/* ------
SYS.TABLES returns a row for each user table in SQL Server.
SYS.COLUMNS returns a row for each column of an object that has
columns, such as views or tables.
SYS.INDEXES contains a row per index or heap of a tabular object,
such as a table, view, or table-valued function.
SYS.PARTITIONS contains a row for each partition of all the tables
and most types of indexes in the database.
SYS.ALLOCATION_UNITS contains a row for each allocation unit in the
database.
SYS.SCHEMAS contains a row for each database schema.
*/
Use Demo
Go
SELECT
Sch.NAME AS SCHEMA_NAME,
Tab.NAME AS TABLE_NAME,
COUNT(Col.OBJECT_ID) AS
COLUMN_COUNT,
Part.ROWS AS ROW_COUNTS,
CAST(ROUND((SUM(Aut.USED_PAGES) /
128.00),
2)
AS NUMERIC(36, 2))
AS USED_MB,
CAST(ROUND((SUM(Aut.TOTAL_PAGES) -
SUM(Aut.USED_PAGES)) /
128.00,
2)
AS NUMERIC(36, 2))
AS UNUSED_MB,
CAST(ROUND((SUM(Aut.TOTAL_PAGES) /
128.00),
2)
AS NUMERIC(36, 2))
AS TOTAL_MB
FROM SYS.TABLES Tab
INNER JOIN SYS.COLUMNS Col
ON Tab.OBJECT_ID = Col.OBJECT_ID
INNER JOIN SYS.INDEXES Ind
ON Tab.OBJECT_ID = Ind.OBJECT_ID
INNER JOIN SYS.PARTITIONS Part
ON Ind.OBJECT_ID = Part.OBJECT_ID AND
Ind.INDEX_ID
= Part.INDEX_ID
INNER JOIN SYS.ALLOCATION_UNITS Aut
ON Part.PARTITION_ID =
Aut.CONTAINER_ID
INNER JOIN SYS.SCHEMAS Sch
ON Tab.SCHEMA_ID = Sch.SCHEMA_ID
GROUP BY
Tab.NAME, Sch.NAME, Part.ROWS
ORDER BY
Sch.NAME, Tab.NAME
|
DBName
|
SchemaName
|
TableName
|
ColumnCount
|
RowCounts
|
Used_MB
|
Unused_MB
|
Total_MB
|
Demo
|
dbo
|
tbl_SalesOrder
|
133
|
263
|
69.62
|
6.23
|
75.85
|
Demo
|
dbo
|
tbl_Customers
|
12565
|
41724326
|
30030.71
|
1305.83
|
31336.54
|
Demo
|
dbo
|
tbl_SalesPerson
|
35
|
1123
|
18.32
|
1.64
|
19.96
|
Demo
|
dbo
|
tbl_Product
|
12
|
94
|
0.28
|
0.56
|
0.84
|
Data
validation — Data
validation is the process of checking the data against predefined rules and
standards, such as data types, formats, ranges, and constraints.
💫Schema
Validation: Verify data
adherence to predefined schemas, checking types, formats, and structures.
💫Integrity
Constraints: Enforce
rules and constraints to maintain data integrity, preventing inconsistencies.
💫Cross-Field
Validation: Validate
relationships and dependencies between different fields to ensure logical
coherence.
💫Data
Quality Metrics: Define and
track quality metrics, such as completeness, accuracy, and consistency.
💫Automated
Validation Scripts: Develop
and run automated scripts to check data against predefined rules and criteria.
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learn more, please follow us -
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