Downloaded 13 times
![CREATE PROCEDURE [dbo].[usp_1]
WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
AS BEGIN ATOMIC WITH
(
TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE =
N'us_english'
)
SELECT c1, c2 from dbo.T1
END
GO
ALTER PROCEDURE [dbo].[usp_1]
WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
AS BEGIN ATOMIC WITH
(
TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE =
N'us_english'
)
SELECT c1 from dbo.T1
END
GO
You can now perform ALTER
operations on natively compiled
stored procedures using the
ALTER PROCEDURE statement
Use sp_recompile to
recompile stored procedures on
the next execution
Altering natively compiled stored procedures
Performance](https://image.slidesharecdn.com/microsoftiicicloseminario3evoluzionedeldatabase-170317135424/75/2-Ciclo-Microsoft-CRUI-3-Sessione-l-evoluzione-delle-piattaforme-tecnologiche-a-supporto-del-database-7-2048.jpg)
![Dynamic data masking walkthrough
ALTER TABLE [Employee] ALTER COLUMN [SocialSecurityNumber]
ADD MASKED WITH (FUNCTION = ‘SSN()’)
ALTER TABLE [Employee] ALTER COLUMN [Email]
ADD MASKED WITH (FUNCTION = ‘EMAIL()’)
ALTER TABLE [Employee] ALTER COLUMN [Salary]
ADD MASKED WITH (FUNCTION = ‘RANDOM(1,20000)’)
GRANT UNMASK to admin1
1) Security officer defines dynamic data masking policy in T-SQL over sensitive data in Employee table
2) Application user selects from Employee table
3) Dynamic data masking policy obfuscates the sensitive data in the query results
SELECT [Name],
[SocialSecurityNumber],
[Email],
[Salary]
FROM [Employee]
Security](https://image.slidesharecdn.com/microsoftiicicloseminario3evoluzionedeldatabase-170317135424/75/2-Ciclo-Microsoft-CRUI-3-Sessione-l-evoluzione-delle-piattaforme-tecnologiche-a-supporto-del-database-24-2048.jpg)
![Nurse
Database
One
Policy manager creates a filter predicate and security policy
Two
App user (e.g., nurse) selects from Patients table
Three
Security Policy transparently rewrites query to apply filter predicate
CREATE FUNCTION dbo.fn_securitypredicate(@wing int)
RETURNS TABLE WITH SCHEMABINDING AS
return SELECT 1 as [fn_securitypredicate_result] FROM
StaffDuties d INNER JOIN Employees e
ON (d.EmpId = e.EmpId)
WHERE e.UserSID = SUSER_SID() AND @wing = d.Wing;
CREATE SECURITY POLICY dbo.SecPol
ADD FILTER PREDICATE dbo.fn_securitypredicate(Wing) ON Patients
WITH (STATE = ON)
Filter
Predicate:
INNER
JOIN…
Security
Policy
Application
Patients
SELECT * FROM Patients
SELECT * FROM Patients
SEMIJOIN APPLY dbo.fn_securitypredicate(patients.Wing);
SELECT Patients.* FROM Patients,
StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId)
WHERE e.UserSID = SUSER_SID() AND Patients.wing = d.Wing;
Security
RLS in three steps
Policy Manager](https://image.slidesharecdn.com/microsoftiicicloseminario3evoluzionedeldatabase-170317135424/75/2-Ciclo-Microsoft-CRUI-3-Sessione-l-evoluzione-delle-piattaforme-tecnologiche-a-supporto-del-database-26-2048.jpg)
![-- The following syntax creates a security policy with a filter
predicate for the Customer table, and leaves the security policy
disabled
CREATE SECURITY POLICY [FederatedSecurityPolicy]
ADD FILTER PREDICATE
[rls].[fn_securitypredicate]([CustomerId])
ON [dbo].[Customer];
-- Create a new schema and predicate function, which will use the
application user ID stored in CONTEXT_INFO to filter rows.
CREATE FUNCTION rls.fn_securitypredicate (@AppUserId int)
RETURNS TABLE
WITH SCHEMABINDING
AS
RETURN (
SELECT 1 AS fn_securitypredicate_result
WHERE
DATABASE_PRINCIPAL_ID() = DATABASE_PRINCIPAL_ID('dbo') --
application context
AND CONTEXT_INFO() = CONVERT(VARBINARY(128), @AppUserId);
GO
Creates security policy for
row-level security
The following examples
demonstrate use of CREATE
SECURITY POLICY syntax
For an example of a complete
security policy scenario, see
Row-Level Security
Create security policy
Security](https://image.slidesharecdn.com/microsoftiicicloseminario3evoluzionedeldatabase-170317135424/75/2-Ciclo-Microsoft-CRUI-3-Sessione-l-evoluzione-delle-piattaforme-tecnologiche-a-supporto-del-database-27-2048.jpg)
Uploaded byJürgen Ambrosi
2° Ciclo Microsoft CRUI 3° Sessione: l'evoluzione delle piattaforme tecnologiche a supporto del database
The document provides a comprehensive technical overview of SQL Server 2016, highlighting its built-in features and enhancements such as Always On, in-memory OLTP, and temporal tables. It also covers security improvements like dynamic data masking and row-level security, alongside performance optimizations and integration with Azure services. Additionally, it details functionalities related to data management, big data integration via PolyBase, and new reporting capabilities in SQL Server Reporting Services (SSRS).
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2° Ciclo Microsoft CRUI 3° Sessione: l'evoluzione delle piattaforme tecnologiche a supporto del database
- 1. SQL Server 2016 EverythingBuilt-In – Technical Overview
- 2. SQL Server 2008 SQL Server 2008R2 SQL Server 2000XML ● KPIs Compression ● Policy-Based Mgmt ● Programmability PowerPivot ● SharePoint Integration ● Master Data Services SQL Server 2012 AlwaysOn ● ColumnStore Index ● Data Quality Services ● Power View ● Cloud Connectivity SQL Server 2014 In-Memory Across Workloads ● Performance & Scale ● Hybrid Cloud Optimized ● HDInsight ● Cloud BI Management Studio ● Mirroring SQL Server 2005 SQL Server 2016 Enhanced Always On ● In-Memory OLTP ● Stretch DB ● Temporal Tables ● Enhanced Backup to Cloud ● Polybase ● Real- Time Operational Analytics ● Row-Level Security ● Query Store ● R Services● Always Encrypted ● Mobile BI The evolution of Microsoft SQL Server
- 3. How we developSQL Cloud-first but not cloud-only Use SQL Database to improve core SQL Server features and cadence Many interesting and compelling on-premises cloud scenarios SQL Server and APS Azure SQL Virtual Machines Azure SQL Database Azure SQL Data Warehouse
- 4.
