SQL Server 2017 on Linux
- SQL Server 2017 will run natively on Linux
- It provides the same features and capabilities as SQL Server on Windows
- It supports the same editions as Windows and can be licensed with the same license
- It has the same database engine and core services as Windows
- Some advanced features like PolyBase and Stretch Database are not yet supported on Linux
- It uses a new platform abstraction layer to run on Linux
Supporting Hadoop in containers takes much more than the very primitive support Docker provides using the Storage Plugin. A production scale Hadoop deployment inside containers needs to honor anti/affinity, fault-domain and data-locality policies. Kubernetes alone, with primitives such as StatefulSets and PersitentVolumeClaims, is not sufficient to support a complex data-heavy application such as Hadoop. One needs to think about this problem more holistically across containers, networking and storage stacks. Also, constructs around deployment, scaling, upgrade etc in traditional orchestration platforms is designed for applications that have adopted a microservices philosophy, which doesn't fit most Big Data applications across the ingest, store, process, serve and visualization stages of the pipeline. Come to this technical session to learn how to run and manage lifecycle of containerized Hadoop and other applications in the data analytics pipeline efficiently and effectively, far and beyond simple container orchestration. #BigData, #NoSQL, #Hortonworks, #Cloudera, #Kafka, #Tensorflow, #Cassandra, #MongoDB, #Kudu, #Hive, #HBase, PARTHA SEETALA, CTO, Robin Systems.
Machine learning services with SQL Server 2017Mark Tabladillo
SQL Server 2017 introduces Machine Learning Services with two independent technologies: R and Python. The purpose of this presentation is 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.
Exploring microservices in a Microsoft landscapeAlex Thissen
Presentation for Dutch Microsoft TechDays 2015 with Marcel de Vries:
During this session we will take a look at how to realize a Microservices architecture (MSA) using the latest Microsoft technologies available. We will discuss some fundamental theories behind MSA and show you how this can actually be realized with Microsoft technologies such as Azure Service Fabric. This session is a real must-see for any developer that wants to stay ahead of the curve in modern architectures.
Supporting Hadoop in containers takes much more than the very primitive support Docker provides using the Storage Plugin. A production scale Hadoop deployment inside containers needs to honor anti/affinity, fault-domain and data-locality policies. Kubernetes alone, with primitives such as StatefulSets and PersitentVolumeClaims, is not sufficient to support a complex data-heavy application such as Hadoop. One needs to think about this problem more holistically across containers, networking and storage stacks. Also, constructs around deployment, scaling, upgrade etc in traditional orchestration platforms is designed for applications that have adopted a microservices philosophy, which doesn't fit most Big Data applications across the ingest, store, process, serve and visualization stages of the pipeline. Come to this technical session to learn how to run and manage lifecycle of containerized Hadoop and other applications in the data analytics pipeline efficiently and effectively, far and beyond simple container orchestration. #BigData, #NoSQL, #Hortonworks, #Cloudera, #Kafka, #Tensorflow, #Cassandra, #MongoDB, #Kudu, #Hive, #HBase, PARTHA SEETALA, CTO, Robin Systems.
Machine learning services with SQL Server 2017Mark Tabladillo
SQL Server 2017 introduces Machine Learning Services with two independent technologies: R and Python. The purpose of this presentation is 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.
Exploring microservices in a Microsoft landscapeAlex Thissen
Presentation for Dutch Microsoft TechDays 2015 with Marcel de Vries:
During this session we will take a look at how to realize a Microservices architecture (MSA) using the latest Microsoft technologies available. We will discuss some fundamental theories behind MSA and show you how this can actually be realized with Microsoft technologies such as Azure Service Fabric. This session is a real must-see for any developer that wants to stay ahead of the curve in modern architectures.
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
Better performance and cost effectiveness empower better results in the cognitive era. For more information, visit: http://www.ibm.com/systems/power/hardware/linux-lc.html
There is increased interest in using Kubernetes, the open-source container orchestration system for modern, stateful Big Data analytics workloads. The promised land is a unified platform that can handle cloud native stateless and stateful Big Data applications. However, stateful, multi-service Big Data cluster orchestration brings unique challenges. This session will delve into the technical gaps and considerations for Big Data on Kubernetes.
