This document summarizes new features in SQL Server 2019 including intelligent query processing, data classification and auditing, accelerated database recovery, data virtualization, SQL Server replication in one command, additional capabilities and migration tools, and a modern platform with Linux, containers, and machine learning services. It provides examples of how these features can help solve modern data challenges and gain performance without changing applications.
In this session, we explain how the new version of SQL Server will improve database operations, advance security and compliance and bring advanced analytics to all your data workloads.
This presentation shows new features in SQL 2019, and a recap of features from SQL 2000 through 2017 as well. You would be wise to hear someone from Microsoft deliver this material.
In this session, we explain how the new version of SQL Server will improve database operations, advance security and compliance and bring advanced analytics to all your data workloads.
This presentation shows new features in SQL 2019, and a recap of features from SQL 2000 through 2017 as well. You would be wise to hear someone from Microsoft deliver this material.
In this session, we explain how the new version of SQL Server will improve database operations, advance security and compliance and bring advanced analytics to all your data workloads.
Brk3288 sql server v.next with support on linux, windows and containers was...Bob Ward
SQL Server is bringing its world-class RDBMS to Linux and Windows with SQL Server v.Next. In this session you will learn what´s next for SQL Server on Linux and how application developers and IT architects can now leverage the enterprise class features of SQL Server in every edition on Linux, Windows and containers.
In this presentation we introduce the basic concepts around SQL Server Azure: the database in the cloud.
Regards,
Ing. Eduardo Castro, PhD
http://ecastrom.blogspot.com
http://comunidadwindows.org
6° Sessione - Ambiti applicativi nella ricerca di tecnologie statistiche avan...Jürgen Ambrosi
In questa sessione vedremo, con il solito approccio pratico di demo hands on, come utilizzare il linguaggio R per effettuare analisi a valore aggiunto,
Toccheremo con mano le performance di parallelizzazione degli algoritmi, aspetto fondamentale per aiutare il ricercatore nel raggiungimento dei suoi obbiettivi.
In questa sessione avremo la partecipazione di Lorenzo Casucci, Data Platform Solution Architect di Microsoft.
This session shows an overview of the features and architecture of SQL Server on Linux and Containers. It covers install, config, performance, security, HADR, Docker containers, and tools. Find the demos on http://aka.ms/bobwardms
En esta presentación examinamos los roles y responsabilidades en la administración de SQL Azure.
Saludos,
Eduardo Castro Martinez – Microsoft SQL Server MVP
http://mswindowscr.org
http://comunidadwindows.org
Costa Rica
Technorati Tags: SQL Server
LiveJournal Tags: SQL Server
del.icio.us Tags: SQL Server
http://ecastrom.blogspot.com
http://ecastrom.wordpress.com
http://ecastrom.spaces.live.com
http://universosql.blogspot.com
http://todosobresql.blogspot.com
http://todosobresqlserver.wordpress.com
http://mswindowscr.org/blogs/sql/default.aspx
http://citicr.org/blogs/noticias/default.aspx
Microsoft released SQL Azure more than two years ago - that's enough time for testing (I hope!). So, are you ready to move your data to the Cloud? If you’re considering a business (i.e. a production environment) in the Cloud, you need to think about methods for backing up your data, a backup plan for your data and, eventually, restoring with Red Gate Cloud Services (and not only). In this session, you’ll see the differences, functionality, restrictions, and opportunities in SQL Azure and On-Premise SQL Server 2008/2008 R2/2012. We’ll consider topics such as how to be prepared for backup and restore, and which parts of a cloud environment are most important: keys, triggers, indexes, prices, security, service level agreements, etc.
Microsoft SQL server 2017 Level 300 technical deckGeorge Walters
This deck covers new features in SQL Server 2017, as well as carryover features from 2012 onwards. This includes high availability, columnstore, alwayson, In-memory tables, and other enterprise features.
In this session, we explain how the new version of SQL Server will improve database operations, advance security and compliance and bring advanced analytics to all your data workloads.
