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Datawarehousebest practices,[object Object],Dr.  Eduardo Castro, MSc,[object Object],ecastro@simsasys.com,[object Object],http://ecastrom.blogspot.com,[object Object],http://comunidadwindows.org,[object Object],http://tiny.cc/comwindows,[object Object],Facebook: ecastrom,[object Object],Twitter: edocastro,[object Object]
Sources,[object Object],This presentation is based on the following sources,[object Object],Datawarehouse,[object Object],Ravi RanJan,[object Object],Top 10 Best Practices for Building a Large Scale Relational Data Warehouse,[object Object],SQL CAT,[object Object]
Complexities of Creating a Data Warehouse,[object Object],Incomplete errors ,[object Object],Missing Fields,[object Object],Records or Fields That, by Design, are not Being Recorded,[object Object],Incorrect errors,[object Object],Wrong Calculations, Aggregations,[object Object],Duplicate Records,[object Object],Wrong Information Entered into Source System,[object Object],Source. Datawarehouse. Ravi RanJan,[object Object]
Data Warehouse Pitfalls,[object Object],You are going to spend much time extracting, cleaning, and loading data,[object Object],You are going to find problems with systems feeding the data warehouse,[object Object],You will find the need to store/validate data not being captured/validated by any existing system,[object Object],Large scale data warehousing can become an exercise in data homogenizing,[object Object],Source. Datawarehouse. Ravi RanJan,[object Object]
Data Warehouse Pitfalls…,[object Object],The time it takes to load the warehouse will expand to the amount of the time in the available window... and then some,[object Object],You are building a HIGH maintenance system,[object Object],You will fail if you concentrate on resource optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer,[object Object],Source. Datawarehouse. Ravi RanJan,[object Object]
Best Practices,[object Object],Complete requirements and design,[object Object],Prototyping is key to business understanding,[object Object],Utilizing proper aggregations and detailed data,[object Object],Training is an on-going process,[object Object],Build data integrity checks into your system.,[object Object],Source. Datawarehouse. Ravi RanJan,[object Object]
Top 10 Best Practices for Building a Large Scale Relational Data Warehouse,[object Object],Building a large scale relational data warehouse is a complex task. ,[object Object],This section describes some design techniques that can help in architecting an efficient large scale relational data warehouse with SQL Server.,[object Object],Most large scale data warehouses use table and index partitioning, and therefore, many of the recommendations here involve partitioning. ,[object Object],Most of these tips are based on experiences building large data warehouses on SQL Server ,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Consider partitioning large fact tables ,[object Object],Consider partitioning fact tables that are 50 to 100GB or larger. ,[object Object],Partitioning can provide manageability and often performance benefits.,[object Object],Faster, more granular index maintenance.,[object Object],More flexible backup / restore options.,[object Object],Faster data loading and deleting,[object Object],Faster queries when restricted to a single partition..,[object Object],Typically partition the fact table on the date key.,[object Object],Enables sliding window.,[object Object],Enables partition elimination.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Build clustered index on the date key of the fact table,[object Object],This supports efficient queries to populate cubes or retrieve a historical data slice.,[object Object],If you load data in a batch window for the clustered index on the fact table then use the options 	ALLOW_ROW_LOCKS = OFF and 	ALLOW_PAGE_LOCKS = OFF ,[object Object],This helps speed up table scan operations during query time and helps avoid excessive locking activity during large updates.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Build clustered index on the date key of the fact table,[object Object],Build nonclustered indexes for each foreign key. ,[object Object],This helps ‘pinpoint queries' to extract rows based on a selective dimension predicate.,[object Object],Use filegroups for administration requirements such as backup / restore, partial database availability, etc.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Choose partition grain carefully,[object Object],Most customers use month, quarter, or year.,[object Object],For efficient deletes, you must delete one full partition at a time.,[object Object],It is faster to load a complete partition at a time.,[object Object],Daily partitions for daily loads may be an attractive option.,[object Object],However, keep in mind that a table can have a maximum of 1000 partitions.,[object Object],Partition grain affects query parallelism. ,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Choose partition grain carefully,[object Object],For SQL Server 2005:,[object Object],Queries touching a single partition can parallelize up to MAXDOP (maximum degree of parallelism). ,[object Object],Queries touching multiple partitions use one thread per partition up to MAXDOP.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Choose partition grain carefully,[object Object],For SQL Server 2008:,[object Object],Parallel threads up to MAXDOP are distributed proportionally to scan partitions, and multiple threads per partition may be used even when several partitions must be scanned.,[object Object],Avoid a partition design where only 2 or 3 partitions are touched by frequent queries, if you need MAXDOP parallelism (assuming MAXDOP =4 or larger). ,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Design dimension tables appropriately,[object Object],Use integer surrogate keys for all dimensions, other than the Date dimension. ,[object Object],Use the smallest possible integer for the dimension surrogate keys. This helps to keep fact table narrow.,[object Object],Use a meaningful date key of integer type derivable from the DATETIME data type (for example: 20060215).,[object Object],Don't use a surrogate Key for the Date dimension,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Design dimension tables appropriately,[object Object],Build a clustered index on the surrogate key for each dimension table,[object Object],Build a non-clustered index on the Business Key (potentially combined with a row-effective-date) to support surrogate key lookups during loads.,[object Object],Build nonclustered indexes on other frequently searched dimension columns.,[object Object],Avoid partitioning dimension tables.