- 5.
- 6. ALTER TABLE Sales.SalesOrderDetail ALTERINDEX PK_SalesOrderID REBUILD WITH (BUCKET_COUNT=100000000) ALTER support Full schema change support: add/alter/drop column/constraint Add/drop index supported Performance The ALTER TABLE syntax is used for making changes to the table schema, as well as for adding, deleting, and rebuilding indexes Indexes are considered part of the table definition Key advantage is the ability to change the BUCKET_COUNT with an ALTER INDEX statement
- 7. CREATE PROCEDURE [dbo].[usp_1] WITHNATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER AS BEGIN ATOMIC WITH ( TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english' ) SELECT c1, c2 from dbo.T1 END GO ALTER PROCEDURE [dbo].[usp_1] WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER AS BEGIN ATOMIC WITH ( TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english' ) SELECT c1 from dbo.T1 END GO You can now perform ALTER operations on natively compiled stored procedures using the ALTER PROCEDURE statement Use sp_recompile to recompile stored procedures on the next execution Altering natively compiled stored procedures Performance
- 8. Transact-SQL Full support forall Collation and Unicode Support (var)char columns can use any code page supported by SQL Server Character columns in index keys can use any SQL Server collation Expressions in natively compiled modules as well as constraints on memory- optimized tables can use any SQL Server collation Scalar User-Defined Functions for In-Memory OLTP Create, drop, and alter natively compiled, scalar user-defined functions Native compilation improves performance of the evaluation of UDFs in T-SQL Performance
- 9. Cross-Feature Support System-Versioned TemporalTables Query Store Row-Level Security (RLS) Multiple Active Result Sets (MARS) Transparent Data Encryption (TDE) Performance
- 10. In SQL Server2016, the storage for memory- optimized tables will be encrypted as part of enabling TDE on the database Simply follow the same steps as you would for a disk-based database Support for Transparent Data Encryption (TDE) Windows Operating System Level Data Protection SQL Server Instance Level User Database Level Database Encryption Key Service Master Key DPAPI encrypts the Service Master Key Master Database Level Database Encryption Key Service Master Key Encrypts the Database master Key for the master Database Database Master Key of the master Database creates a certificate in the master database The certificate encrypts the database Encryption Key in the user database The entire user database is secured by the Datbase Encryption Key (DEK) of the user database by using transparent database encryption Created at a time of SQL Server setup Statement: CREAT MASTER KEY… Statement: CREATE CERTIFICATE… Statement: CREATE DATABASE ENCRYPTION KEY… Statement: ALTER DATABSE… SET ENCRYPTION Performance
- 11. New Transaction PerformanceAnalysis Overview report New report replaces the need to use the Management Data Warehouse to analyze which tables and stored procedures are candidates for in-memory optimization Performance
- 12. Temporal Tables Query backin time
- 13. Data changes overtime Tracking and analyzing changes is often important Temporal in DB Automatically tracks history of data changes Enables easy querying of historical data states Advantages over workarounds Simplifies app development and maintenance Efficiently handles complex logic in DB engine Why temporal Time travel Data audit Slowly changing dimensions Repair record-level corruptions Performance
- 14. No change inprogramming model New Insights INSERT / BULK INSERT UPDATE DELETE MERGE DML SELECT * FROM temporal Querying How to start with temporal CREATE temporal TABLE PERIOD FOR SYSTEM_TIME… ALTER regular_table TABLE ADD PERIOD… DDL FOR SYSTEM_TIME AS OF FROM..TO BETWEEN..AND CONTAINED IN Temporal Querying ANSI 2011 compliant Performance
- 15. Temporal table (actualdata) Insert / Bulk Insert * Old versions Update */ Delete * How does system-time work? History table Performance
- 16. Temporal table (actualdata) Temporal queries * (Time travel, etc.) How does system-time work? History table Regular queries (current data) * Include historical version Performance
- 17.
- 18. Data disclosure prevention Client-side encryptionof sensitive data using keys that are never given to the database system Queries on encrypted data Support for equality comparison, including join, group by, and distinct operators Application transparency Minimal application changes via server and client library enhancements Allows customers to securely store sensitive data outside of their trust boundary. Data remains protected from high-privileged, yet unauthorized, users. The need for Always Encrypted Security
- 19. trust boundary "SELECT NameFROM Customers WHERE SSN = @SSN","111-22-3333" How it works Help protect data at rest and in motion, on-premises and in the cloud Name Wayne Jefferson ADO .NET Name 0x19ca706fbd9a Result SetResult Set Client Name SSN Country 0x19ca706fbd9a 0x7ff654ae6d USA SQL Server or SQL Database "SELECT Name FROM Customers WHERE SSN = @SSN",0x7ff654ae6d ciphertext Encrypted sensitive data and corresponding keys are never seen in plaintext in SQL Server dbo.Customers ciphertext Security
- 20. Randomized encryption Encrypt('123-45-6789') =0x17cfd50a Repeat: Encrypt('123-45-6789') = 0x9b1fcf32 Allows for transparent retrieval of encrypted data but NO operations More secure Deterministic encryption Encrypt('123-45-6789') = 0x85a55d3f Repeat: Encrypt('123-45-6789') = 0x85a55d3f Allows for transparent retrieval of encrypted data AND equality comparison E.g. in WHERE clauses and joins, distinct, group by Two types of encryption available Randomized encryption uses a method that encrypts data in a less predictable manner Deterministic encryption uses a method that always generates the same encrypted value for any given plaintext value Types of encryption for Always Encrypted Security
- 21.
