toggles selection:

┌───┬───┬───┬───┐
│Q1 │Q2 │▓Q3▓│▓Q4▓│   ← Q3, Q4 selected (highlighted)
└───┴───┴───┴───┘

BI Tool Slice/Dice Features
Feature	Tableau	Power BI	Looker
Filter controls	Filters shelf, Quick Filters	Slicers, Filter pane	Filter tiles, Dashboard filters
Multi-select	CTRL+click	Multi-select slicer	Checkbox filters
Cross-filtering	Dashboard actions	Cross-filter interactions	Cross-filtering tiles
Relative dates	Relative date filters	Relative date slicer	Date filters, timeframes
Saved filters	Dashboard filter actions	Bookmarks	Looks, Filter persistence
Filter visualization	Context in title	Slicer visibility	Active filters display

3. Cross-Filtering:

Interacting with one visualization filters others on the same dashboard:

Click 'West' on map → Bar chart updates to show West data
This is an implicit slice triggered by selection

4. Drill Path Definition:

BI tools allow defining hierarchies that support both slice and drill operations:

Time Hierarchy: Year → Quarter → Month → Week → Day

User can slice at any level, then drill-down within that sliced context.

5. Report-Level vs Page-Level vs Visual-Level Filters:

Report-level: Slice applies to all pages/dashboards
Page-level: Slice applies to all visuals on one page
Visual-level: Slice applies to one specific chart

This hierarchy allows building complex dashboards with different filter scopes.

MDX: Native Multidimensional Language

Traditional OLAP systems (like Microsoft Analysis Services, Essbase) use MDX (Multidimensional Expressions) rather than SQL. MDX has native slice/dice syntax: SELECT {[Measures].[Sales]} ON COLUMNS, {[Time].[2024].[Q3]} ON ROWS FROM [SalesCube] WHERE ([Geography].[West]). The WHERE clause is the slicer. Modern columnar databases (Snowflake, BigQuery) use SQL with OLAP-aware optimizations instead.

Analytical Patterns Using Slice and Dice

Slice and dice operations enable powerful analytical patterns. Here are common use cases that demonstrate their value:

Pattern 1: Time-Comparative Analysis

Slice to specific periods and compare:

[Current Quarter Slice] vs [Same Quarter Last Year Slice]

Example: Compare Q3 2024 performance to Q3 2023.

Pattern 2: Cohort Analysis

Dice to specific customer/product cohorts:

[Customers acquired in 2023] × [Purchases in 2024]

Example: How are 2023-acquired customers performing in 2024?

Pattern 3: A/B Test Analysis

Dice to treatment and control groups:

[Test Group = 'A'] metrics vs [Test Group = 'B'] metrics

analytical_patterns.sql
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-- ANALYTICAL PATTERNS WITH SLICE AND DICE
 
-- Pattern 1: Year-over-Year comparison (Parallel slices)
WITH current_period AS (
    SELECT 
        p.category,
        s.region,
        SUM(f.sales_amount) as sales_2024
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE d.year = 2024 AND d.quarter = 'Q3'  -- SLICE: Q3 2024
    GROUP BY p.category, s.region
),
prior_period AS (
    SELECT 
        p.category,
        s.region,
        SUM(f.sales_amount) as sales_2023
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE d.year = 2023 AND d.quarter = 'Q3'  -- SLICE: Q3 2023
    GROUP BY p.category, s.region
)
SELECT 
    c.category,
    c.region,
    c.sales_2024,
    p.sales_2023,
    ROUND((c.sales_2024 - p.sales_2023) / NULLIF(p.sales_2023, 0) * 100, 1) as yoy_growth_pct
FROM current_period c
LEFT JOIN prior_period p USING (category, region)
ORDER BY yoy_growth_pct DESC NULLS LAST;
 