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Design dimension tables appropriately,[object Object],Avoid enforcing foreign key relationships between the fact and the dimension tables, to allow faster data loads.,[object Object],You can create foreign key constraints with NOCHECK to document the relationships; but don’t enforce them. ,[object Object],Ensure data integrity though Transform Lookups, or perform the data integrity checks at the source of the data.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Write effective queries for partition elimination,[object Object],Whenever possible, place a query predicate (WHERE condition) directly on the partitioning key (Date dimension key) of the fact table.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Use Sliding Window technique to maintain data,[object Object],Maintain a rolling time window for online access to the fact tables. Load newest data, unload oldest data.,[object Object],Always keep empty partitions at both ends of the partition range to guarantee that the partition split (before loading new data) and partition merge (after unloading old data) do not incur any data movement.,[object Object],Avoid split or merge of populated partitions. Splitting or merging populated partitions can be extremely inefficient, as this may cause as much as 4 times more log generation, and also cause severe locking.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Use Sliding Window technique to maintain data,[object Object],Create the load staging table in the same filegroup as the partition you are loading.,[object Object],Create the unload staging table in the same filegroup as the partition you are deleteing.,[object Object],It is fastest to load newest full partition at one time, but only possible when partition size is equal to the data load frequency (for example, you have one partition per day, and you load data once per day).,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Use Sliding Window technique to maintain data,[object Object],If the partition size doesn't match the data load frequency, incrementally load the latest partition. ,[object Object],Various options for loading bulk data into a partitioned table are discussed in the whitepaper ,[object Object],http://www.microsoft.com/technet/prodtechnol/sql/bestpractice/loading_bulk_data_partitioned_table.mspx.,[object Object],Always unload one partition at a time.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Efficiently load the initial data,[object Object],Use SIMPLE or BULK LOGGED recovery model during the initial data load.,[object Object],Create the partitioned fact table with the Clustered index.,[object Object],Create non-indexed staging tables for each partition, and separate source data files for populating each partition.,[object Object],Populate the staging tables in parallel.,[object Object],Use multiple BULK INSERT, BCP or SSIS tasks.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Efficiently load the initial data,[object Object],Create as many load scripts to run in parallel as there are CPUs, if there is no IO bottleneck. If IO bandwidth is limited, use fewer scripts in parallel.,[object Object],Use 0 batch size in the load. Use 0 commit size in the load. ,[object Object],Use TABLOCK.,[object Object],Use BULK INSERT if the sources are flat files on the same server. Use BCP or SSIS if data is being pushed from remote machines.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Efficiently load the initial data,[object Object],Build a clustered index on each staging table, then create appropriate CHECK constraints.,[object Object],SWITCH all partitions into the partitioned table.,[object Object],Build nonclustered indexes on the partitioned table.,[object Object],Possible to load 1 TB in under an hour on a 64-CPU server with a SAN capable of 14 GB/Sec throughput (non-indexed table). ,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Efficiently delete old data,[object Object],Use partition switching whenever possible.,[object Object],To delete millions of rows from nonpartitioned, indexed tables,[object Object],Avoid DELETE FROM ...WHERE ...,[object Object],Huge locking and logging issues ,[object Object],Long rollback if the delete is canceled,[object Object],Usually faster to ,[object Object],INSERT the records to keep into a non-indexed table,[object Object],Create index(es) on the table,[object Object],Rename the new table to replace the original,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Efficiently delete old data,[object Object],As an alternative, ‘trickle' deletes using the following repeatedly in a loop	DELETE TOP (1000) ... ; 	COMMIT,[object Object],Another alternative is to update the row to mark as deleted, then delete later during non critical time. ,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Manage statistics manually,[object Object],Statistics on partitioned tables are maintained for the table as a whole.,[object Object],Manually update statistics on large fact tables after loading new data.,[object Object],Manually update statistics after rebuilding index on a partition.,[object Object],If you regularly update statistics after periodic loads, you may turn off autostats on that table.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Manage statistics manually,[object Object],This is important for optimizing queries that may need to read only the newest data.,[object Object],Updating statistics on small dimension tables after incremental loads may also help performance. ,[object Object],Use FULLSCAN option on update statistics on dimension tables for more accurate query plans.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Consider efficient backup strategies ,[object Object],Backing up the entire database may take significant amount of time for a very large database.,[object Object],For example, backing up a 2 TB database to a 10-spindle RAID-5 disk on a SAN may take 2 hours (at the rate 275 MB/sec).,[object Object],Snapshot backup using SAN technology is a very good option.,[object Object],Reduce the volume of data to backup regularly.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]
Consider efficient backup strategies ,[object Object],The filegroups for the historical partitions can be marked as READ ONLY.,[object Object],Perform a filegroup backup once when a filegroup becomes read-only.,[object Object],Perform regular backups only on the read / write filegroups. ,[object Object],Note that RESTOREs of the read-only filegroups cannot be performed in parallel.,[object Object],Source. Top 10 Best Practices for Building  Large Scale Relational Data Warehouse SQL CAT,[object Object]

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