- 22. Configuration made easyin new Azure portal Policy-driven at table and column level, for defined set of users Data masking applied in real time to query results based on policy Multiple masking functions available, such as full or partial, for various sensitive data categories (credit card numbers, SSN) SQL Database SQL Server 2016 Table.CreditCardNo 4465-6571-7868-5796 4468-7746-3848-1978 4484-5434-6858-6550 Real-time data masking, partial masking Prevent abuse of sensitive data by hiding it from users Security Dynamic Data Masking
- 23. Regulatory compliance Sensitive data protection Agility and transparency Datais masked on the fly, with underlying data in database remaining intact (transparent to application and applied according to user privilege) Limit access to sensitive data by defining policies to obfuscate specific database fields, without affecting database integrity Security Benefits of Dynamic Data Masking
- 24. Dynamic data maskingwalkthrough ALTER TABLE [Employee] ALTER COLUMN [SocialSecurityNumber] ADD MASKED WITH (FUNCTION = ‘SSN()’) ALTER TABLE [Employee] ALTER COLUMN [Email] ADD MASKED WITH (FUNCTION = ‘EMAIL()’) ALTER TABLE [Employee] ALTER COLUMN [Salary] ADD MASKED WITH (FUNCTION = ‘RANDOM(1,20000)’) GRANT UNMASK to admin1 1) Security officer defines dynamic data masking policy in T-SQL over sensitive data in Employee table 2) Application user selects from Employee table 3) Dynamic data masking policy obfuscates the sensitive data in the query results SELECT [Name], [SocialSecurityNumber], [Email], [Salary] FROM [Employee] Security
- 25. Row-Level Security SQL Server2016 SQL Database
- 26. Nurse Database One Policy manager createsa filter predicate and security policy Two App user (e.g., nurse) selects from Patients table Three Security Policy transparently rewrites query to apply filter predicate CREATE FUNCTION dbo.fn_securitypredicate(@wing int) RETURNS TABLE WITH SCHEMABINDING AS return SELECT 1 as [fn_securitypredicate_result] FROM StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId) WHERE e.UserSID = SUSER_SID() AND @wing = d.Wing; CREATE SECURITY POLICY dbo.SecPol ADD FILTER PREDICATE dbo.fn_securitypredicate(Wing) ON Patients WITH (STATE = ON) Filter Predicate: INNER JOIN… Security Policy Application Patients SELECT * FROM Patients SELECT * FROM Patients SEMIJOIN APPLY dbo.fn_securitypredicate(patients.Wing); SELECT Patients.* FROM Patients, StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId) WHERE e.UserSID = SUSER_SID() AND Patients.wing = d.Wing; Security RLS in three steps Policy Manager
- 27. -- The followingsyntax creates a security policy with a filter predicate for the Customer table, and leaves the security policy disabled CREATE SECURITY POLICY [FederatedSecurityPolicy] ADD FILTER PREDICATE [rls].[fn_securitypredicate]([CustomerId]) ON [dbo].[Customer]; -- Create a new schema and predicate function, which will use the application user ID stored in CONTEXT_INFO to filter rows. CREATE FUNCTION rls.fn_securitypredicate (@AppUserId int) RETURNS TABLE WITH SCHEMABINDING AS RETURN ( SELECT 1 AS fn_securitypredicate_result WHERE DATABASE_PRINCIPAL_ID() = DATABASE_PRINCIPAL_ID('dbo') -- application context AND CONTEXT_INFO() = CONVERT(VARBINARY(128), @AppUserId); GO Creates security policy for row-level security The following examples demonstrate use of CREATE SECURITY POLICY syntax For an example of a complete security policy scenario, see Row-Level Security Create security policy Security
- 28. Capability Row-Level Security providesfine-grained access control over rows in a table based on conditions you set up Benefits Store data for many users in same databases and tables while limiting access by other users who share same tables Security Summary: Row-Level Security
- 29. Security enhancements Built-in toolsfor enabling compliance: SQL Server audit tools Create server audits, with server audit specifications (audited events can be written to event logs or to audit files) User-defined audit: Allows middle-tier application to write custom events into audit log, which enables more flexibility to store audit information Audit filtering: Provides greater flexibility to filter wanted events in audit log Audit resilience: Audit logging is now tolerant to loss of connectivity to target directory and will recover automatically once network connection is re-established Security
- 30. Azure Key Vaultsupport Azure Key Vault: Central key management that leverages hardware security modules (HSMs), separation of key management from data management Support for AKV available through SQL Server Connector for AKV Extensible Key Management (EKM) provider for SQL Server Leverage Azure Key Vault for managing encryption keys Both on-premises and SQL Server-in-a-VM users can assume control of encryption keys for Transparent Data Encryption (TDE), Column Level Encryption (CLE), and Backup Encryption while leveraging additional security benefits of Azure Key Vault Security
- 31. Transparent Data Encryption(TDE) Encrypt data when it is stored on disk, and decrypt it when read into memory Developers can encrypt database files, log files, and backup files without changing existing applications Intel AES-NI hardware encryption acceleration Support for storage of memory-optimized OLTP tables (new) Exclusive to SQL Server Enterprise edition Security
- 32. Transparent Data Encryption(TDE) Encrypt backup by specifying encryption algorithm and encryptor Supports on-premises and Azure storage locations Configurable for Managed Backup to Windows Azure Backup encryption now supported with compression, using AES- NI hardware acceleration Security
- 33. Summary: Security Always encrypted:Sensitive data always encrypted (and queryable) Dynamic Data Masking: Real-time obfuscation of data Row-Level Security: Fine-grained access control of table rows Audit success/failure of database operations TDE support for storage of In-Memory OLTP tables Enhanced auditing for OLTP with ability to track history of record changes Security
- 34. AlwaysOn Failover onSQL Server instance level Shared storage (SAN/SMB) Failover can take minutes based on load Multi-node clustering Passive secondary nodes Failover on database level Direct attached storage Failover takes seconds Multiple secondaries Active secondaries Availability Failover Cluster Instances for servers Availability Groups for groups of databases
- 35. WSFC node WSFCnode Failover Cluster Instances Server failover Shared storage Multi-node clustering Passive secondary nodes Failover in minutes SQL Server 2016 Shared storage SQL Server 2016SQL Server Failover Cluster Instance Availability
- 36. Availability Group Listener Availability Groups Multi-databasefailover Direct attached storage Multiple secondaries Active secondaries Failover in seconds SQL Server 2016 SQL Server 2016SQL Server 2016 Availability
- 37. Availability Groups +Failover Clustering AlwaysOn: Failover Cluster Instances and Availability Groups work together to ensure data is accessible despite failures Availability Windows Server Failover Clustering (WSFC) Cluster Network Subnet Network Subnet Node NodeNodeNodeNode SQL Server Instance SQL Server Instance SQL Server Instance AlwaysOn SQL Server Failover Cluster Instance Primary Replica Secondary Replica Secondary Replica Secondary Replica AlwaysOn Availability Group Instance Network Name WSFC Configuration WSFC Configuration WSFC Configuration WSFC Configuration WSFC Configuration Instance Network Name Instance Network Name Instance Network Name Availability Group Listener Virtual Network Name Storage Storage Storage Share Storage
- 38.