-- Pattern 2: Segment comparison (Parallel dices)
SELECT 
    CASE 
        WHEN cust.signup_date >= '2023-01-01' THEN 'New (2023+)'
        WHEN cust.signup_date >= '2021-01-01' THEN 'Established (2021-22)'
        ELSE 'Legacy (pre-2021)'
    END as customer_segment,
    COUNT(DISTINCT f.customer_key) as customer_count,
    SUM(f.sales_amount) as total_sales,
    SUM(f.sales_amount) / COUNT(DISTINCT f.customer_key) as sales_per_customer
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN customer_dim cust ON f.customer_key = cust.customer_key
WHERE d.year = 2024  -- SLICE: 2024 only
GROUP BY customer_segment
ORDER BY sales_per_customer DESC;
 
-- Pattern 3: Exception identification (Dice + filter on metrics)
WITH regional_stats AS (
    SELECT 
        s.region,
        p.category,
        SUM(f.sales_amount) as sales,
        AVG(SUM(f.sales_amount)) OVER (PARTITION BY s.region) as region_avg,
        STDDEV(SUM(f.sales_amount)) OVER (PARTITION BY s.region) as region_stddev
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE d.year = 2024 AND d.quarter = 'Q3'  -- SLICE
      AND s.region IN ('West', 'Central')     -- DICE
    GROUP BY s.region, p.category
)
SELECT 
    region,
    category,
    sales,
    region_avg,
    ROUND((sales - region_avg) / NULLIF(region_stddev, 0), 2) as z_score,
    CASE 
        WHEN sales > region_avg + 2 * region_stddev THEN 'Exceptional'
        WHEN sales < region_avg - 2 * region_stddev THEN 'Underperforming'
        ELSE 'Normal'
    END as performance_status
FROM regional_stats
WHERE sales < region_avg - region_stddev  -- Filter to underperformers
   OR sales > region_avg + region_stddev  -- or over-performers
ORDER BY z_score DESC;

Pattern Library

Build a library of reusable analytical patterns. Each pattern is a combination of slice/dice/roll-up/drill-down that answers a specific type of business question. Common patterns: Period comparison, Segment analysis, Exception detection, Trend identification, Mix analysis, Waterfall decomposition. Template these patterns for quick adaptation to new questions.

Summary and Key Takeaways

We've thoroughly explored slice and dice operations—the filtering capabilities that focus multidimensional analysis on specific subcubes of interest. Let's consolidate the key concepts:

Key Takeaways

•Slice fixes one dimension to one value — Reducing dimensionality, creating a cross-section through the data cube.
•Dice constrains multiple dimensions — Creating a smaller subcube while maintaining all dimensions.
•Both are implemented as WHERE clauses — Slice uses equality, dice uses IN lists or ranges.
•Operations combine fluidly — Slice/dice to focus, then roll-up/drill-down within that focus.
•Dynamic filtering is essential — Parameterized queries, optional filters, and saved presets enable interactive analysis.
•Performance requires indexing — Dimension keys, covering indexes, and partition pruning enable fast filtering.
•BI tools abstract the operations — Filter panels, slicers, and cross-filtering provide intuitive interfaces.
•Common patterns emerge — Period comparison, cohort analysis, exception detection are standard slice/dice applications.

What's Next:

With slice, dice, roll-up, and drill-down understood, we have one more essential operation: Pivot. Pivoting rotates the data cube—swapping dimensions between rows and columns—to reveal patterns that might be hidden in other orientations. It's the final piece of the core OLAP operation set.

After pivot, we'll explore Data Cubes and their materialization strategies, completing our understanding of OLAP fundamentals.

Page Complete

You now understand slice and dice operations—how they filter the data cube to create focused views for analysis. You can implement these operations in SQL, optimize their performance, and apply them in sophisticated analytical patterns. Next, we'll explore the pivot operation for dimensional rotation.

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Database Management SystemsOLAP Operations

OLAP Operations: Multidimensional Data Analysis

LevelIntermediate

Duration75 mins

TopicOLAP Operations

3 / 5

Slice and Dice: Focused Multidimensional Analysis

Cutting Through the Data Cube

"Show me all product sales, but only for Q3 2024."

