List all heap tables of a given database

-- List all heap tables 
-- Try this with tempdb and see how many temp tables or table variables have been created there, you will be amazed with large olap databases.  
SELECT s.name + '.' + t.name AS TableName

FROM sys.tables t INNER JOIN sys.schemas s

ON t.schema_id = s.schema_id INNER JOIN sys.indexes i

ON t.object_id = i.object_id

AND i.type = 0 -- = Heap

ORDER BY TableName

Database external fragmentation overview

use MyDatabase




go


select top 50

avg_fragmentation_in_percent*s.page_count/100.00 fragmented_page_count

,o.name as TableName

,i.name as IndexName

,s.partition_number

,s.avg_fragmentation_in_percent

,s.page_count

,s.index_depth

,s.index_level

,s.index_type_desc

,d.name as DataBaseName

,s.*

from sys.dm_db_index_physical_stats(db_id(), null, null, null, 'limited') s

--sys.dm_db_index_physical_stats(db_id(), object_id('MyTableInCurrentDB'), null, null, 'limited') s

inner join sys.databases d on d.database_id=s.database_id

inner join sys.objects o on o.object_id=s.object_id

left join sys.indexes i on i.object_id=s.object_id and i.index_id=s.index_id

where -- have large external fragmentation

s.avg_fragmentation_in_percent>10

-- ignore small table/index

and s.page_count>1

-- ignore heap table

and s.index_id>0

order by avg_fragmentation_in_percent*s.page_count desc

Checking SQL server stats

-- use dm_db_stats_properties to get stats info
-- for SQL 2012 and above
SELECT
 OBJECT_NAME([sp].[object_id]) AS "Table"
 ,[sp].[stats_id] AS "Statistic ID"
 ,[s].[name] AS "Statistic"
 ,[sp].[rows_sampled]
 ,[sp].[modification_counter] AS "Modifications"
 ,[sp].[last_updated] AS "Last Updated"
 ,[sp].[rows]
 ,[sp].[unfiltered_rows]
 FROM [sys].[stats] AS [s]
   OUTER APPLY sys.dm_db_stats_properties ([s].[object_id],[s].[stats_id]) AS [sp]
 --WHERE [s].[object_id] = OBJECT_ID(N'Sales.TestSalesOrderDetail');
 ORDER BY 1,2

--
-- quick way to check which stats objects are used by query

DBCC FREEPROCCACHE

SELECT 

    p.Name,

    total_quantity = SUM(th.Quantity)

FROM AdventureWorks.Production.Product AS p

JOIN AdventureWorks.Production.TransactionHistory AS th ON

    th.ProductID = p.ProductID

WHERE

    th.ActualCost >= $5.00

    AND p.Color = N'Red'

GROUP BY

    p.Name

ORDER BY

    p.Name

OPTION

(

    QUERYTRACEON 3604,

    QUERYTRACEON 9292,

    QUERYTRACEON 9204

)

--
-- checking auto created stats which are never updated or updated before 1 week
DECLARE @NumOfDays int, @StateCheckDateTimeDeadLine datetime
SET @NumOfDays=7
SET @StateCheckDateTimeDeadLine=DATEADD(hour, -24*@NumOfDays, getdate())
--SELECT @StateCheckDateTimeDeadLine

SELECT object_name(object_id) as TableName
,name AS StatisticsName
,STATS_DATE(object_id, stats_id) AS StatisticsUpdatDate
,auto_created AS IsAutoCreated
FROM sys.stats
WHERE auto_created=1
-- never updated or updated before dead line
AND (STATS_DATE(object_id, stats_id) IS NULL
OR STATS_DATE(object_id, stats_id)<=@StateCheckDateTimeDeadLine)
order by 1,2

-- after this check detail of each stat
DBCC SHOW_STATISTICS ( table_or_indexed_view_name , target )
[ WITH [ NO_INFOMSGS ] < option > [ , n ] ]
< option > :: =
    STAT_HEADER | DENSITY_VECTOR | HISTOGRAM | STATS_STREAM

DBCC SHOW_STATISTICS ("MyTable", MyPrimayKey);
DBCC SHOW_STATISTICS ("MyTable", MyStatsName);

reference:
1)
http://sqlblog.com/blogs/paul_white/archive/2011/09/21/how-to-find-the-statistics-used-to-compile-an-execution-plan.aspx
2)
http://www.sqlskills.com/blogs/erin/understanding-when-statistics-will-automatically-update/

Sizing database/partition/partitioned table with [dm_db_partition_stats]