- 39. Interest in bigdata spurs customer demand Adoption of big data technologies like Hadoop Increase in number and variety of data sources that generate large quantities of data Realization that data is “too valuable” to delete Dramatic decline in hardware cost, especially storage $
- 40. PolyBase and queries Providesa scalable, T-SQL-compatible query processing framework for combining data from both universes Access any data
- 41. PolyBase View Execute T-SQLqueries against relational data in SQL Server and semi-structured data in Hadoop or Azure Blob Storage Leverage existing T-SQL skills and BI tools to gain insights from different data stores Access any data PolyBase View in SQL Server 2016
- 42. Access any data PolyBaseuse cases
- 43. Components introduced inSQL Server 2016 PolyBase Engine Service PolyBase Data Movement Service (with HDFS Bridge) External table constructs MR pushdown computation support Access any data Head Node SQL 2016 PolyBase Engine PolyBase DMS
- 44. Step 1: Setup a Hadoop cluster… Hortonworks or Cloudera distributions Hadoop 2.0 or above Linux or Windows On-premises or in Azure Access any data Hadoop Cluster Namenode Datanode Datanode Datanode Datanode File System AB 01 01 01 01 File System File System File System
- 45. Step 1: …orset up an Azure Storage Blob Azure Storage Blob (ASB) exposes an HDFS layer PolyBase reads and writes from ASB using Hadoop RecordReader/RecordWrite No compute pushdown support for ASB Access any data Azure Azure Storage Volume Azure Storage Volume Azure Storage Volume
- 46. Access any data Queryrelational and non-relational data, on-premises and in Azure T-SQL query Apps SQL Server Hadoop Summary: PolyBase Query relational and non-relational data with T-SQL
- 47.
- 48. Scale and manage Increased productivity AnalysisServices themes for SQL Server 2016 Improved productivity and performance
- 49. Analysis Services Overview AnalysisServices (SSAS) is an online analytical data engine used in decision support and business analytics It provides analytical data for business reports and client applications such as Reporting Services reports, Power BI, Excel, and other third-party data visualization tools SSAS supports two modelling modes—multidimensional and tabular The multidimensional mode also includes a data mining engine
- 50. Analysis Services Overview OneSemantic Model - Two Ways to Develop Dimensions and measure groups Highly scalable and mature Feature rich and complex Multidimensional models Tables and relationships Fast by design with in-memory Easy to get started and simple Tabular models
- 51. Analysis Services ArchitecturalOverview BI SEMANTIC MODEL Data access Use SQL Server Data Tools for Visual Studio to create BI semantic models DATA SOURCES CLIENT TOOLS Data model Business logic Queries
- 52. Reporting Services Overview ReportingServices (SSRS) delivers enterprise, web-enabled reporting functionality Implemented as a Report Server, in one of two modes—Native and SharePoint—it can scale to support thousands of users SSRS 2016 is extended with a new capability to deliver mobile reports, and is enhanced with a new modern web portal To contrast classic RDL reports with the new mobile reports, SSRS reports are now referred to as either paginated or mobile
- 53. Reporting Services architecture WebAPIs Report server database Web portal Web browsers Power BI Mobile Report Builder Mobile Report Publisher Visual Studio SQL Server SQL Server Analysis Services Oracle Teradata ODBC, OLE DB More/custom Processing and rendering Scheduling and delivery Email File share SharePoint Power BI Custom REPORT VIEWING (PCs, mobile) REPORT DESIGN (PCs) REPORT SERVER DATA SOURCES REPORT DELIVERY DESTINATIONS Custom apps/LOB
- 54. SQL Server 2016 Enhancedanalysis Modern reports Rich tools Mobile reports Hybrid BI SharePoint 2016 support
- 55. New SSRS Features Supportsthe current versions of Microsoft.NET Framework 4 This includes 4.0 and 4.5.1 If no version of .NET Framework 4.x is installed, SQL Server setup installs .NET 4.0 during the feature installation Render paginated reports as PowerPoint files Text boxes and images render as PowerPoint text boxes and images Data visualizations render as images The PowerPoint slide layout can be adjusted to suit, by moving and resizing individual objects
- 56. New SSRS Features Abilityto deliver subscriptions by using a single file share account, reusable across multiple subscriptions Pin report items to Power BI dashboards Requires registering the Report Server with Power BI Can pin charts, gauge panels, maps and images, and configure how often the data is refreshed Dashboard tile refresh is achieved with subscriptions Supported by all SQL Server editions Support of SharePoint mode for SharePoint 2016
- 57. New SSRS Features Paginatedreport authoring: Custom parameter pane, allowing design-time control over report parameter layout Two new chart types: Tree Map and Sunburst charts, which effectively visualize hierarchical data Report Builder Now has a new modern look and feel, with streamlined UI elements Supports High DPI (Dots Per Inch) scaling and devices
- 58. New SSRS Features Updateddata source support: Oracle Database 12c (uses ODP.NET) Oracle Essbase 11 SAP BW 7.5 Teradata 15 Personalized connection strings: Enables data sources to pass current user name as custom data Data Source=localhost;Initial Catalog=Sales Analysis;CustomData={{ UserID }}
- 59. New SSRS Features KeyPerformance Indicators (KPIs): Deliver visual cues that communicate the amount of progress made toward a goal Typically valuable for teams, managers, and businesses by enabling them to quickly evaluate the progress made toward measurable goals Developed in, and displayed by, the new SSRS web portal Supported by Enterprise edition, and Native mode only
- 60. New SSRS Features KeyPerformance Indicators (KPIs): Enable defining value, goal, status and trend metrics The value, goal and status metrics are single values—typically numeric, though text is also possible The trend metric is designed to use a time series (e.g. monthly values) Values can be sourced from shared datasets, or by manually entered values Datasets can be parameterized Datasets must use caching Cache plans should be used to schedule the refresh Can be configured to launch related content—either a mobile report, or a custom URL The status colors—by default—are green (1), amber (0), or red (-1), and these can be overridden by custom branding
- 61. New SSRS Features Theweb portal is a new HTML5 application that targets modern web standards and modern browsers Report Manager has been removed and the web portal is now the default used to browse/manage/consume the Report Server catalog Stores, manages and secures reports and related assets: Paginated reports Mobile reports Key Performance Indicators (KPIs) Power BI Desktop files Excel workbooks
- 62. New SSRS Features Supportscustom branding, allowing the customization of logo, web portal colors and a mobile report theme
- 63. New SSRS Features PDFreplaces ActiveX for remote printing (plug-in free printing) New enhanced subscription management: Easily enable/disable Provide a description User can change the owner
- 64. Create interactive mobilereports Responsive experiences for phones and tablets
- 65. Consume mobile reports Nativeapps for all major mobile platforms One mobile app for Power BI and SSRS mobile reports Native mobile experience optimized for touch Keep data up-to-date with real-time query or scheduled data refresh Interact with mobile reports even when disconnected
- 66.