"Compare Electronics and Home Goods categories across regions, but exclude online sales."

"Analyze West region performance across all products and time periods."

What You Will Learn

The Slice Operation: Cutting Through One Dimension

Formal Definition:

Given an n-dimensional cube C with dimensions (D₁, D₂, ..., Dₙ), a slice on dimension Dᵢ with value v produces an (n-1)-dimensional subcube where Dᵢ is fixed at value v.

Visual Metaphor:

Imagine a physical block of ice representing a 3D data cube [Time × Product × Region]. A slice is literally slicing through the block with a knife:

Slice on Time = 'Q3' → You get a 2D slab showing [Product × Region] for Q3 only
Slice on Region = 'West' → You get a 2D slab showing [Time × Product] for West only
Slice on Product = 'Electronics' → You get a 2D slab showing [Time × Region] for Electronics only

slice_examples.sql
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-- SLICE OPERATION EXAMPLES
-- Each slice fixes one dimension to a single value
 
-- Slice on TIME dimension: Fix to Q3 2024
-- Result: 2D subcube of [Product × Region] for Q3
SELECT 
    p.category,
    s.region,
    SUM(f.sales_amount) as total_sales,
    SUM(f.quantity_sold) as units_sold
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE d.quarter = 'Q3' AND d.year = 2024  -- SLICE: Fix time to Q3 2024
GROUP BY p.category, s.region
ORDER BY s.region, total_sales DESC;
 
-- Slice on GEOGRAPHY dimension: Fix to West region
-- Result: 2D subcube of [Time × Product] for West
SELECT 
    d.quarter,
    p.category,
    SUM(f.sales_amount) as total_sales,
    AVG(f.unit_price) as avg_price
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE s.region = 'West'  -- SLICE: Fix geography to West
  AND d.year = 2024
GROUP BY d.quarter, p.category
ORDER BY d.quarter, p.category;
 
-- Slice on PRODUCT dimension: Fix to Electronics category
-- Result: 2D subcube of [Time × Region] for Electronics
SELECT 
    d.month_name,
    s.region,
    SUM(f.sales_amount) as total_sales,
    COUNT(DISTINCT f.store_key) as active_stores
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE p.category = 'Electronics'  -- SLICE: Fix product to Electronics
  AND d.year = 2024
GROUP BY d.month_name, d.month_num, s.region
ORDER BY d.month_num, s.region;

Key Characteristics of Slice:

Dimension Reduction: After slicing, the fixed dimension is no longer part of the result set—it's constant, so it doesn't need to appear in GROUP BY or output columns (though you might include it for context).
Single Value Selection: Classic slice selects exactly one member from the dimension. Selecting multiple values (e.g., Q2 and Q3) is a dice operation, not a slice.
Filtering Before Aggregation: Slice is applied in the WHERE clause, filtering fact rows before any aggregation occurs.
Hierarchy Level Flexibility: You can slice at any hierarchy level—slice by Year, Quarter, Month, or even Day.

Slice vs. Filter

The Dice Operation: Selecting a Sub-Cube

Formal Definition:

Given an n-dimensional cube C, a dice operation specifies selection criteria for multiple dimensions:

D₁ ∈ {v₁₁, v₁₂, ...}
D₂ ∈ {v₂₁, v₂₂, ...}
...

The result is an n-dimensional subcube containing only the specified members.

Visual Metaphor:

If slice is cutting a flat slab from the cube, dice is carving out a smaller cube from within the larger cube. You're constraining multiple edges:

Time: Only Q3 and Q4
Product: Only Electronics and Home
Region: Only West and Central

The result is still a 3D cube, just smaller: 2 quarters × 2 categories × 2 regions = 8 cells instead of potentially hundreds.