Containers offer significant value to businesses; including increased developer agility, and the ability to move applications between on-premises servers, cloud instances, and across data centers. Organizations have embarked on this journey to containerization with an emphasis on stateless workloads. Stateless applications are usually microservices or containerized applications that don’t “store” data. Web services (such as front end UIs and simple, content-centric experiences) are often great candidates as stateless applications since HTTP is stateless by nature. There is no dependency on the local container storage for the stateless workload.
Stateful applications, on the other hand, are services that require backing storage and keeping state is critical to running the service. Hadoop, Spark and to lesser extent, noSQL platforms such as Cassandra, MongoDB, Postgres, and mySQL are great examples. They require some form of persistent storage that will survive service restarts...
Speakers
Anant Chintamaneni, VP Products, BlueData
Nanda Vijaydev, Director Solutions, BlueData
MOUG17 Keynote: Oracle OpenWorld Major AnnouncementsMonica Li
Midwest Oracle Users Group Training Day 2017 Presentation by Rich Niemiec, Chief Innovation Officer at Viscosity North America.
Catch up on OOW17's top announcements in this 1 hour presentation.
Hyper-C is OpenStack on Windows Server 2016, based on Nano Server, Hyper-V, Storage Spaces Direct (S2D) and Open vSwitch for Windows. Bare metal deployment features Cloudbase Solutions Juju charms and MAAS.
Red Hat Ceph Storage: Past, Present and FutureRed_Hat_Storage
Ceph is a massively scalable, open source, software-defined storage system that runs on commodity hardware. Get an update about the latest version of Red Hat Ceph Storage, including information about the newest features and use cases, with a particular focus on cloud storage and OpenStack. We’ll also explore the themes and directions for the roadmap for the next 12 months.
Startup Case Study: Leveraging the Broad Hadoop Ecosystem to Develop World-Fi...DataWorks Summit
Back in 2014, our team set out to change the way the world exchanges and collaborates with data. Our vision was to build a single tenant environment for multiple organisations to securely share and consume data. And we did just that, leveraging multiple Hadoop technologies to help our infrastructure scale quickly and securely.
Today Data Republic’s technology delivers a trusted platform for hundreds of enterprise level companies to securely exchange, commercialise and collaborate with large datasets.
Join Head of Engineering, Juan Delard de Rigoulières and Senior Solutions Architect, Amin Abbaspour as they share key lessons from their team’s journey with Hadoop:
* How a startup leveraged a clever combination of Hadoop technologies to build a secure data exchange platform
* How Hadoop technologies helped us deliver key solutions around governance, security and controls of data and metadata
* An evaluation on the maturity and usefulness of some Hadoop technologies in our environment: Hive, HDFS, Spark, Ranger, Atlas, Knox, Kylin: we've use them all extensively.
* Our bold approach to expose APIs directly to end users; as well as the challenges, learning and code we created in the process
* Learnings from the front-line: How our team coped with code changes, performance tuning, issues and solutions while building our data exchange
Whether you’re an enterprise level business or a start-up looking to scale - this case study discussion offers behind-the-scenes lessons and key tips when using Hadoop technologies to manage data governance and collaboration in the cloud.
Speakers:
Juan Delard De Rigoulieres, Head of Engineering, Data Republic Pty Ltd
Amin Abbaspour, Senior Solutions Architect, Data Republic
Insights into Real-world Data Management ChallengesDataWorks Summit
Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
Today enterprises desire to move more and more of their data lakes to the cloud to help them execute faster, increase productivity, drive innovation while leveraging the scale and flexibility of the cloud. However, such gains come with risks and challenges in the areas of data security, privacy, and governance. In this talk we cover how enterprises can overcome governance and security obstacles to leverage these new advances that the cloud can provide to ease the management of their data lakes in the cloud. We will also show how the enterprise can have consistent governance and security controls in the cloud for their ephemeral analytic workloads in a multi-cluster cloud environment without sacrificing any of the data security and privacy/compliance needs that their business context demands. Additionally, we will outline some use cases and patterns as well as best practices to rationally manage such a multi-cluster data lake infrastructure in the cloud.