- 67. Revolution R Enterpriseand SQL Big data analytics platform Based on open source R High-performance, scalable, full-featured Statistical and machine-learning algorithms are performant, scalable, and distributable Write once, deploy anywhere Scripts and models can be executed on a variety of platforms, including non-Microsoft (Hadoop, Teradata in-DB) Integration with the R Ecosystem Analytic algorithms accessed via R function with similar syntax for R users (with arbitrary R functions/packages) Advanced analytics Data source integration Parallel external-memory algorithm library Data Compute context integration Resources Requests R scripts + CRAN algorithms
- 68. SQL Server 2016R integration scenario Exploration Use Revolution R Enterprise (RRE) from R integrated development environment (IDE) to analyze large data sets and build predictive and embedded models with compute on SQL Server machine (SQL Server compute context) Operationalization Developer can operationalize R script/model over SQL Server data by using T-SQL constructs DBA can manage resources, plus secure and govern R runtime execution in SQL Server Advanced analytics
- 69. R script usagefrom SQL Server Original R script: IrisPredict <- function(data, model){ library(e1071) predicted_species <- predict(model, data) return(predicted_species) } library(RODBC) conn <- odbcConnect("MySqlAzure", uid = myUser, pwd = myPassword); Iris_data <-sqlFetch(conn, "Iris_Data"); Iris_model <-sqlQuery(conn, "select model from my_iris_model"); IrisPredict (Iris_data, model); Calling R script from SQL Server: /* Input table schema */ create table Iris_Data (name varchar(100), length int, width int); /* Model table schema */ create table my_iris_model (model varbinary(max)); declare @iris_model varbinary(max) = (select model from my_iris_model); exec sp_execute_external_script @language = 'R' , @script = ' IrisPredict <- function(data, model){ library(e1071) predicted_species <- predict(model, data) return(predicted_species) } IrisPredict(input_data_1, model); ' , @parallel = default , @input_data_1 = N'select * from Iris_Data' , @params = N'@model varbinary(max)' , @model = @iris_model with result sets ((name varchar(100), length int, width int , species varchar(30))); Values highlighted in yellow are SQL queries embedded in the original R script Values highlighted in aqua are R variables that bind to SQL variables by name Advanced analytics
- 70. Capability Extensible in-database analytics,integrated with R, exposed through T-SQL Centralized enterprise library for analytic models Benefits SQL Server Analytical engines Integrate with R Become fully extensible Data management layer Relational data Use T-SQL interface Stream data in-memory Analytics library Share and collaborate Manage and deploy R + Data Scientists Business Analysts Publish algorithms, interact directly with data Analyze through T-SQL, tools, and vetted algorithms DBAs Manage storage and analytics together Summary: R integration and advanced analytics Advanced analytics
- 71.
- 72. Ever-growing data, ever-shrinkingIT What to do? Expand server and storage Move data elsewhere Delete Massive tables (hundreds of millions/billions of rows, TBs size) Users want/need to retain data indefinitely Cold data infrequently accessed but must be online Datacenter consolidation Maintenance challenges Business SLAs at risk Hybrid solutions
- 73. Capability Stretch large operationaltables from on-premises to Azure with the ability to query Benefits Stretch SQL Server into Azure Securely stretch cold tables to Azure with remote query processing SQL SERVER 2016 Azure Hybrid solutions
- 74. Stretch Database architecture Howit works Creates a secure linked server definition in the on-premises SQL Server Targets remote endpoint with linked server definition Provisions remote resources and begins to migrate eligible data, if migration is enabled Queries against tables run against both local database and remote endpoint Remote endpoint Remote data Azure Internetboundary Local database Local data Eligible data Linked servers Hybrid solutions
- 75. Queries continue working Businessapplications continue working without disruption DBA scripts and tools work as before (all controls still held in local SQL Server) Developers continue building or enhancing applications with existing tools and methods Hybrid solutions
- 76. Advanced security featuressupported Data in motion always via secure channels (TLS 1.1/1.2) Always Encrypted supported if enabled by user (encryption key remains on-premises) Row-Level Security and Auditing supported Hybrid solutions
- 77. Backup and restorebenefits Hybrid solutions DBAs only back up/restore local SQL Server hot data StretchDB ensures remote data is transactionally consistent with local Upon completion of local restore, SQL Server reconciles with remote using metadata operation, not data copy Time of restore for remote not dependent on size of data
- 78. Order history Name SSNDate Jane Doe cm61ba906fd 2/28/2005 Jim Gray ox7ff654ae6d 3/18/2005 John Smith i2y36cg776rg 4/10/2005 Bill Brown nx290pldo90l 4/27/2005 Sue Daniels ypo85ba616rj 5/12/2005 Sarah Jones bns51ra806fd 5/22/2005 Jake Marks mci12hh906fj 6/07/2005 Eric Mears utb76b916gi 6/18/2014 Rachel Hogan px61hi9306fj 7/1/2014 Sam Johnson ol43bi506gd 7/12/2014 David Simon tx83hal916fi 7/29/2014 Order history Name SSN Date Jane Doe cm61ba906fd 2/28/2005 Jim Gray ox7ff654ae6d 3/18/2005 John Smith i2y36cg776rg 4/10/2005 Bill Brown nx290pldo90l 4/27/2005 Customer data Product data Order History Stretch to cloud App Query Microsoft Azure Jim Gray ox7ff654ae6d 3/18/2005 Hybrid solutions
Editor's Notes
- #3 Speaker notes: This slide shows the evolution of SQL server overtime. Today SQL Server is leader in Database Management System according to Gartner. Microsoft has made tremendous investments in the platform, in order to make SQL Server 2016 the fastest release ever produced. SQL Server 2016 has been in production in Microsoft Cloud through Azure SQL Database for more than 1 year and now is available to customers and partners to run on-premises or in Service Providers’ datacenter.
- #22 Source: https://msdn.microsoft.com/en-us/library/dn269114(v=sql.130).aspx In SQL Server 2016 you can perform ALTER operations on memory-optimized tables by using the ALTER TABLE statement. The database application can continue to run, and any operation that is accessing the table is blocked until the alteration process is completed.
- #23 Source: https://msdn.microsoft.com/en-us/library/dn957475(v=sql.130).aspx In SQL Server 2016 , you can perform ALTER operations on natively compiled stored procedures using the ALTER PROCEDURE statement. In the previous release of SQL Server, you had to complete manual steps to modify natively compiled stored procedures. When executing ALTER PROCEDURE on a natively compiled stored procedure, the procedure is recompiled using a new definition. While recompilation is in progress, the old version of the procedure continues to be available for execution. Once compilation completes, procedure executions are drained, and the new version of the procedure is installed.When you alter a natively compiled stored procedure, you can modify the following options. Parameter EXECUTE AS TRANSACTION ISOLATION LEVEL LANGUAGE DATEFIRST DATEFORMAT DELAYED_DURABILITY For more information on ALTER PROCEDURE functionality and syntax, see ALTER PROCEDURE (Transact-SQL) You can execute sp_recompile on a natively compiled stored procedure, which causes the stored procedure to recompile on the next execution.
