SQL BI & Reporting

When to Use

• Writing summary reports with subtotals across multiple dimensions
• Building time-series queries (bucketing, gap filling, moving averages)
• Pivoting row data into columns or unpivoting columns into rows
• Generating date ranges or number sequences (tally tables)
• Solving gaps-and-islands problems (consecutive ranges, session analysis)
• Exporting query results as JSON, XML, or CSV
• Writing queries optimized for Power BI DirectQuery or SSRS
• Year-over-year, cohort, or funnel analysis

When NOT to use: application schema design (table design, naming conventions, access control), query performance tuning (execution plans, index tuning, wait stats), or ETL pipeline design.

Multi-Level Aggregation

Three operators produce subtotals from a single GROUP BY — choose based on your needs:

Operator	Produces	Use when
ROLLUP(A, B, C)	(A,B,C), (A,B), (A), ()	Columns form a hierarchy (year > quarter > month)
CUBE(A, B)	All 2^n combinations	Need every cross-dimensional combination
GROUPING SETS(...)	Exactly what you list	Need specific subtotals, not a formula

-- Hierarchical subtotals: year > quarter > grand total
SELECT
    YEAR(OrderDate)              AS OrderYear,
    DATEPART(quarter, OrderDate) AS OrderQtr,
    SUM(TotalAmount)             AS Revenue
FROM Sales.Orders
GROUP BY ROLLUP(
    YEAR(OrderDate),
    DATEPART(quarter, OrderDate)
)
ORDER BY OrderYear, OrderQtr;

Use GROUPING(col) to distinguish subtotal NULLs from real NULLs — returns 1 for subtotal rows, 0 for data rows.

Full reference: Aggregation Patterns — GROUPING_ID, conditional aggregation, HAVING vs WHERE, COUNT semantics with NULLs.

Window Functions

Quick Reference

Category	Functions	Frame needed?
Ranking	ROW_NUMBER, RANK, DENSE_RANK, NTILE	No
Offset	LAG, LEAD, FIRST_VALUE, LAST_VALUE	Yes for LAST_VALUE
Aggregate	SUM, AVG, COUNT, MIN, MAX	Yes for running/sliding

Running total

SELECT
    OrderDate,
    TotalAmount,
    SUM(TotalAmount) OVER (
        ORDER BY OrderDate
        ROWS UNBOUNDED PRECEDING
    ) AS RunningTotal
FROM Sales.Orders;

Always specify ROWS explicitly. The default frame (when ORDER BY is present but no frame is specified) is RANGE UNBOUNDED PRECEDING — this includes ties, produces different results, and is slower.

Moving average

-- 7-day moving average
AVG(DailyRevenue) OVER (
    ORDER BY OrderDate
    ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) AS MovingAvg7Day

Period-over-period comparison

-- Year-over-year monthly revenue (offset 12 for monthly data)
LAG(Revenue, 12) OVER (ORDER BY OrderYear, OrderMonth) AS PriorYearRevenue

Greatest-N-per-group

-- Most recent order per customer
WITH Ranked AS (
    SELECT *,
        ROW_NUMBER() OVER (
            PARTITION BY CustomerID
            ORDER BY OrderDate DESC
        ) AS RN
    FROM Sales.Orders
)
SELECT * FROM Ranked WHERE RN = 1;

Named WINDOW clause (2022+)

When multiple window functions share the same partition and order, avoid repetition:

SELECT
    OrderID,
    ROW_NUMBER() OVER Win           AS RowNum,
    SUM(TotalAmount) OVER Win       AS RunningTotal,
    LAG(TotalAmount) OVER Win       AS PrevAmount
FROM Sales.Orders
WINDOW Win AS (PARTITION BY CustomerID ORDER BY OrderDate);

Date Math Patterns

Bucketing

Need	SQL Server 2022+	Pre-2022
Truncate to day	DATETRUNC(day, col)	CAST(col AS DATE)
Truncate to month	DATETRUNC(month, col)	DATEFROMPARTS(YEAR(col), MONTH(col), 1)
15-minute buckets	DATE_BUCKET(minute, 15, col)	DATEADD(minute, DATEDIFF(minute, 0, col)/15*15, 0)
Custom week start	DATE_BUCKET(week, 1, col, @origin)	Manual DATEADD/DATEDIFF calculation