Slice Characteristics

•Fixes ONE dimension to ONE value
•Reduces dimensionality by 1
•Result is a cross-section (n-1 dims)
•Example: Region = 'West'
•Metaphor: Cutting a slab from cube

Dice Characteristics

•Constrains MULTIPLE dimensions
•Maintains dimensionality
•Result is a subcube (still n dims)
•Example: Region IN ('West','East') AND Quarter IN ('Q3','Q4')
•Metaphor: Carving smaller cube from larger

dice_examples.sql

-- DICE OPERATION: Constrain multiple dimensions simultaneously
-- Maintains all dimensions but with restricted scope
 
-- Dice: Time (Q3, Q4) × Product (Electronics, Home) × Region (West, Central)
SELECT 
    d.quarter,
    p.category,
    s.region,
    SUM(f.sales_amount) as total_sales,
    SUM(f.quantity_sold) as units_sold,
    COUNT(*) as transaction_count
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE d.year = 2024
  AND d.quarter IN ('Q3', 'Q4')              -- Dice on Time: 2 quarters
  AND p.category IN ('Electronics', 'Home')   -- Dice on Product: 2 categories
  AND s.region IN ('West', 'Central')         -- Dice on Region: 2 regions
GROUP BY d.quarter, p.category, s.region
ORDER BY d.quarter, s.region, p.category;
 
-- Result: 2×2×2 = 8 cells (compare to potentially hundreds in full cube)

Dice for Comparative Analysis

Combining OLAP Operations

Real analytical workflows combine all OLAP operations fluidly. Understanding how they interact enables sophisticated analysis:

Operation Ordering:

Dice/Slice First: Restrict to the subcube of interest
Roll-up/Drill-down: Navigate hierarchy levels within that subcube
Repeat: Further slice/dice as focus narrows

Example Analytical Workflow:

Starting point: Full 3D cube [Year × Category × Region]

Slice on Year = 2024 → Now analyzing just 2024 data
Drill-down Year to Quarter → See Q1, Q2, Q3, Q4 breakdown
Dice on Quarters (Q3, Q4) and Regions (West, Central) → Focus on second-half Western performance
Drill-down Category to Brand → See brand-level detail within Electronics and Home
Slice on Q3 only → Final focus: Q3 brand performance in Western regions

combined_operations.sql
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-- COMBINED OLAP OPERATIONS
-- Analytical workflow from high-level to detailed insight
 
-- Step 1: SLICE (Year = 2024) + ROLL-UP (to Quarterly level)
SELECT d.quarter, SUM(f.sales_amount) as sales
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
WHERE d.year = 2024  -- SLICE
GROUP BY d.quarter;  -- Rolled up to Quarter
 
-- Step 2: DICE (Q3, Q4 × West, Central) - maintaining quarterly view
SELECT 
    d.quarter,
    s.region,
    SUM(f.sales_amount) as sales
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN store_dim s ON f.store_key = s.store_key
WHERE d.year = 2024
  AND d.quarter IN ('Q3', 'Q4')       -- DICE: two quarters
  AND s.region IN ('West', 'Central') -- DICE: two regions
GROUP BY d.quarter, s.region;
 
-- Step 3: DRILL-DOWN on Product (Category → Brand) within diced subcube
SELECT 
    d.quarter,
    s.region,
    p.category,
    p.brand,                          -- DRILL-DOWN: added brand
    SUM(f.sales_amount) as sales,
    RANK() OVER (
        PARTITION BY d.quarter, s.region, p.category 
        ORDER BY SUM(f.sales_amount) DESC
    ) as brand_rank
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN store_dim s ON f.store_key = s.store_key
JOIN product_dim p ON f.product_key = p.product_key
WHERE d.year = 2024
  AND d.quarter IN ('Q3', 'Q4')
  AND s.region IN ('West', 'Central')
  AND p.category IN ('Electronics', 'Home')  -- DICE: added category constraint
GROUP BY d.quarter, s.region, p.category, p.brand;
 