Brk3288 sql server v.next with support on linux, windows and containers was...Bob Ward
SQL Server is bringing its world-class RDBMS to Linux and Windows with SQL Server v.Next. In this session you will learn what´s next for SQL Server on Linux and how application developers and IT architects can now leverage the enterprise class features of SQL Server in every edition on Linux, Windows and containers.
In this presentation we introduce the basic concepts around SQL Server Azure: the database in the cloud.
Regards,
Ing. Eduardo Castro, PhD
http://ecastrom.blogspot.com
http://comunidadwindows.org
6° Sessione - Ambiti applicativi nella ricerca di tecnologie statistiche avan...Jürgen Ambrosi
In questa sessione vedremo, con il solito approccio pratico di demo hands on, come utilizzare il linguaggio R per effettuare analisi a valore aggiunto,
Toccheremo con mano le performance di parallelizzazione degli algoritmi, aspetto fondamentale per aiutare il ricercatore nel raggiungimento dei suoi obbiettivi.
In questa sessione avremo la partecipazione di Lorenzo Casucci, Data Platform Solution Architect di Microsoft.
This session shows an overview of the features and architecture of SQL Server on Linux and Containers. It covers install, config, performance, security, HADR, Docker containers, and tools. Find the demos on http://aka.ms/bobwardms
En esta presentación examinamos los roles y responsabilidades en la administración de SQL Azure.
Saludos,
Eduardo Castro Martinez – Microsoft SQL Server MVP
http://mswindowscr.org
http://comunidadwindows.org
Costa Rica
Technorati Tags: SQL Server
LiveJournal Tags: SQL Server
del.icio.us Tags: SQL Server
http://ecastrom.blogspot.com
http://ecastrom.wordpress.com
http://ecastrom.spaces.live.com
http://universosql.blogspot.com
http://todosobresql.blogspot.com
http://todosobresqlserver.wordpress.com
http://mswindowscr.org/blogs/sql/default.aspx
http://citicr.org/blogs/noticias/default.aspx
Microsoft released SQL Azure more than two years ago - that's enough time for testing (I hope!). So, are you ready to move your data to the Cloud? If you’re considering a business (i.e. a production environment) in the Cloud, you need to think about methods for backing up your data, a backup plan for your data and, eventually, restoring with Red Gate Cloud Services (and not only). In this session, you’ll see the differences, functionality, restrictions, and opportunities in SQL Azure and On-Premise SQL Server 2008/2008 R2/2012. We’ll consider topics such as how to be prepared for backup and restore, and which parts of a cloud environment are most important: keys, triggers, indexes, prices, security, service level agreements, etc.
Microsoft SQL server 2017 Level 300 technical deckGeorge Walters
This deck covers new features in SQL Server 2017, as well as carryover features from 2012 onwards. This includes high availability, columnstore, alwayson, In-memory tables, and other enterprise features.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
4. Performance
• Query Store
• Adaptive Query
Processing
• Automatic
Tuning
• Columnstore and
In-Memory OLTP
• “It Just Runs
Faster”
Security
• Always
Encrypted
• Row Level
Security
• Dynamic Data
Masking
Availability
• Clusterless
Availability
Groups
• Distributed
Transactions for
Availability
Groups
• Resumable Index
Maintenance
Developer
• JSON
• Temporal Tables
• Graph Database
Modern
Platform
• Linux and
Containers
• Machine
Learning Services
with R and
Python
5. Solving Modern Data Challenges
1 0 1 0
0 1 0 1
0 1 1 0
SQL
ciphertext
Enclave
plaintext
R
SQL Server 2019
Arm64
6. • Intelligent Query Processing
Gain Performance with no
application changes
• Data Classification and Auditing
Need to classify and audit
your data?
• Accelerated Database Recovery
Long running transactions
affect your availability
• Data Virtualization
Want external access with
no data movement?
• SQL Server Replication in one command
Is SQL Server on Linux and
Containers compatible?