Speaker:
Jeff Sposetti, Product Management, Hortonworks
Choosing technologies for a big data solution in the cloudJames Serra
Has your company been building data warehouses for years using SQL Server? And are you now tasked with creating or moving your data warehouse to the cloud and modernizing it to support “Big Data”? What technologies and tools should use? That is what this presentation will help you answer. First we will cover what questions to ask concerning data (type, size, frequency), reporting, performance needs, on-prem vs cloud, staff technology skills, OSS requirements, cost, and MDM needs. Then we will show you common big data architecture solutions and help you to answer questions such as: Where do I store the data? Should I use a data lake? Do I still need a cube? What about Hadoop/NoSQL? Do I need the power of MPP? Should I build a "logical data warehouse"? What is this lambda architecture? Can I use Hadoop for my DW? Finally, we’ll show some architectures of real-world customer big data solutions. Come to this session to get started down the path to making the proper technology choices in moving to the cloud.
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
Better performance and cost effectiveness empower better results in the cognitive era. For more information, visit: http://www.ibm.com/systems/power/hardware/linux-lc.html
There is increased interest in using Kubernetes, the open-source container orchestration system for modern, stateful Big Data analytics workloads. The promised land is a unified platform that can handle cloud native stateless and stateful Big Data applications. However, stateful, multi-service Big Data cluster orchestration brings unique challenges. This session will delve into the technical gaps and considerations for Big Data on Kubernetes.
Containers offer significant value to businesses; including increased developer agility, and the ability to move applications between on-premises servers, cloud instances, and across data centers. Organizations have embarked on this journey to containerization with an emphasis on stateless workloads. Stateless applications are usually microservices or containerized applications that don’t “store” data. Web services (such as front end UIs and simple, content-centric experiences) are often great candidates as stateless applications since HTTP is stateless by nature. There is no dependency on the local container storage for the stateless workload.
Stateful applications, on the other hand, are services that require backing storage and keeping state is critical to running the service. Hadoop, Spark and to lesser extent, noSQL platforms such as Cassandra, MongoDB, Postgres, and mySQL are great examples. They require some form of persistent storage that will survive service restarts...
Speakers
Anant Chintamaneni, VP Products, BlueData
Nanda Vijaydev, Director Solutions, BlueData
MOUG17 Keynote: Oracle OpenWorld Major AnnouncementsMonica Li
Midwest Oracle Users Group Training Day 2017 Presentation by Rich Niemiec, Chief Innovation Officer at Viscosity North America.
Catch up on OOW17's top announcements in this 1 hour presentation.
Hyper-C is OpenStack on Windows Server 2016, based on Nano Server, Hyper-V, Storage Spaces Direct (S2D) and Open vSwitch for Windows. Bare metal deployment features Cloudbase Solutions Juju charms and MAAS.
Red Hat Ceph Storage: Past, Present and FutureRed_Hat_Storage
Ceph is a massively scalable, open source, software-defined storage system that runs on commodity hardware. Get an update about the latest version of Red Hat Ceph Storage, including information about the newest features and use cases, with a particular focus on cloud storage and OpenStack. We’ll also explore the themes and directions for the roadmap for the next 12 months.
Startup Case Study: Leveraging the Broad Hadoop Ecosystem to Develop World-Fi...DataWorks Summit
Back in 2014, our team set out to change the way the world exchanges and collaborates with data. Our vision was to build a single tenant environment for multiple organisations to securely share and consume data. And we did just that, leveraging multiple Hadoop technologies to help our infrastructure scale quickly and securely.
Today Data Republic’s technology delivers a trusted platform for hundreds of enterprise level companies to securely exchange, commercialise and collaborate with large datasets.
Join Head of Engineering, Juan Delard de Rigoulières and Senior Solutions Architect, Amin Abbaspour as they share key lessons from their team’s journey with Hadoop:
* How a startup leveraged a clever combination of Hadoop technologies to build a secure data exchange platform
* How Hadoop technologies helped us deliver key solutions around governance, security and controls of data and metadata
* An evaluation on the maturity and usefulness of some Hadoop technologies in our environment: Hive, HDFS, Spark, Ranger, Atlas, Knox, Kylin: we've use them all extensively.
