Microsoft. MCSA upgrade to SQL Server 2012 Certification Courseware. Version 1.0

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1 Microsoft MCSA upgrade to SQL Server 2012 Certification Courseware Version 1.0

2 SQL Server 2012 for Data Warehousing Appendix MSCA SQL Server 2012 Upgrade Course Body Temple 1 Deployment Models Package Deployment Model Project Deployment Model Body Temple 2 1

3 Package Deployment Model Storage MSDB File System Package Configurations Property values to be set dynamically at run time Package Deployment Utility Generate all required files for easier deployment Body Temple 3 Project Deployment Model The SSIS catalog Storage and management for SSIS projects on a SQL Server 2012 instance Folders A hierarchical structure for organizing and securing SSIS projects Body Temple 4 2

4 Deployment Model Comparison Feature Package Deployment Project Deployment Unit of Deployment Package Project Storage File system or MSDB SSIS Catalog Dynamic configuration Package configurations Environment variables mapped to project-level parameters and connection managers Compiled format Multiple.dtsx files Single.ispac file Troubleshooting Configure logging for each package SSIS catalog includes builtin reports and views Body Temple 5 Creating an SSIS Catalog Pre-requisites SQL Server 2012 SQL CLR enabled Creating a catalog Use SQL Server Management Studio One SSIS catalog per SQL Server instance Catalog Security Folder Security Object Security Catalog Encryption Sensitive Parameters Body Temple 6 3

5 Environments and Variables Environments Execution contexts for projects Variables Environment-specific values that can be mapped to project parameters and connection manager properties at run time Body Temple 7 Deploying an SSIS Project Integration Services Deployment Wizard SQL Server Data Tools SQL Server Management Studio Body Temple 8 4

6 Viewing Project Execution Information Integration Services Dashboard provides built-in reports Additional sources of information: Event Handlers Error Outputs Logging Debug Dump Files Body Temple 9 Data Quality Services Cleansing Data Matching Data Body Temple 10 5

7 What Is a Knowledge Base? Repository of knowledge about data: Domains define values and rules for each field Matching policies define rules for identifying duplicate records KB Body Temple 11 What Is a Domain? Domains are specific to a data field Domains contain the rules for the data KB Domains can be individual or composite City Name State First Name Family Name Body Temple 12 6

8 What Is a Reference Data Service? The Azure Marketplace hosts specialist data cleansing providers Set up an account KB Subscribe to a reference service Map your domain to the reference service Address Name First Name Family Name Body Temple 13 Creating a Data Cleansing Project 1. Select a knowledge base 2. Map columns to domains 3. Review suggestions and corrections 4. Export results Body Temple 14 7

9 Viewing Cleansed Data Output The values for all fields after data cleansing Source The original value for fields that were mapped to domains and cleansed Reason The reason the output value was selected by the cleansing operation Confidence An indication of the confidence Data Quality Services estimates for corrected values Status The status of the output column (correct or corrected) Body Temple 15 Using the Data Cleansing Data Flow Transformation Input columns to be cleansed Select knowledge base and map columns to domains Output cleansed columns Body Temple 16 8

10 Creating a Matching Policy Define matching rules for business entities Rules match entities based on domains: Similarity: Similar or exact match Weight: Percentage to apply if match succeeds Prerequisite: Mandatory domain match for rule to succeed If the combined weight of all matches meets or exceeds the rule s minimum matching score, the entities are duplicates Body Temple 17 Creating a Data Matching Project 1. Select a knowledge base 2. Map columns to domains 3. Review match clusters 4. Export matches and survivors Select survivorship rule: Pivot record Most complete and longest record Most complete record Longest record Body Temple 18 9

11 Viewing Data Matching Results Cluster ID Identifier for a cluster of matched records Record ID Identifier for a matched record Matching Rule The rule that produced the match Score Combined weighting of match criteria Pivot Mark A matched record chosen arbitrarily by Data Quality Services as the pivot record for a cluster Body Temple 19 Master Data Services Master Data Services v Data Quality Services Master Data Services Body Temple 20 10