--
-- whole database size
SELECT   
        (SUM(reserved_page_count)*8192)/1024000 AS [TotalSpace(MB)]
        ,(SUM([used_page_count])*8192)/1024000 AS [UsedSpace(MB)]
        ,(SUM([row_count])) AS [RowCount]
        FROM sys.dm_db_partition_stats

--
-- size of each partition scheme       
select    s.name as [partition_scheme]
        ,sum(ps.[reserved_page_count])*8192/10240000  AS [TotalSpace(MB)]
        ,sum(ps.[used_page_count])*8192/10240000 AS [UsedSpace(MB)]
        ,sum([row_count]) AS [RowCount]
    from    sys.indexes i inner join sys.partition_schemes s
            on i.data_space_id = s.data_space_id 
            inner join
                (
                SELECT   
                    [object_id]
                    ,[index_id]
                    ,sum([used_page_count]) as [used_page_count]
                    ,sum([reserved_page_count]) as [reserved_page_count]
                    ,sum([row_count]) as [row_count]
                    FROM [sys].[dm_db_partition_stats]
                    group by [object_id],[index_id]
                ) ps
            on ps.object_id=i.object_id and ps.index_id=i.index_id
    group by s.name
    order by 1,2

--
-- size of partitioned table
select
        object_schema_name(i.object_id) as [schema]
        ,object_name(i.object_id) as [object]
        ,i.name as [index]
        ,ps.[used_page_count]
        ,ps.[reserved_page_count]
        ,ps.[row_count]
        ,s.name as [partition_scheme]
        ,f.name as [patition_function]
    from    sys.indexes i inner join sys.partition_schemes s
            on i.data_space_id = s.data_space_id 
            inner join sys.partition_functions f
            on f.function_id = s.function_id
            inner join
                (
                SELECT   
                    [object_id]
                    ,[index_id]
                    ,sum([used_page_count]) as [used_page_count]
                    ,sum([reserved_page_count]) as [reserved_page_count]
                    ,sum([row_count]) as [row_count]
                    FROM [sys].[dm_db_partition_stats]
                    group by [object_id],[index_id]
                ) ps
            on ps.object_id=i.object_id and ps.index_id=i.index_id
    order by 1,2,3

Inmon vs Kimball – Architecture Comparison and Data Valut Architect

1) A good slide show for reference:
http://www.slideshare.net/kgraziano/introduction-to-data-vault-modeling#btnNext

Inmon states that the data warehouse is:

Subject-oriented

The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.

Non-volatile

Data in the data warehouse are never over-written or deleted — once committed, the data are static, read-only, and retained for future reporting.

Integrated

The data warehouse contains data from most or all of an organization's operational systems and these data are made consistent.

Time-variant

For An operational system, the stored data contains the current value.

Kimball: DW is where we publish used data.

Data Valut by Dan:
Hub: Business Key
Link: represent 3nf many to many relation for Business key
Satellite: description for business key

2)
From:

http://oracledwbi.wordpress.com/2010/03/25/inmon-vs-kimball-architecture-comparison/

	Inmon – Hub & Spoke	Kimball – Bus Architecture
Methodology & Architecture
Overall approach	Top-down	Bottom-up
Architectural structure	Enterprise wide (atomic) data warehouse “feeds” departmental databases	Data marts model a single business process; enterprise consistency achieved through data bus and conformed dimensions
Complexity of the method	Quite complex	Fairly simple
Comparison with established development methodologies	Derived from the spiral methodology	Four-step process; a departure from RDBMS methods
Discussion of physical design	Fairly thorough	Fairly light
Scalability	Growing scope and changing requirements are critical	Need to adapt to highly volatile needs within a limited scope
Application	Strategic solution	Tactical solution
Information interdependence	Supported	Supported
Deployment cost	Higher start-up costs, with lower subsequent project development costs	Lower start-up costs, with each subsequent project costing about the same
Time to deploy	Longer start-up time	Quick start-up time
Development skills required	Larger team(s) of specialists	Small teams of generalists
Persistency of data	High rate of change from source systems	Source systems are relatively stable
Data modelling
Data orientation	Subject or data-driven	Process oriented
Tools	Traditional (ERDs, DISs)	Dimensional modelling
End-user accessibility	Low	High
Philosophy
Primary audience	IT professionals	End users
Place in the organisation	Corporate wide facility	Growth subject to departmental demand
Objective	Deliver a sound technical solution based on proven database methods and technologies	Deliver a solution that makes it easy for end users to directly query the data and still get reasonable response times