-- Step 4: SLICE Q3 only, focus on top brands per category
WITH ranked_brands AS (
    SELECT 
        d.quarter,
        s.region,
        p.category,
        p.brand,
        SUM(f.sales_amount) as sales,
        RANK() OVER (
            PARTITION BY s.region, p.category 
            ORDER BY SUM(f.sales_amount) DESC
        ) as brand_rank
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN store_dim s ON f.store_key = s.store_key
    JOIN product_dim p ON f.product_key = p.product_key
    WHERE d.year = 2024
      AND d.quarter = 'Q3'                -- SLICE: only Q3 now
      AND s.region IN ('West', 'Central')
      AND p.category IN ('Electronics', 'Home')
    GROUP BY d.quarter, s.region, p.category, p.brand
)
SELECT * FROM ranked_brands 
WHERE brand_rank <= 5                     -- Top 5 brands per region+category
ORDER BY region, category, brand_rank;

OLAP Operations Summary
Operation	Action	Result	SQL Mechanism
Roll-up	Aggregate up hierarchy	Coarser granularity	GROUP BY at higher level
Drill-down	Decompose down hierarchy	Finer granularity	GROUP BY at lower level + WHERE filter
Slice	Fix one dimension	Reduce dimensionality	WHERE dim = single_value
Dice	Constrain multiple dimensions	Smaller subcube, same dimensionality	WHERE with multiple IN clauses
Pivot	Rotate dimensions	Different perspective, same data	CASE expressions or PIVOT clause

Dynamic Filtering Patterns

Pattern 1: Parameterized Filters

Queries accept filter values as parameters:

SELECT ...
FROM sales_fact f ...
WHERE ($region IS NULL OR s.region = $region)          -- Optional slice
  AND ($category IS NULL OR p.category = $category)     -- Optional slice
  AND ($start_date IS NULL OR d.full_date >= $start_date)
  AND ($end_date IS NULL OR d.full_date <= $end_date)

Pattern 2: Dynamic Member Lists

Handle multi-select filters where users choose multiple values:

dynamic_filters.sql

-- DYNAMIC SLICE AND DICE PATTERNS
 
-- Pattern 1: Optional filters with NULL checks
-- All parameters optional - NULL means "all values"
CREATE OR REPLACE FUNCTION get_sales_cube(
    p_year INT DEFAULT NULL,
    p_quarter TEXT DEFAULT NULL,
    p_category TEXT DEFAULT NULL,
    p_region TEXT DEFAULT NULL
) RETURNS TABLE (
    quarter TEXT,
    category TEXT,
    region TEXT,
    total_sales NUMERIC,
    units_sold BIGINT
) AS $$
BEGIN
    RETURN QUERY
    SELECT 
        d.quarter,
        p.category,
        s.region,
        SUM(f.sales_amount)::NUMERIC as total_sales,
        SUM(f.quantity_sold)::BIGINT as units_sold
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE (p_year IS NULL OR d.year = p_year)         -- Optional year slice
      AND (p_quarter IS NULL OR d.quarter = p_quarter) -- Optional quarter slice
      AND (p_category IS NULL OR p.category = p_category)
      AND (p_region IS NULL OR s.region = p_region)
    GROUP BY d.quarter, p.category, s.region;
END;
$$ LANGUAGE plpgsql;
 
-- Usage examples:
SELECT * FROM get_sales_cube(2024, NULL, NULL, 'West');  -- Year + Region slice
SELECT * FROM get_sales_cube(2024, 'Q3', 'Electronics', NULL);  -- Three slices
 
-- Pattern 2: Multi-value dice with arrays (PostgreSQL)
CREATE OR REPLACE FUNCTION get_sales_diced(
    p_years INT[] DEFAULT NULL,
    p_quarters TEXT[] DEFAULT NULL,
    p_categories TEXT[] DEFAULT NULL,
    p_regions TEXT[] DEFAULT NULL
) RETURNS TABLE (...) AS $$
BEGIN
    RETURN QUERY
    SELECT ...
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE (p_years IS NULL OR d.year = ANY(p_years))
      AND (p_quarters IS NULL OR d.quarter = ANY(p_quarters))
      AND (p_categories IS NULL OR p.category = ANY(p_categories))
      AND (p_regions IS NULL OR s.region = ANY(p_regions))
    GROUP BY d.quarter, p.category, s.region;
END;
$$ LANGUAGE plpgsql;
 