SARGable date filters

-- GOOD: index seek
WHERE OrderDate >= '2024-01-01' AND OrderDate < '2025-01-01'

-- BAD: index scan (function on column)
WHERE YEAR(OrderDate) = 2024

Gap filling

LEFT JOIN from a continuous date spine (tally table or calendar table) to your sparse data. ISNULL replaces NULL with zero for missing dates. Use a range join to keep the date column SARGable — never CAST(col AS DATE) in a JOIN:

SELECT
    C.FullDate,
    ISNULL(SUM(O.TotalAmount), 0) AS Revenue
FROM dbo.Calendar C
LEFT JOIN Sales.Orders O
    ON O.OrderDate >= C.FullDate
   AND O.OrderDate <  DATEADD(day, 1, C.FullDate)
WHERE C.FullDate >= '2024-01-01'
  AND C.FullDate <  '2025-01-01'
GROUP BY C.FullDate
ORDER BY C.FullDate;

Full reference: Time Series — calendar table design, fiscal calendars, cohort analysis, temporal table AS OF queries, moving averages.

Tally Tables

Generate number sequences with zero I/O using the stacking CTE pattern:

WITH
    L0 AS (SELECT 1 AS c UNION ALL SELECT 1),
    L1 AS (SELECT 1 AS c FROM L0 CROSS JOIN L0 AS B),
    L2 AS (SELECT 1 AS c FROM L1 CROSS JOIN L1 AS B),
    L3 AS (SELECT 1 AS c FROM L2 CROSS JOIN L2 AS B),
    L4 AS (SELECT 1 AS c FROM L3 CROSS JOIN L3 AS B),
    Nums AS (
        SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS N
        FROM L4
    )
SELECT N FROM Nums
WHERE N <= @Count;   -- REQUIRED: limits output to needed rows

Every tally CTE must include WHERE N <= <limit> — without it, the full 65,536 rows are generated. When used for date spines, the limit is DATEDIFF(day, @Start, @End). Never omit the limit and rely on an outer query to filter — the optimizer may still materialize all rows.

On SQL Server 2022+, use GENERATE_SERIES(1, @Count) for simpler syntax. Do not use recursive CTEs for number generation — they are row-by-row and 10-50x slower.

Full reference: Tally Tables — inline function wrapper, date range generation, gap filling, string splitting, permanent vs inline tradeoffs.

Pivot & Unpivot

Static PIVOT (known columns):

SELECT ProductName, [Q1], [Q2], [Q3], [Q4]
FROM (...) AS Src
PIVOT (SUM(Revenue) FOR Qtr IN ([Q1],[Q2],[Q3],[Q4])) AS Pvt;

Dynamic PIVOT (runtime columns): build with STRING_AGG + QUOTENAME + sp_executesql. QUOTENAME prevents SQL injection.

Unpivot: use CROSS APPLY VALUES instead of the UNPIVOT operator. UNPIVOT silently drops NULL rows; CROSS APPLY VALUES preserves them.

CROSS APPLY (
    VALUES ('Q1', S.Q1), ('Q2', S.Q2), ('Q3', S.Q3), ('Q4', S.Q4)
) AS Q(Quarter, Revenue)

Full reference: Pivot & Unpivot — dynamic PIVOT pattern, multi-column unpivot, conditional aggregation alternative.

Gaps and Islands

Detecting consecutive sequences (islands) and breaks (gaps) in ordered data.

Island detection — the ROW_NUMBER difference technique:

-- GroupKey is constant within each consecutive run
DATEADD(day,
    -ROW_NUMBER() OVER (PARTITION BY SensorID ORDER BY ReadingDate),
    ReadingDate
) AS GroupKey

Group by GroupKey to find each island's start, end, and length.

Gap detection — LEAD to find the next value, then check for breaks:

LEAD(OrderDay) OVER (ORDER BY OrderDay) AS NextDay
-- Gap exists when NextDay - OrderDay > 1

Full reference: Gaps and Islands — session analysis, status change tracking, date-based vs sequence-based patterns.