12 Master Data Services and Data Quality Services Data Quality Services Domain-Focused Applicable to any dataset that contains domains in the KB Is Seattle a valid value for City? Master Data Services Entity-Focused Applicable to specific instances of business entities What is the correct name, address, and phone number for customer 1235? Use Data Quality Services to: Cleanse data before importing into master data hub Apply matching policies to find duplicate master data entities Body Temple 21 Components of Master Data Services Master Data Services database Contains the objects that support Master Data Services Master Data Manager Web application Enables administrators to create and manage Master Data Services objects, such as entities and attributes Enables data stewards to manage master data Master Data Services Configuration Manager Enables administrators to create the Master Data Services database and Web application Master Data Services Add-In for Microsoft Excel Enables administrators and data stewards to manage master data in Excel Data Steward Administrator Body Temple 11

13 What Is a Master Data Services Model? A versioned data model for specific business item or area of the business Contains definitions for entities required in the business area Often an entity with the same name as the model, as well as related entities Each entity has a defined set of attributes All entities have Code and Name attributes Attributes can be categorized in attribute groups Each instance of an entity is a known as a member Account Type Entity Attributes: Code (string) Name (string) Customer Entity Customers Model Version 1 Attributes: Code (free-form text) Name (free-form text) Address (free-form text) Phone (free-form text) AccountType (domain-based) CreditLimit (free-form number) Contact Details Attribute Group Member Code: 1 Name: Standard Member Code: 2 Name: Premier Member Code: 1235 Name: Ben Smith Address: 1 High St, Seattle Phone: AccountType: 1 CreditLimit: 1000 Version 2 Version Body Temple 23 Editing a Model in Microsoft Excel Use the Master Data Services Add-In for Excel to connect to a model Create entities Add columns to create attributes Edit entity member data in worksheets Publish changes to Master Data Services Body Temple 24 12

14 Hierarchies and Collections Derived Hierarchies Natural hierarchical groupings based on domain-based relationships Explicit Hierarchies Hierarchical groupings of specific members Can be mandatory or non-mandatory Collections Account Type Member Code: 1 Name: Standard Consolidated Member Code: CustUS Name: US Customers Consolidated Member Code: CustEU Name: European Customers Special Delivery Customers Customer Member Code: 1235 Name: Ben Smith Account Type: 1 Customer Member Code: 1267 Name: Amy Strande Account Type: 1 Leaf Member Code: 1235 Name: Ben Smith Leaf Member Code: 1267 Name: Amy Strande Leaf Member Code: 2214 Name: Sabina Schütz Non-hierarchical groupings of specific members Can include leaf and consolidated members from any hierarchy Leaf Member Code: 2600 Name: Andrew Sinclair Leaf Member Code: 1785 Name: Doris Krieger Consolidated Member Code: Cust-EU Name: European Customers Body Temple Finding Duplicate Members Create a Data Quality Services knowledge base with domains for entity attributes Create a matching policy to identify possible duplicates Match data in the Master Data Services Add-In for Excel Body Temple 26 13

15 Validating Members with Business Rules Define business rules with the business rule expression editor Publish business rules Use business rules to validate entity member data in: Explorer Excel Body Temple 27 Master Data Hub Architecture CRM SSIS Master Data Hub SSIS Other consumers (e.g. Data Warehouse ETL) SSIS SSIS Order Processing System Data Steward Marketing System Users insert and update data in application data stores Application data is loaded into master data hub for consolidation and management by data stewards Master data flows back to application data stores and other consumers across the enterprise Body Temple 14