• Additional capabilities, Migration Tools, and Database
Compatibility
Additional capabilities and
Migration
https://aka.ms/bobwardms
https://github.com/microsoft/sqlworkshops/tree/master/SQLGroundToCloud/slides
7. The intelligent database
Gain performance without
changing the application
The Intelligent Query Processing feature family
Intelligent QP
Adaptive QP
Adaptive Joins
Batch Mode
Interleaved
Execution
Memory Grant
Feedback
Row Mode
Batch Mode
Table Variable
Deferred Compilation
Approximate QP
Approximate
Count Distinct
Batch Mode for
Row Store
Scalar UDF
inlining
8. The Solution(s)
• Build intelligent, adaptable operators
• Modify query plans in cache based
on previous execution
• Expand batch mode
• Execution data drives downstream
compilation
• Smarter query processing
140
140
140
150
150
150
140 SQL Server 2017
150 SQL Server 2019
150
10. ADD SENSITIVITY CLASSIFICATION TO
dbo.sales.price, dbo.sales.discount
WITH ( LABEL='Highly Confidential',
INFORMATION_TYPE='Financial' )
data_sensitivity_information
Who, what, and when accessed my
classified data?
14. SQL Server
T-SQL
Analytics Apps
ODBC NoSQL Relational databases Big data
PolyBase external tables
It all started with T-SQL against Hadoop
Query data where it lives using T-SQL
Compute engine integrated with SQL Server
Distributed, scalable query performance
Manual/deploy with SQL Server
Auto deploy/optimize with Big Data Clusters
Intelligence over all data
“It’s all about
Data Virtualization”
17. Managed SQL Server, Spark,
and data lake
Store high volume data in a data lake and access
it easily using either SQL or Spark
Management services, admin portal, and
integrated security make it all easy to manage
SQL
Server
Data virtualization
Combine data from many sources without
moving or replicating it
Scale out compute and caching to boost
performance
T-SQL
Analytics Apps
Open
database
connectivity
NoSQL Relational
databases
HDFS
Complete AI platform
Easily feed integrated data from many sources to
your model training
Ingest and prep data and then train, store, and
operationalize your models all in one system
SQL Server External Tables
Compute pools and data pools
Spark
Scalable, shared storage (HDFS)
External
data sources
Admin portal and management services
Integrated AD-based security
SQL Server
ML Services
Spark &
Spark ML
HDFS
REST API containers
for models
18. SQL Platform Abstraction Layer
(SQLPAL)
RDBMS IS AS RS
Windows Linux
Windows Host Ext. Linux Host Extension
SQL Platform Abstraction Layer
(SQLPAL)
Host extension mapping to OS system calls
(IO, Memory, CPU scheduling)
Win32-like APIs
SQL OS API
SQL OS v2
All other systems
System resource &
latency sensitive code paths
Choice across OS and containers
23. String or binary data would be truncated
String or binary data would be truncated
in table '%.*ls', column '%.*ls’.
Truncated value: '%.*ls'
SELECT page_info.*
FROM sys.dm_exec_requests AS d
CROSS APPLY
sys.fn_PageResCracker(d.page_resource) AS r
CROSS APPLY sys.dm_db_page_info(r.db_id,
r.file_id, r.page_id,'DETAILED')
AS page_info;
25. Reduce
upgrade risks
Unified
application
certification
Upgrade to latest
SQL Database
Engine version
Upgrade your SQL Server Database Engine or
move instances to the cloud with no code changes
Applications tested and certified on a given SQL
Server version are also implicitly tested and
certified on that SQL Server version native
database compatibility level
Separate application and platform layer upgrade
cycles for less disruption
Microsoft fully supports Compatibility Certification
Compatibility Certification benefits
Upgrade to the latest SQL Server Database Engine
without changing your critical applications
Frictionless migration
with no code changes
Certify once, run on-premises and in the cloud with Compatibility
Certification
Upgrade & modernize your SQL Server database on-premises, in the cloud and on the edge with Compatibility
Certification that eliminates risks of application compatibility
26. http://aka.ms/bobwardms
http://aka.ms/bobsqldemos
http://aka.ms/sqllinuxbook
Use our free training at https://aka.ms/sqlworkshops
Learn from videos and demos at https://aka.ms/sqlchannel
Download and try SQL Server 2019 at http://aka.ms/ss19
Watch the video Modernizing SQL Server at
https://www.youtube.com/watch?v=5RPkuQHcxxsto to plan your migration
Read the what’s new for SQL 2019 documentation
Sign-up for the EAP program at https://aka.ms/eapsignup
Editor's Notes
2 mins
2 mins
2 mins
5 mins
This is animated in this sequence
Data Virtualization with Polybase and Big Data Clusters
Intelligent Query Processing and In-Memory databases
Security enhancements with Always encrypted with enclaves
New HA capabilities such enhancements for AGs, built-in HA support from k8s and OpenShift, and Accelerated Database Recovery
Extensibility now with Java
Modern platforms choices with compatibility – Backup a db on any of these and restore to any of these
The areas in the blue are all features that exist 1:1 in Azure SQL Server Database.