Data Export

Format	T-SQL	Notes
JSON	FOR JSON PATH	Dot-notation aliases control nesting: AS "customer.name" produces {"customer":{"name":"..."}}
XML	FOR XML PATH	Only when downstream requires XML (SOAP, EDI)
CSV column	STRING_AGG(col, ',')	WITHIN GROUP for ordering (2017+)
Bulk file	BCP / BULK INSERT	TABLOCK for columnstore direct-path

FOR JSON PATH nesting

Use dot-notation aliases to control JSON structure — no subqueries needed for flat nesting:

SELECT
    O.OrderID      AS "id",
    C.CustomerName AS "customer.name",
    C.Email        AS "customer.email"
FROM Sales.Orders O
JOIN Sales.Customers C ON C.CustomerID = O.CustomerID
FOR JSON PATH;
-- {"id":1001,"customer":{"name":"Acme","email":"info@acme.com"}}

Full reference: Export Patterns — FOR JSON nesting, JSON_OBJECT (2022+), Power BI DirectQuery optimization, BCP parameters.

Validation Checklist

Before finalizing any BI query, verify:

1. Subtotal rows — GROUPING(col) = 1 filters identify rollup rows; confirm NULL isn't confused with real data
2. Window frames — every windowed aggregate has an explicit ROWS BETWEEN clause
3. Date filters — all WHERE and JOIN predicates on date columns use range predicates, never CAST(col AS DATE) or YEAR(col)
4. Gap fills — LEFT JOIN from calendar/tally produces rows for every expected period; ISNULL handles missing data
5. Dynamic PIVOT — generated SQL is printed and inspected before sp_executesql; all column names pass through QUOTENAME
6. Export shape — FOR JSON PATH output matches the consumer's expected schema; test with a LIMIT before full run

Common Mistakes

Mistake	Fix
Default window frame with ORDER BY (RANGE, not ROWS)	Always specify ROWS BETWEEN ... explicitly
Using RANGE for moving averages (includes ties)	Use ROWS BETWEEN N PRECEDING AND CURRENT ROW
YEAR(col) = 2024 in WHERE (kills seeks)	Range predicate: col >= '2024-01-01' AND col < '2025-01-01'
CAST(col AS DATE) in JOIN conditions	Use range join: ON col >= date AND col < DATEADD(day, 1, date)
Tally CTE without WHERE N <= limit	Always add WHERE N <= @count — without it, generates full 65K rows
COUNT(column) when you want total rows	COUNT(*) includes NULLs; COUNT(col) excludes them
AVG ignoring NULL semantics	AVG uses COUNT(col) as denominator — use ISNULL(col, 0) if NULLs mean zero
UNPIVOT dropping NULL rows	Use CROSS APPLY VALUES to preserve NULLs
NOT IN with nullable subquery	Use NOT EXISTS — NOT IN silently returns nothing when subquery contains NULL
Recursive CTE for number generation	Use the stacking CTE pattern — set-based and 10-50x faster
FOR JSON AUTO in production	Use FOR JSON PATH — AUTO changes shape when aliases change
HAVING for non-aggregate filters	Move to WHERE — it filters before grouping, which is cheaper
FORMAT for date truncation	FORMAT is CLR-backed (10-50x slower) — use DATETRUNC (2022+) or DATEADD/DATEDIFF
LAST_VALUE without explicit frame	Default frame ends at CURRENT ROW — use ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
LAG with fixed offset on sparse data	If periods are missing, LAG(val, 12) skips to the wrong year — use a self-join

Reference Files

File	Topics
Aggregation Patterns	GROUPING SETS, ROLLUP, CUBE, conditional aggregation, HAVING vs WHERE, COUNT with NULLs
Time Series	Date bucketing, DATETRUNC, DATE_BUCKET, calendar tables, gap filling, YoY/MoM, moving averages, fiscal calendars, cohort analysis, temporal AS OF
Tally Tables	Stacking CTE pattern, GENERATE_SERIES, date ranges, gap filling, inline vs permanent
Pivot & Unpivot	Static PIVOT, dynamic PIVOT with QUOTENAME, CROSS APPLY VALUES unpivot, multi-column unpivot
Gaps and Islands	ROW_NUMBER difference, LAG/LEAD gaps, session analysis, status tracking, consecutive ranges
Export Patterns	FOR JSON, FOR XML, STRING_AGG, BCP/BULK INSERT, Power BI DirectQuery