-- Usage: Dice on specific lists
SELECT * FROM get_sales_diced(
    ARRAY[2023, 2024],                    -- Two years
    ARRAY['Q3', 'Q4'],                    -- Two quarters
    ARRAY['Electronics', 'Appliances'],   -- Two categories
    ARRAY['West', 'Central', 'East']      -- Three regions
);

Performance with Dynamic Filters

Optimizing Slice and Dice Performance

Efficient slice and dice operations require thoughtful indexing, partitioning, and data organization. Here are key optimization strategies:

1. Dimension Key Indexing:

Create indexes on dimension foreign keys in fact tables for fast filtering:

-- Composite index for common slice/dice patterns
CREATE INDEX idx_sales_dims ON sales_fact(date_key, product_key, store_key);

-- Partial indexes for frequently-sliced values
CREATE INDEX idx_sales_recent ON sales_fact(product_key, store_key)
WHERE date_key >= (SELECT MIN(date_key) FROM time_dim WHERE year >= 2023);

2. Partition Pruning:

When slicing on partitioned columns (typically time), the database can skip entire partitions:

-- If sales_fact is partitioned by year-month
-- Slice on year = 2024 only scans 2024 partitions
SELECT ... WHERE d.year = 2024;  -- Prunes 2023 and earlier

Index Strategies for Slice/Dice Operations
Filter Pattern	Recommended Index	Benefit
Single dimension slice	B-tree on dimension key	Fast equality lookup
Range slice (dates)	B-tree on date column	Range scan efficiency
Multi-value dice (IN list)	B-tree on dimension key	Multiple point lookups
Low-cardinality dimension	Bitmap index (if supported)	Fast bitwise filtering
Composite slice+dice	Multi-column index	Cover all filter columns
Frequent filter combination	Partial/filtered index	Smaller, targeted index

slice_dice_optimization.sql
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-- SLICE/DICE PERFORMANCE OPTIMIZATION
 
-- 1. Bitmap indexes for low-cardinality dimensions (Oracle-style, concept)
-- In PostgreSQL, regular B-tree works; bitmap scans happen automatically
 
-- 2. Covering indexes to avoid table lookups
CREATE INDEX idx_sales_covering ON sales_fact(date_key, product_key, store_key)
INCLUDE (sales_amount, quantity_sold);
 
-- Now this slice query may use index-only scan:
SELECT SUM(f.sales_amount)
FROM sales_fact f
WHERE f.date_key BETWEEN 20240701 AND 20240930;
 
-- 3. Statistics for accurate cardinality estimates
ANALYZE sales_fact;
ANALYZE time_dim;
ANALYZE product_dim;
ANALYZE store_dim;
 
-- 4. Materialized views for common dice patterns
CREATE MATERIALIZED VIEW mv_quarterly_regional_sales AS
SELECT 
    d.year,
    d.quarter,
    p.category,
    s.region,
    SUM(f.sales_amount) as total_sales,
    SUM(f.quantity_sold) as total_units,
    COUNT(*) as transaction_count
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN product_dim p ON f.product_key = p.product_key
JOIN store_dim s ON f.store_key = s.store_key
GROUP BY d.year, d.quarter, p.category, s.region;
 
-- Index the materialized view for common slices
CREATE INDEX idx_mv_quarter ON mv_quarterly_regional_sales(year, quarter);
CREATE INDEX idx_mv_region ON mv_quarterly_regional_sales(region);
 
-- Dice query can now use the materialized view
SELECT * FROM mv_quarterly_regional_sales
WHERE year = 2024 
  AND quarter IN ('Q3', 'Q4')
  AND region IN ('West', 'Central');
 