For Kubernetes, this can also be deployed in Azure Kubernetes Service
3 mins
2 mins
2 mins
3 mins
3 mins
Online index enhancements
Resumable online index creation
Online Clustered Columnstore index creation and rebuild
Always On availability group enhancements includes
Primary connection intent
More replicas
2 mins
3 mins
This is a great example for a company that has existing data sources but doesn’t want to or cannot move the data. This diagram can be demonstrated with a demo video or using the github examples at https://github.com/microsoft/sqlworkshops/tree/master/ModernizeYourDatabases2019/ModernizeSQL2019/Module%208%20Activity%20-%20Data%20Virtualization
BDC is all about providing the install and configuration of Polybase automatically but if you don’t have Hadoop we will install one for you and include Spark with it. The right side of the slide is about using BDC as a complete ML/AI platform. All of this deployed on a cluster that is secure and provides built-in management, HA, and scale using Kubernetes.
Note: As of CTP 2.5, BDC does not support ODBC drivers for Polybase
The Platform Abstraction Layer (“PAL”) is what enables SQL Server to run on Linux and Docker. The PAL is used to consolidate OS/platform specific code to enable SQL Server code to become OS agnostic.
The SQL Server team set strict requirements to ensure that functionality, performance, and scale were not compromised when deployed to Linux.
Part of what makes this possible is the integration of certain parts of MSR’s project Drawbridge. Drawbridge provided an abstraction between the underlying operating system and the application for the purposes of secure containers. Drawbridge was combined with SQL Server OS, which provided memory management, thread scheduling, and IO services, to create SQLPAL.
In short, the creation of the PAL allows the same, time-proven core code base for SQL Server to run on new environments such as Docker and Linux – as opposed to porting the Windows code base into multiple operating environments. SQL Server 2017 is not a re-write or a port – it is the same performant, scalable product Microsoft customers have relied upon for years.
Speaker note: for more detail, see https://blogs.technet.microsoft.com/dataplatforminsider/2016/12/16/sql-server-on-linux-how-introduction/
The right diagram is to show how to update SQL Server by “switching” containers
HA built into k8s for a single SQL instance
The diagram is to emphasize
Process isolation
Secure
Resource control with RG
Starting with SQL Server 2016, the SQL Server team has stopped deprecating compatibility levels purposefully. The Database Engine is also literally the same accross SQL Server and Azure SQL (including Managed Instance), so a database can be moved seamlessly and the same database code (T-SQL) works the same and undergoes the same optimization processes.
Therefore, an application that was certified for compatibility 100 (SQL 2008) can work with the same behavior that was set for that version, even when moved to Azure SQL or a modern version of SQL Server.
This means:
•No more need to specifically certify for Azure or on-premises separately. Applications tested and certified on a given SQL Server version are also implicitly tested and certified on that SQL Server version native database compatibility level.
•Reduced upgrade risks because during platform modernization, application and platform layer upgrade cycles can be separated for less disruption, and improved change management
•Upgrading to a new version of SQL Server or Azure SQL can be done with no code changes by keeping the same compatibility level as the source
New Databases are set to compatibility level mapping to the version of the Database Engine. When a database is upgraded from any earlier version of SQL Server, the database retains its existing compatibility level if it is at least minimum allowed for that instance of SQL Server or Azure SQL. Upgrading a database with a compatibility level lower than the allowed level (for example from SQL 2005), sets the database to the lowest compatibility level allowed.