16 Master Data Services Staging Tables MDS generates a staging table for each: Leaf member Consolidated member Relationship stg.entityname_leaf stg.entityname_consolidated stg.entityname_relationship ID ImportType ImportStatus_ID Batch_ID BatchTag ErrorCode Code Name NewCode <AttributeName> ID ImportType ImportStatus_ID Batch_ID BatchTag HierarchyName ErrorCode Code Name NewCode <AttributeName> ID RelationshipType ImportStatus_ID Batch_ID BatchTag HierarchyName ParentCode ChildCode SortOrder ErrorCode Body Temple Staging and Importing Data 1. Load data into staging tables Using SSIS, Import and Export Wizard, Transact-SQL, etc. 2. Run staging stored procedures stg.udp.entityname_leaf stg.udp.entityname_consolidated stg.udp.entityname_relationship 3. View import status in Master Data Manager 4. Match data to identify any duplicates 5. Apply business rules to validate data Body Temple 30 15

17 Consuming Master Data with Subscription Views Create subscription views in Master Data Manager Formats: Leaf Attributes Consolidated Attributes Collection Attributes Collections Explicit Parent Child Explicit levels Derived Parent Child Derived Levels Body Temple 31 Incremental ETL Overview of Data Warehouse Load Cycles Considerations for Incremental ETL Body Temple 32 16

18 Overview of Data Warehouse Load Cycles ETL process extracts new and modified data ETL process inserts or modifies data in the data warehouse based on changes Staging Database Data Warehouse Extract changes from data sources Refresh the data warehouse based on changes Users modify data in business applications Body Temple Considerations for Incremental ETL Data modifications to be tracked Load order Dimension keys Updating dimension members Updating fact records? Body Temple 34 17

19 Slowly Changing Dimensions Types of change to a dimension member: Type 1: Changing attributes are updated in the dimension record Key AltKey Name Phone City 101 C123 Mary New York Key AltKey Name Phone City 101 C123 Mary New York Type 2: Historical attribute changes result in a new record Key AltKey Name Phone City Current 101 C123 Mary New York True Key AltKey Name Phone City Current 101 C123 Mary New York False 102 C123 Mary Seattle True Type 3: The original and current values of historical attributes are stored in the dimension record Key AltKey Name Phone OriginalCity CurrentCity EffectiveDate 101 C123 Mary New York New York 1/1/00 Key AltKey Name Phone OriginalCity CurrentCity EffectiveDate 101 C123 Mary New York Seattle 6/7/ Body Temple Options for Extracting Modified Data Extract all records Store a primary key and checksum Use a datetime column as a high water mark Use Change Data Capture Use Change Tracking Body Temple 36 18

20 Extracting Rows Based on a Datetime Column Extraction Log Data Source Staging Database 1. Note the current time 2. Retrieve the last extraction time from an extraction log 3. Extract and transfer records that were modified between the last extraction and the current time 4. Replace the stored last extraction value with the current time Body Temple 37 Change Data Capture 1. Enable Change Data Capture EXEC sys.sp_cdc_enable_db EXEC = = = = 1 2. Map start and end times to log sequence numbers binary(10); = sys.fn_cdc_map_time_to_lsn('smallest greater = sys.fn_cdc_map_time_to_lsn('largest less than or 3. Handle null log sequence numbers IF IS NULL) OR IS NULL) -- There may have been no transactions in the timeframe 4. Extract changes between log sequence numbers SELECT * 'all') Body Temple 38 19

21 Data Flow Data Flow Extracting Data with Change Data Capture Extraction Log Data Source Staging Database 1. Identify the end-point for the extraction (LSN or DateTime) 2. Retrieve the last extraction end-point from an extraction log 3. Extract and transfer records that were modified during the LSN range defined by the previous extraction end-point and the current end-point 4. Replace the logged end-point value with the current end-point Body Temple 39 The CDC Control Task and Data Flow Components Initial Extraction Incremental Extraction CDC Control Mark Initial Load Start 1 1 CDC Control Get Processing Range 2 Source CDC State Variable CDC State Variable CDC 2 CDC Source CDC Splitter Staged Inserts CDC State Table 3 CDC Control Mark Initial Load End 3 Staged Inserts Staged Updates Staged Deletes 4 CDC Control Mark Processed Range 1. A CDC Control Task records the starting LSN 2. A data flow extracts all records 3. A CDC Control task records the ending LSN 1. CDC Control Task establishes the range of LSNs to be extracted 2. A CDC Source extracts records and CDC metadata 3. Optionally, a CDC Splitter splits the data flow into inserts, updates, and deletes 4. A CDC Control task records the ending LSN Body Temple 40 20