* Our bold approach to expose APIs directly to end users; as well as the challenges, learning and code we created in the process
* Learnings from the front-line: How our team coped with code changes, performance tuning, issues and solutions while building our data exchange
Whether you’re an enterprise level business or a start-up looking to scale - this case study discussion offers behind-the-scenes lessons and key tips when using Hadoop technologies to manage data governance and collaboration in the cloud.
Speakers:
Juan Delard De Rigoulieres, Head of Engineering, Data Republic Pty Ltd
Amin Abbaspour, Senior Solutions Architect, Data Republic
Insights into Real-world Data Management ChallengesDataWorks Summit
Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
Today enterprises desire to move more and more of their data lakes to the cloud to help them execute faster, increase productivity, drive innovation while leveraging the scale and flexibility of the cloud. However, such gains come with risks and challenges in the areas of data security, privacy, and governance. In this talk we cover how enterprises can overcome governance and security obstacles to leverage these new advances that the cloud can provide to ease the management of their data lakes in the cloud. We will also show how the enterprise can have consistent governance and security controls in the cloud for their ephemeral analytic workloads in a multi-cluster cloud environment without sacrificing any of the data security and privacy/compliance needs that their business context demands. Additionally, we will outline some use cases and patterns as well as best practices to rationally manage such a multi-cluster data lake infrastructure in the cloud.
Speaker:
Jeff Sposetti, Product Management, Hortonworks
Choosing technologies for a big data solution in the cloudJames Serra
Has your company been building data warehouses for years using SQL Server? And are you now tasked with creating or moving your data warehouse to the cloud and modernizing it to support “Big Data”? What technologies and tools should use? That is what this presentation will help you answer. First we will cover what questions to ask concerning data (type, size, frequency), reporting, performance needs, on-prem vs cloud, staff technology skills, OSS requirements, cost, and MDM needs. Then we will show you common big data architecture solutions and help you to answer questions such as: Where do I store the data? Should I use a data lake? Do I still need a cube? What about Hadoop/NoSQL? Do I need the power of MPP? Should I build a "logical data warehouse"? What is this lambda architecture? Can I use Hadoop for my DW? Finally, we’ll show some architectures of real-world customer big data solutions. Come to this session to get started down the path to making the proper technology choices in moving to the cloud.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
SQL Server 2017 Deep Dive - @Ignite 2017Travis Wright
This was a presentation given at Ignite 2017 on SQL Server 2017. It covers the main new capabilities of SQL Server 2017. The video recording of the session is available here: https://myignite.microsoft.com/sessions/54946?source=sessions
SQL Server 2017 will bring SQL Server to Linux for the first time. This presentation covers the scope, schedule, and architecture as well as a background on why Microsoft is making SQL Server available on Linux.
First introduced with the Analytics Platform System (APS), PolyBase simplifies management and querying of both relational and non-relational data using T-SQL. It is now available in both Azure SQL Data Warehouse and SQL Server 2016. The major features of PolyBase include the ability to do ad-hoc queries on Hadoop data and the ability to import data from Hadoop and Azure blob storage to SQL Server for persistent storage. A major part of the presentation will be a demo on querying and creating data on HDFS (using Azure Blobs). Come see why PolyBase is the “glue” to creating federated data warehouse solutions where you can query data as it sits instead of having to move it all to one data platform.
Kubernetes is an open source container cluster orchestration platform founded by Google. This presentation covers an overview of it's main concepts, plus how it fits into Google Cloud Platform. This was delivered by Kit Merker at DevNexus 2015 in Atlanta.
2017 OWASP SanFran March Meetup - Hacking SQL Server on Scale with PowerShellScott Sutherland
This presentation will provide an overview of common SQL Server discovery, privilege escalation, persistence, and data targeting techniques. Techniques will be shared for escalating privileges on SQL Server and associated Active Directory domains. Finally I’ll show how PowerShell automation can be used to execute the SQL Server attacks on scale with PowerUpSQL. All scripts demonstrated during the presentation are available on GitHub. This should be useful to penetration testers and system administrators trying to gain a better understanding of their SQL Server attack surface and how it can be exploited.