- #24 https://msdn.microsoft.com/en-us/library/dn935012.aspx The sp_recompile (Transact-SQL)stored procedure can be used with the natively compiled, scalar user-defined function. It will result in the function being recompiled using the definition that exists in metadata.
- #25 Support for using temporal system-versioning with In-Memory OLTP. For more information, see https://msdn.microsoft.com/en-us/library/mt590207.aspx Query store support for natively compiled code from In-Memory OLTP workloads. For more information, see Using the Query Store with In-Memory OLTP https://msdn.microsoft.com/en-us/library/mt590480.aspx Row-Level Security in Memory-Optimized Tables https://msdn.microsoft.com/en-us/library/dn511014.aspx#rls
- #26 Source: https://msdn.microsoft.com/en-us/library/dn688968(v=sql.130).aspx Encryption In SQL Server 2016, the storage for memory-optimized tables will be encrypted as part of enabling TDE on the database. For more information, see Transparent Data Encryption (TDE).
- #27 Note that in SQL Server 2014 you needed to set up a Management Data Warehouse (MDW) in order to run the In-Memory OLTP reports. SQL Server 2016 now includes lightweight reports available in SSMS without setting up MDW. Open SSMS and right click on the AdventureWorks2016 database and select Reports | Standard Reports | Transaction Performance Analysis Overview. Select the Tables Analysis report link. This presents you with a prioritized plot chart that graphs the amounts of potential gain against the potential amount of work to migrate the top tables to In-Memory OLTP. This analysis is based upon the workload that we just ran with the batch file. This helps you see the highest potential gain with the lowest potential amount of migration work required. Select the SalesOrderDetail_ondisk to explore further. Note that this report contains information on latch statistics, lock statistics, recommended In-Memory index type, and more. Click the Navigate Backwards button twice in the upper left of the report bar (see illustration above) to go back to the main Report and click on Stored Procedure Analysis.
- #29 Source: https://msdn.microsoft.com/en-us/library/dn935015(v=sql.130).aspx A temporal table is a new type of table that provides correct information about stored facts at any point in time. Each temporal table consists of two tables actually, one for the current data and one for the historical data. The system automatically ensures that when the data changes in the table with the current data, the previous values are stored in the historical table. Querying constructs are provided to hide this complexity from users. For more information, see Temporal Tables. Introduction to Key Components and Concepts What is a Temporal Table? A temporal table is a table for which a PERIOD definition exists and which contains system columns with a datatype of datetime2 into which the period of validity is recorded by the system, and which has an associated history table into which the system records all prior versions of each record with their period of validity. With a temporal table, the value of each record at any point in time can be determined, rather than just the current value of each record. A temporal table is also referred to as a system-versioned table. Why Temporal? Real data sources are dynamic and more often than not business decisions rely on insights that analysts can get from data evolution. Use cases for temporal tables include: Understanding business trends over time Tracking data changes over time Auditing all changes to data Maintaining a slowly changing dimension for decision support applications Recovering from accidental data changes and application errors
- #30 Source: https://msdn.microsoft.com/en-us/library/dn935015(v=sql.130).aspx How Does Temporal Work? System-versioning for a table is implemented as a pair of tables, a current table and a history table. Within each of these tables, two additional datetime (datetime2 datatype) columns are used to define the period of validity for each record – a system start time (SysStartTime) column and a system end time (SysEndTime) column. The current table contains the current value for each record. The history table contains the each previous value for each record, if any, and the start time and end time for the period for which it was valid. INSERTS: On an INSERT, the system sets the value for the SysStartTime column to the UTC time of the current transaction based on the system clock and assigns the value for the SysEndTime column to the maximum value of 9999-12-31 – this marks the record as open. UPDATES: On an UPDATE, the system stores the previous value of the record in the history table and sets the value for the SysEndTime column to the UTC time of the current transaction based on the system clock. This marks the record as closed, with a period recorded for which the record was valid. In the current table, the record is updated with its new value and the system sets the value for the SysStartTime column to the UTC time for the transaction based on the system clock. The value for the updated record in the current table for the SysEndTime column remains the maximum value of 9999-12-31. DELETES: On a DELETE, the system stores the previous value of the record in the history table and sets the value for the SysEndTime column to the UTC time of the current transaction based on the system clock. This marks this record as closed, with a period recorded for which the previous record was valid. In the current table, the record is removed. Queries of the current table will not return this value. Only queries that deal with history data return data for which a record is closed. MERGE: On a MERGE, MERGE behaves as an INSERT, an UPDATE, or a DELETE based on the condition for each record.
- #31 Source: https://msdn.microsoft.com/en-us/library/dn935015(v=sql.130).aspx The SYSTEM_TIME period columns used to record the SysStartTime and SysEndTime values must be defined with a datatype of datetime2.
- #32 Source: https://msdn.microsoft.com/en-us/library/dn935015(v=sql.130).aspx The SYSTEM_TIME period columns used to record the SysStartTime and SysEndTime values must be defined with a datatype of datetime2.
- #36 Source: https://msdn.microsoft.com/en-us/library/mt163865(v=sql.130).aspx Selecting Deterministic or Randomized Encryption Always Encrypted supports two types of encryption: randomized encryption and deterministic encryption: Deterministic encryption uses a method that always generates the same encrypted value for any given plaintext value. Using deterministic encryption allows grouping, filtering by equality, and joining tables based on encrypted values, but can also allow unauthorized users to guess information about encrypted values by examining patterns in the encrypted column. This weakness is increased when there is a small set of possible encrypted values, such as True/False, or North/South/East/West region. Deterministic encryption must use a column collation with a binary2 sort order for character columns. Randomized encryption uses a method that encrypts data in a less predictable manner. Randomized encryption is more secure, but prevents equality searches, grouping, indexing, and joining on encrypted columns. Use deterministic encryption for columns that will be used as search or grouping parameters, such as a government ID number. Use randomized encryption for data such as confidential investigation comments, which are not grouped with other records, or for data used to join tables (in the row that contains the encrypted column of interest).