22 Change Tracking 1. Enable Change Tracking ALTER DATABASE Sales SET CHANGE_TRACKING = ON (CHANGE_RETENTION = 7 DAYS, AUTO_CLEANUP = ON) ALTER TABLE Salespeople ENABLE CHANGE_TRACKING WITH (TRACK_COLUMNS_UPDATED = OFF) 2. Record the current version and extract the initial data = CHANGE_TRACKING_CURRENT_VERSION(); SELECT * FROM Salespeople 3. Extract changes since the last extracted version, and then update the last extracted version = CHANGE_TRACKING_CURRENT_VERSION(); SELECT * FROM CHANGETABLE(CHANGES CT INNER JOIN Salespeople s ON CT.SalespersonID = s.salespersonid Tip: Use snapshot isolation to ensure consistency Body Temple 41 Extracting Data with Change Tracking Extraction Log Data Source Staging Database 1. Retrieve the last version number that was extracted from an extraction log 2. Extract and transfer records that were modified since the last version, retrieving the current version number 3. Replace the logged version number with the current version number Body Temple 42 21

23 Data Flow Data Flow Data Flow Options for Incrementally Loading Data Insert, Update, or Delete from CDC Output Tables Use a Lookup transformation Use the Slowly Changing Dimension transformation Use the MERGE statement Use a checksum Body Temple 43 Using CDC Output Tables Staging and Data Warehouse Co-located Remote Data Warehouse Staging DB Data Warehouse Staging DB Data Warehouse Execute SQL Task INSERT FROM Source Staged Inserts Destination Dimension Table Execute SQL Task UPDATE FROM JOIN ON BizKey Source Staged Updates OLE DB Command UPDATE Execute SQL Task DELETE WHERE BizKey IN or UPDATE FROM JOIN ON BizKey Source Staged Deletes OLE DB Command UPDATE or DELETE Body Temple 22

24 The Lookup Transformation Match No Match Lookup Update Insert Redirect non-matched rows to the no match output Look up extracted data in a dimension or fact table based on a business key or unique combination of keys If no match is found, insert a new record Optionally, if a match is found, update non-key columns to apply a type 1 change Body Temple 45 The Slowly Changing Dimension Transformation OLE DB Command to update existing record Changing Attributes (Type 1) Inferred members Derived Column to add current row indicator column Historical Attributes (Type 2) SCD OLE DB Command to set existing record s current row indicator to false New records Union All OLE DB Command to insert minimal record Derived Column sets current row indicator to True OLE DB Destination for new records Body Temple 46 23

25 source data The MERGE Statement Matches source and target rows Performs insert, update, or delete operations based on row The target table matching results being loaded MERGE INTO DW.dbo.FactSales as tgt USING (SELECT c.customerkey, s.orderno, s.quantity, s.amount FROM Staging.dbo.Sales s INNER JOIN DW.dbo.DimCustomer c ON s.customerid = c.customerbizkey) AS src (CustomerKey, OrderNo, Quantity, Amount) ON (src.customerkey = tgt.customerkey AND src.orderno = tgt.orderno) WHEN MATCHED THEN UPDATE SET Quantity = src.quantity, Amount = src.amount WHEN NOT MATCHED THEN INSERT (CustomerKey, OrderNo, Quantity, Amount) VALUES (src.customerkey, src.orderno, src.quantity, src.amount) Action when no existing records are found Matching criteria to identify existing records Body Temple 47 Action when existing records are found End of Presentation SQL Server 2012 for Data Warehousing Appendix MSCA SQL Server 2012 Upgrade Course Body Temple 48 24

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