-- 5. Partition-based optimization
-- Create list partitions by commonly-sliced dimension
CREATE TABLE sales_fact_partitioned (
    LIKE sales_fact INCLUDING ALL
) PARTITION BY LIST (region_key);
 
CREATE TABLE sales_west PARTITION OF sales_fact_partitioned
    FOR VALUES IN (1, 2, 3);  -- West region store keys
CREATE TABLE sales_east PARTITION OF sales_fact_partitioned
    FOR VALUES IN (4, 5, 6);  -- East region store keys
 
-- Slice on region automatically prunes to relevant partition

Aggregate-Aware Slice/Dice

Slice and Dice in Business Intelligence Tools

Common BI Interface Patterns:

1. Filter Panels:

Dedicated UI areas with dropdown/multi-select controls for each dimension. Selecting values creates slice or dice conditions:

┌─────────────────────────┐
│ FILTERS                 │
├─────────────────────────┤
│ Year: [2024 ▼]          │  ← Slice: year = 2024
│ Quarter: [Q3] [Q4]      │  ← Dice: quarter IN (Q3,Q4)
│ Region: [All ▼]         │  ← No filter (all regions)
│ Category: [Electronics] │  ← Slice: category = 'Electronics'
└─────────────────────────┘

2. Slicer Tiles:

Visual tiles showing dimension members; clicking toggles selection:

┌───┬───┬───┬───┐
│Q1 │Q2 │▓Q3▓│▓Q4▓│   ← Q3, Q4 selected (highlighted)
└───┴───┴───┴───┘

BI Tool Slice/Dice Features
Feature	Tableau	Power BI	Looker
Filter controls	Filters shelf, Quick Filters	Slicers, Filter pane	Filter tiles, Dashboard filters
Multi-select	CTRL+click	Multi-select slicer	Checkbox filters
Cross-filtering	Dashboard actions	Cross-filter interactions	Cross-filtering tiles
Relative dates	Relative date filters	Relative date slicer	Date filters, timeframes
Saved filters	Dashboard filter actions	Bookmarks	Looks, Filter persistence
Filter visualization	Context in title	Slicer visibility	Active filters display

3. Cross-Filtering:

Interacting with one visualization filters others on the same dashboard:

Click 'West' on map → Bar chart updates to show West data
This is an implicit slice triggered by selection

4. Drill Path Definition:

BI tools allow defining hierarchies that support both slice and drill operations:

Time Hierarchy: Year → Quarter → Month → Week → Day

User can slice at any level, then drill-down within that sliced context.

5. Report-Level vs Page-Level vs Visual-Level Filters:

Report-level: Slice applies to all pages/dashboards
Page-level: Slice applies to all visuals on one page
Visual-level: Slice applies to one specific chart

This hierarchy allows building complex dashboards with different filter scopes.

MDX: Native Multidimensional Language

Analytical Patterns Using Slice and Dice

Slice and dice operations enable powerful analytical patterns. Here are common use cases that demonstrate their value:

Pattern 1: Time-Comparative Analysis

Slice to specific periods and compare:

[Current Quarter Slice] vs [Same Quarter Last Year Slice]

Example: Compare Q3 2024 performance to Q3 2023.

Pattern 2: Cohort Analysis

Dice to specific customer/product cohorts:

[Customers acquired in 2023] × [Purchases in 2024]

Example: How are 2023-acquired customers performing in 2024?

Pattern 3: A/B Test Analysis

Dice to treatment and control groups:

[Test Group = 'A'] metrics vs [Test Group = 'B'] metrics

analytical_patterns.sql
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-- ANALYTICAL PATTERNS WITH SLICE AND DICE
 