- #38 Source: https://msdn.microsoft.com/en-us/library/mt130841(v=sql.130).aspx Dynamic Data Masking limits sensitive data exposure by masking it to non-privileged users. It helps prevent unauthorized access to sensitive data by enabling customers to designate how much sensitive data to reveal with minimal impact on the application layer. A policy-based security feature, it hides sensitive data in the result set of a query over designated database fields, while the data in the database is not changed. Dynamic Data Masking is easy to use with existing applications, since masking rules are applied in the query results, and there is no need to modify existing queries. For example, a call center support person may identify callers by several digits of their Social Security Number or credit card number, but those data items should not be fully exposed to the support person. A developer can define a masking rule to be applied to each query result that masks all but the last four digits of any Social Security Number or credit card number in the result set. For another example, by using the appropriate data mask to protect personally identifiable information (PII) data, a developer can query production environments for troubleshooting purposes without violating compliance regulations. Dynamic Data Masking limits the exposure of sensitive data and prevents accidental viewing by engineers that access directly databases for troubleshooting purposes or non-privileged application users. It doesn’t aim to prevent privileged database users from connecting directly to the database and running exhaustive queries that expose pieces of the sensitive data. Dynamic Data Masking complements other SQL Server security features (auditing, encryption, Row-Level Security…), and it is highly recommended to enable them in addition in order to protect better sensitive data in the database. Since data is masked just before being returned to the user, changing the data type to an unmasked type will return unmasked data. Dynamic Data Masking is available in SQL Server 2016. However, to enable it, you must use trace flags 209 and 219. For SQL Database, see Get started with SQL Database Dynamic Data Masking (Azure Preview portal).
- #39 Source: https://msdn.microsoft.com/en-us/library/bb510411(v=sql.130).aspx#RLS Row-Level Security introduces predicate-based access control. It features a flexible, centralized, predicate-based evaluation that can take into consideration metadata (such as labels) or any other criteria the administrator determines as appropriate. The predicate is used to determine whether the user has the appropriate access to the data based on user attributes. Label-based access control can be implemented by using predicate-based access control. For more information, see Row-Level Security.
- #42 Policy manager creates a filter predicate and security policy App user (e.g., a nurse) selects from the Patients table The query is transparently rewritten to apply the filter predicate Notice: No app changes!
- #43 Source: https://msdn.microsoft.com/en-us/library/dn765131(v=sql.130).aspx Row-level filtering of data selected from a table is enacted through a security predicate filter defined as an inline table valued function. The function is then invoked and enforced by a security policy. The policy can restrict the rows that may be viewed (a filter predicate), but does not restrict the rows that can be inserted or updated from a table (a blocking predicate). There is no indication to the application that rows have been filtered from the result set; if all rows are filtered, then a null set will be returned. Filter predicates are applied while reading data from the base table, and it affects all get operations: SELECT, DELETE (user cannot delete rows that are filtered), and UPDATE (user cannot update rows that are filtered, although it is possible to update rows in such way that they will be subsequently filtered). Blocking predicates are not available in this version of RLS, but equivalent functionality (user cannot INSERT or UPDATE rows such that they will subsequently be filtered) can be implemented using check constraints or triggers. Filter predicates and security policies have the following behavior: Define a security policy that filters the rows of a table. The application is unaware that any rows have been filtered for SELECT, UPDATE, and DELETE operations, including situations where all the rows have been filtered out. The application can INSERT any rows, regardless of whether they will be filtered during any other operation. Define a predicate function that joins with another table and/or invokes a function. The join/function is accessible from the query and works as expected without any additional permission checks. Issue a query against a table that has a security predicate defined but disabled. Any rows that would have been filtered or restricted are not affected. The dbo user, a member of the db_owner role, or the table owner queries against a table that has a security policy defined and enabled. Rows are filtered/restricted as defined by the security policy. Attempts to alter the schema of a table bound by a security policy will result in an error. However, columns not referenced by the filter predicate can be altered. Attempts to add a predicate on a table that already has one defined (regardless of whether it is enabled or disabled) results in an error. Attempts to modify a function used as a predicate on a table within a security policy results in an error. Defining multiple active security policies that contain non-overlapping predicates, succeeds. Source: https://msdn.microsoft.com/en-us/library/azure/dn765131.aspx
- #45 Auditing an instance of the SQL Server Database Engine or an individual database involves tracking and logging events that occur on the Database Engine. SQL Server audit lets you create server audits, which can contain server audit specifications for server-level events, and database audit specifications for database-level events. Audited events can be written to the event logs or to audit files. All editions of SQL Server support server-level audits. Database-level auditing is limited to the Enterprise, Developer, and Evaluation editions.
- #46 Azure Key Vault offers central key management, leverages hardware security modules (HSMs), and promotes the separation of key management from the management of data to help meet regulatory compliances. SQL Server’s support for AKV is available through the SQL Server Connector for AKV for all enterprise versions of SQL Server starting with 2012 through the 2016 release. The Connector is a downloadable DLL file that serves as an Extensible Key Management (EKM) provider for SQL Server. The Connector is especially important to customers using SQL Server-in-a-VM who want to leverage Azure Key Vault for managing their encryption keys. SQL Server-in-a-VM allows for quick deployment of SQL Server and is ideally suited for re-hosting existing SQL Server applications in the cloud or for extending portions of their database management operations into the cloud. With Azure Key Vault integration, both on-premises and SQL Server-in-a-VM users can assume control of encryption keys for Transparent Data Encryption (TDE), Column Level Encryption (CLE), and backup encryption while leveraging the additional security benefits of Azure Key Vault.
- #47 Transparent Data Encryption (TDE) in SQL Server, exclusive to SQL Server Enterprise edition
- #48 SQL Server encrypts data while creating a backup. By specifying the encryption algorithm and encryptor (a certificate or asymmetric key) when backing up, you can create an encrypted backup file. On-premises and Windows Azure storage locations are supported for this process. In addition, encryption options can be configured for SQL Server Managed Backup to Windows Azure operations, a new feature introduced in SQL Server 2014. To encrypt during backup, you must specify an encryption algorithm, and an encryptor to secure the encryption key. The following encryption options are supported: Encryption algorithm – The supported encryption algorithms are AES 128, AES 192, and AES 256 Encryptor – A certificate or asymmetric key Backup encryption – Now supported with compression and automatically will use Intel AES-NI hardware acceleration
- #53 Image source: https://msdn.microsoft.com/en-us/library/hh270278.aspx
- #62 When it comes to key BI investments, we are making it much easier to manage relational and non-relational data. PolyBase technology allows you to query Hadoop data and SQL Server relational data through a single T-SQL query. One of the challenges we see with Hadoop is there are not enough people knowledgeable in Hadoop and MapReduce, and this technology simplifies the skill set needed to manage Hadoop data. This can also work across your on-premises environment or SQL Server running in Azure.