Sections Updated for OWASP Meeting:
- SQL Server Link Crawling
- UNC path injection targets
- Command execution details
OpenShift is Red Hat's Platform-as-a-Service (PaaS) that lets developers quickly develop, host, and scale Docker container-based applications. OpenShift enables a uniform and standardised approach to container management across all hosting options including AWS/EC2 and other private/public cloud and on/off-premise variants. At this session, you will learn how Red Hat's enterprise clients are using OpenShift to enable their digital transformation initiatives. Examples will cover how realising a hybrid cloud strategy can simplify and reduce the risk of migrating and transitioning application workloads to containers in the cloud.
Alex Smith, Solutions Architect, Amazon Web Services, ASEAN
Stephen Bylo, Senior Solution Architect, Red Hat Asia Pacific Pte Ltd
By Rafael Benevides and Christian Posta
A lot of functionality necessary for running in a microservices architecture have been built into Kubernetes; why would you re-invent the wheel with lots of complicated client-side libraries? Have you ever asked why you should use containers and what are the benefits for your application? This talk will present a microservices application that have been built using different Java platforms: WildFly Swarm and Vert.x. Then we will deploy this application in a Kubernetes cluster to present the advantages of containers for MSA (Microservices Architectures) and DevOps. The attendees will learn how to create, edit, build, deploy Java Microservices, and also how to perform service discovery, rolling updates, persistent volumes and much more. Finally we will fix a bug and see how a CI/CD Pipeline automates the process and reduces the deployment time.
OSCON16: Analysis of the Xen code review process: An example of software deve...The Linux Foundation
The Xen Project’s code contributions have been growing 10% a year. However, during this period of growth, the code review process became much slower, leading to issues in the community. Code review in the Xen Project—as in many other FOSS projects—is performed on mailing lists. During the last few years, the project observed an increase in the number of messages devoted to code review—in particular, an increase in the number of code review messages per patch series or individual patch.
Everyone in the community had a different theory as to the root causes of the issues based on their observations: some developers believed we didn’t have enough reviewers, some felt the project’s maintainers had become more aggressive, and some felt code review was not coordinated enough. Many observations contradicted each other and were based only on opinions. Consequently, key members of the project could not agree on how to deal with the perceived issues.
Lars Kurth and Daniel Izquierdo explain why the project decided to use data mining techniques using software development analytics to address the issue. The project needed a detailed analysis to verify which theories were valid, which were not, and which were missed. To do this, the team defined a number of parameters in the code review process to determine if it was deteriorating in some way and pinpoint the root causes of this deterioration, if any. Lars and Daniel cover the project’s journey through a number of stories and explore the techniques that enabled the community to improve their review process.
Microsoft Technologies for Data Science 201612Mark Tabladillo
Delivered to SQL Saturday BI Edition -- Atlanta, GA
Microsoft provides several technologies in and around Azure which can be used for casual to serious data science. This presentation provides an overview of the major Microsoft options for both on-premise and cloud-based data science (and hybrid). These technologies have been used by the presenter in various companies and industries, both as a Microsoft consultant and previously independent consultant. As well, the speaker provides insights into data science careers, information which helps imply where the business will likely be for consultants and partners.
Very short overview of the Xen Project Release and Roadmap Process (for the blog). It covers the process valid up to and including Xen 4.6, and the approved proposal for Xen 4.7 and newer.
XPDS16: High-Performance Virtualization for HPC Cloud on Xen - Jun Nakajima &...The Linux Foundation
We have been working to get Xen up and running on self-boot Intel® Xeon Phi processors to build HPC clouds. We see several challenges because of the unique (but not unusual for HPC) hardware technologies and performance requirements. For example, such hardware technologies include 1) >256 CPUs, 2) MCDRAM (high-bandwidth memory), 3) integrated fabric (i.e. Intel® Omni-Path). Unlike the “coprocessor“ model, supporting self-boot with >256 CPUs has various implications to Xen, including scheduling and scalability. We need to allow user applications to use MCDRAM directly to perform optimally. Also, we need to enable the integrated HPC fabric for the VM to use by direct I/O assignment.