-- Pattern 1: Year-over-Year comparison (Parallel slices)
WITH current_period AS (
    SELECT 
        p.category,
        s.region,
        SUM(f.sales_amount) as sales_2024
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE d.year = 2024 AND d.quarter = 'Q3'  -- SLICE: Q3 2024
    GROUP BY p.category, s.region
),
prior_period AS (
    SELECT 
        p.category,
        s.region,
        SUM(f.sales_amount) as sales_2023
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE d.year = 2023 AND d.quarter = 'Q3'  -- SLICE: Q3 2023
    GROUP BY p.category, s.region
)
SELECT 
    c.category,
    c.region,
    c.sales_2024,
    p.sales_2023,
    ROUND((c.sales_2024 - p.sales_2023) / NULLIF(p.sales_2023, 0) * 100, 1) as yoy_growth_pct
FROM current_period c
LEFT JOIN prior_period p USING (category, region)
ORDER BY yoy_growth_pct DESC NULLS LAST;
 
-- Pattern 2: Segment comparison (Parallel dices)
SELECT 
    CASE 
        WHEN cust.signup_date >= '2023-01-01' THEN 'New (2023+)'
        WHEN cust.signup_date >= '2021-01-01' THEN 'Established (2021-22)'
        ELSE 'Legacy (pre-2021)'
    END as customer_segment,
    COUNT(DISTINCT f.customer_key) as customer_count,
    SUM(f.sales_amount) as total_sales,
    SUM(f.sales_amount) / COUNT(DISTINCT f.customer_key) as sales_per_customer
FROM sales_fact f
JOIN time_dim d ON f.date_key = d.date_key
JOIN customer_dim cust ON f.customer_key = cust.customer_key
WHERE d.year = 2024  -- SLICE: 2024 only
GROUP BY customer_segment
ORDER BY sales_per_customer DESC;
 
-- Pattern 3: Exception identification (Dice + filter on metrics)
WITH regional_stats AS (
    SELECT 
        s.region,
        p.category,
        SUM(f.sales_amount) as sales,
        AVG(SUM(f.sales_amount)) OVER (PARTITION BY s.region) as region_avg,
        STDDEV(SUM(f.sales_amount)) OVER (PARTITION BY s.region) as region_stddev
    FROM sales_fact f
    JOIN time_dim d ON f.date_key = d.date_key
    JOIN product_dim p ON f.product_key = p.product_key
    JOIN store_dim s ON f.store_key = s.store_key
    WHERE d.year = 2024 AND d.quarter = 'Q3'  -- SLICE
      AND s.region IN ('West', 'Central')     -- DICE
    GROUP BY s.region, p.category
)
SELECT 
    region,
    category,
    sales,
    region_avg,
    ROUND((sales - region_avg) / NULLIF(region_stddev, 0), 2) as z_score,
    CASE 
        WHEN sales > region_avg + 2 * region_stddev THEN 'Exceptional'
        WHEN sales < region_avg - 2 * region_stddev THEN 'Underperforming'
        ELSE 'Normal'
    END as performance_status
FROM regional_stats
WHERE sales < region_avg - region_stddev  -- Filter to underperformers
   OR sales > region_avg + region_stddev  -- or over-performers
ORDER BY z_score DESC;

Pattern Library

Summary and Key Takeaways

We've thoroughly explored slice and dice operations—the filtering capabilities that focus multidimensional analysis on specific subcubes of interest. Let's consolidate the key concepts:

Key Takeaways

•Slice fixes one dimension to one value — Reducing dimensionality, creating a cross-section through the data cube.
•Dice constrains multiple dimensions — Creating a smaller subcube while maintaining all dimensions.
•Both are implemented as WHERE clauses — Slice uses equality, dice uses IN lists or ranges.
•Operations combine fluidly — Slice/dice to focus, then roll-up/drill-down within that focus.
•Dynamic filtering is essential — Parameterized queries, optional filters, and saved presets enable interactive analysis.
•Performance requires indexing — Dimension keys, covering indexes, and partition pruning enable fast filtering.
•BI tools abstract the operations — Filter panels, slicers, and cross-filtering provide intuitive interfaces.
•Common patterns emerge — Period comparison, cohort analysis, exception detection are standard slice/dice applications.

What's Next:

After pivot, we'll explore Data Cubes and their materialization strategies, completing our understanding of OLAP fundamentals.

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