- #64 Key Points: SQL Server Analysis Services Themes Talk Track: Make it easier to create models Improve tabular modeling and performance Use Analysis Services as a semantic model only High-performance DirectQuery for tabular Strengthen multidimensional Performance and data source updates Technical talking points: DirectQuery and tabular models Loading and analyzing data New analytic functions Data modeling relationships Updated developer tools MOLAP performance improvements Modeling DirectQuery Performance Dev Ops improvements
- #65 https://msdn.microsoft.com/en-us/library/bb522607.aspx
- #68 https://msdn.microsoft.com/en-us/library/ms159106.aspx
- #69 Key Points: SQL Server Reporting Services offers a single, on-premises solution for deploying and managing reports – both traditional, paginated reports and responsive mobile reports. Talk track: Report Design Report Builder enables report authors to design paginated reports and publish them to a report server. Mobile Report Publisher enables report authors to design mobile reports and publish them to a report server. SQL Server Data Tools (SSDT) integrates into Visual Studio 2015 and enables developers to design paginated reports within the Visual Studio integrated development environment and take advantage of projects, source control, build, and deployment. Report Viewing Modern browsers (Microsoft Edge, Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari) provide users with access to their reports across platforms and devices. Power BI Mobile apps are available for all major mobile platforms – Windows, iOS, and Android – and enable users to view KPIs and mobile reports on their phones and tablets. Custom Apps Third-party and custom apps can embed reports (both paginated and mobile) using HTML5 technology. ASP.NET web apps can embed the Report Viewer control for paginated report viewing. Apps can further integrate with Reporting Services using APIs and web services. Report Server A brand-new web portal built on HTML5 enables users to browse, search, view, and manage reports (both paginated and mobile) using modern browsers. The same web APIs that serve the web portal, Power BI Mobile apps, and report design apps also enable third-party and custom apps to integrate with Reporting Services and report server content. An enterprise-scale processing and rendering engine connects to and queries various types of data sources; processes data (filtering, sorting, grouping, aggregation); evaluates expressions and custom code; and renders paginated reports to various formats. A scheduling and delivery agent refreshes datasets and executes reports on a schedule, and delivers paginated reports to users via email and other means. The report server database, built on the SQL Server Database Engine, stores and manages the catalog of content, including data sources, datasets, paginated reports, mobile reports, and KPIs. The database can be on the report server or on a different server running SQL Server. Multiple report servers can operate in a scale-out configuration for additional scale and availability. Data Sources Reporting Services provides native data source connectors for SQL Server, SQL Server Analysis Services, Oracle Database, Oracle Essbase, SAP BW, Teradata, SharePoint lists, and XML. It also provides generic connectors based on standard ODBC and OLE DB protocols for access to many more data source types. Developers can create custom data processing extensions for connectivity to other, proprietary data sources. Report Delivery Destinations Reporting Services can deliver paginated reports on a schedule to email, file shares, and SharePoint libraries. Power BI users can pin items from Reporting Services paginated reports (charts, gauges, maps, images) to their Power BI dashboards. The report server updates their dashboard tiles on a schedule so the data is up-to-date. Developers can create custom delivery extensions to deliver reports to other destinations.
- #70 Key points: Key investments areas in SQL Server 2016 BI capabilities. Talk track: Both SQL Server Reporting Services and Analysis Services receive major enhancements and deliver modern and mobile reporting and powerful modeling capabilities. Let’s take a look at what’s new in Reporting Services.
- #81 Key points: The Microsoft SQL Server Mobile Report Publisher is the single point for designing and publishing of mobile reports. Talk track: With the Mobile Report Publisher you can connect to the SQL Server Reporting Services to access enterprise SQL server data sources. When providing an optimal experience on mobile devices, you need the ability to design different type of layouts: a responsive layout that adapts to very different screen sizes and orientations. With Report Publisher you can easily create beautiful reports for master, tablet and phone view. Connect visualizations to data sources and then publish them to all devices for access by other people in the organization. The Mobile Report Publisher Preview for Windows 7+ is available from the Microsoft Download Center (http://www.microsoft.com/en-us/download/details.aspx?id=50400)
- #82 Key points: Mobile reports dynamically adjust the content to fit your screen (or browser window). You never see content scrolled out of view or flowing onto a different page. While you can view these reports on your PC as well, they're really optimized for mobile devices.: Talk track: You only need one mobile app to access Power BI and SSRS mobile reports. Power BI apps are available for all major mobile platforms. Connect to your on-premises SSRS server and view your KPIs and mobile reports In addition to access via mobile devices you can also access your data via HTML rendering from your browser. The Power BI app to view Reporting Services mobile reports and KPIs is available in preview here: https://itunes.apple.com/us/app/microsoft-power-bi/id929738808?mt=8
- #87 Take out in initial version of NDA roadmap, until fully committed.
- #90 Source: https://msdn.microsoft.com/en-us/library/dn935011(v=sql.130).aspx Stretch Database lets you archive your historical data transparently and securely. In SQL Server 2016, Stretch Database stores your historical data in the Microsoft Azure cloud. After you enable Stretch Database, it silently migrates your historical data to an SQL Database. You don't have to change existing queries and client apps. You continue to have seamless access to both local and remote data. Your local queries and database operations against current data typically run faster. You typically enjoy reduced cost and complexity.
- #91 Source: https://msdn.microsoft.com/en-us/library/mt169378(v=sql.130).aspx Concepts and architecture for Stretch Database Terms Local database. The on-premises SQL Server 2016 database. Remote endpoint. The location in Microsoft Azure that contains the database’s remote data. In SQL Server 2016, this is an Azure SQL Database server. This is subject to change in the future. Local data. Data in a database with Stretch Database enabled that will not be moved to Azure based on the Stretch Database configuration of the tables in the database. Eligible data. Data in a database with Stretch Database enabled that has not yet been moved, but will be moved to Azure based on the Stretch Database configuration of the tables in the database. Remote data. Data in a database with Stretch Database enabled that has already been moved to Azure. Architecture Stretch Database leverages the resources in Microsoft Azure to offload archival data storage and query processing. When you enable Stretch Database on a database, it creates a secure linked server definition in the on-premises SQL Server. This linked server definition has the remote endpoint as the target. When you enable Stretch Database on a table in the database, it provisions remote resources and begins to migrate eligible data, if migration is enabled. Queries against tables with Stretch Database enabled automatically to run against both the local database and the remote endpoint. Stretch Database leverages processing power in Azure to run queries against remote data by rewriting the query. You can see this rewriting as a "remote query" operator in the new query plan.
- #92 Source: https://msdn.microsoft.com/en-us/library/mt163698(v=sql.130).aspx