In addition, we have only a single VM on each node to meet the high-performance requirements of HPC clouds. This (i.e. non-shared) model allowed us to optimize Xen more. In this talk, we share our design and lessons, and discuss the options we considered to achieve high-performance virtualization for HPC.
Hypervisors are becoming more and more widespread in embedded environments, from automotive to medical and avionics. Their use case is different from traditional server and desktop virtualization, and so are their requirements. This talk will explain why hypervisors are used in embedded, and the unique challenges posed by these environments to virtualization technologies.
Xen, a popular open source hypervisor, was born to virtualize x86 Linux systems for the data center. It is now the leading open source hypervisor for ARM embedded platforms. The presentation will show how the ARM port of Xen differs from its x86 counterpart. It will go through the fundamental design decisions that made Xen a good choice for ARM embedded virtualization. The talk will explain the implementation of key features such as device assignment and interrupt virtualization.
SQL Server vNext is the next major release of SQL Server and the first release which will be available on Linux and Docker. This presentation covers all the details.
SUSE Webinar - Introduction to SQL Server on LinuxTravis Wright
Introduction to SQL Server on Linux for SUSE customers. Talks about scope of the first release of SQL Server on Linux, schedule, Early Adoption Program. Recording is available here:
https://www.brighttalk.com/webcast/11477/243417
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.
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
SQL Server goes Linux - Hello, my name is Tux, I would like to join the #SQLF...Andre Essing
SQL Server und Windows, seit Jahren ein unzertrennliches Paar und eine gute Kombination für die Verarbeitung von Daten. Mit SQL Server 2017 wird sich dies jedoch ändern, denn aus dem Norden Europas, genauer aus Helsinki, möchte ein kleiner Pinguin der SQL Server Community beitreten.
Die kommende SQL Server Version wird es somit nicht mehr ausschließlich für Windows geben. Ein Deployment der relationalen Datenbank Engine wird in naher Zukunft auch auf Linux und innerhalb von Docker möglich sein. Somit bietet der SQL Server nun mehr Einsatzmöglichkeiten und Anwendungsfälle als je zuvor. Aber wie funktioniert SQL Server on Linux? Was wird unterstützt, was nicht? Welche Szenarien sind möglich? Alle diese Punkte möchte in dieser Session besprechen.
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.
PASS Summit - SQL Server 2017 Deep DiveTravis Wright
Deep dive into SQL Server 2017 covering SQL Server on Linux, containers, HA improvements, SQL graph, machine learning, python, adaptive query processing, and much much more.
The event, held on 11th December 2018, was a technical presentation about running MS SQL Server 2017 on Linux. We started off by using containers and proceeded in looking at High Availability and Data Protection, more specifically:
- Supported features & Linux differences
- Installing SQL Server on a Linux Container
- Accessing SMB 3.0 shared storage using Samba
- Setting up a Fail over Cluster using Pacemaker
- Setting up AlwaysOn Availability Groups using Pacemaker
- Authenticating to SQL Server using AD Authentication
- Setting up Read-Scale Cross-Platform Availability Groups
https://techspark.mt/sql-server-on-linux-11th-december-2018/
Overview SQL Server on Linux
It is just SQL Server. Everything just works
Initially aimed at Database Engine
Currently around 95% features fully supported.
Hi! Ho! Hi! Ho! SQL Server on Linux We Go!SolarWinds
SQL Server has been running on Windows for years. Now Microsoft is making it available on Linux in order to provide a consistent database platform across Window and Linux servers, as well as on-premises and in the cloud. In this presentation, Janis Griffin, database performance evangelist at SolarWinds, discusses the advantages of using SQL Server on Linux, comparing architecture, cost and performance.
SQL Server 2017 Overview and Partner OpportunitiesTravis Wright
SQL Server 2017 is going to be released later this year. In this session will cover what to expect and how partners can deliver additional value to SQL Server customers.
Azure Virtual Machines Deployment ScenariosBrian Benz
Architecture and Scenarios for deploying Database and middleware applications on Azure Virtual Machines including SQL Server, Oracle, Hadoop, and others.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
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We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
4. Microsoft
is delivering
on choice
SQL Server on Linux
HDInsight on Linux
R Server on Linux
Linux in Azure
SQL Server drivers
and connectivity
Visual Studio Code
extension for SQL Server
Python,
Ruby, …
20,000 applications for the SQL Server on Linux private
preview program, including more than 55% of Fortune
500 companies.
NEW
5. On the platform of your choice
SQL Server v.Next
Targeting CY2017
SQL Server v.Next GA*
SQL Server v.Next Public Preview available now on Linux, Windows, and Docker.
6. • Buying a SQL Server license—per-
server or per-core—grants the
option to use it on Windows Server
or Linux
• Previews are free to download and
use in a non-production capacity
• Same set of editions on Linux:
Developer, Express, Standard, Web,
Enterprise
LICENSE
Licensing
Same license, new choice
7. Stay ahead of the competition with the latest innovations
Be future-ready with Software Assurance
TODAY vNEXT v∞
Always have access to the latest New Version Rights
• Improve productivity with the
latest technologies
• Lower the cost of acquiring new
product versions
• Simplify licensing, budgeting and
administration
9. Windows Linux GA
Developer, Express, Web, Standard, Enterprise
Database Engine
R Services, Integration Services, Analysis Services, Reporting Services, MDS,
DQS
Maximum number of cores Unlimited TBD
Maximum memory utilized per instance 12 TB TBD
Maximum database size 524 PB TBD
Basic OLTP (Basic In-Memory OLTP, Basic operational analytics)
Advanced OLTP (Advanced In-Memory OLTP, Advanced operational analytics)
Basic high availability (2-node single database failover, non-readable
secondary)
Advanced HA (Always On - multi-node, multi-db failover, readable secondaries)
Security
Basic security (Basic auditing, Row-level security, Data masking, Always
Encrypted)
Advanced security (Transparent Data Encryption)
Data
warehousing
PolyBase2
Basic data warehousing/data marts (Basic In-Memory ColumnStore,
Partitioning, Compression)
Advanced data warehousing (Advanced In-Memory ColumnStore)
Advanced data integration (Fuzzy grouping and look ups)
Tools
Windows ecosystem: Full-fidelity Management & Dev Tool (SSMS & SSDT),
command line tools
Linux/OSX/Windows ecosystem: Dev tools (VS Code), DB Admin GUI tool,
command line tools
Developer
Programmability (T-SQL, CLR, Data Types, JSON)
Windows Filesystem Integration - FileTable
BI & Advanced
Analytics
Basic Corporate Business Intelligence (Multi-dimensional models, Basic tabular
model)
Basic “R” integration (Connectivity to R Open, Limited parallelism for ScaleR)
Advanced “R” integration (Full parallelism for ScaleR)
Hybrid cloud Stretch Database
What’s
coming in
SQL Server
on Linux
10. Programming Features
• Support for RHEL, Ubuntu, Docker
• Package based installs, Docker image
• Support for Open Shift, Docker Swarm
• Failover Clustering through Pacemaker
• Backup/Restore
• SSMS on Windows connected to Linux
• Command line tools: sqlcmd, bcp, sqlpackage
• SQL Server Agent
• Replication
• Log Shipping
• Transparent Data Encryption
• SCOM Management Pack
• DMVs
• Full Text Search
Operations Features
• All major language driver compatibility
• In memory OLTP and ColumnStore
• Compression
• Always Encrypted, Row Level Security, and Data Masking
• Service Broker
• Change Data Capture
• Partitioning
• Auditing
• CLR
• JSON, XML
• Third party tools
What’s working already?
…and more!
11. Linux Containers
Windows
Windows Server
• RedHat Enterprise Linux (RHEL) 7.3
• SUSE Enterprise Linux (SLES) v12 SP2
• Ubuntu 16.04 & 16.10
• Possibly other Linux distributions
• Docker: Windows & Linux containers
• Windows Server / Windows 10
• Package based installation
Example: yum install mssql-server
12. SQL Control Access
Database access SQL Authentication
Active Directory Authentication
Granular Permissions
Application access Row-Level Security
Dynamic Data Masking
Monitor Access
Tracking activities Fine-Grained Audit
Protect Data
Encryption at rest Transparent Data Encryption
Backup Encryption
Cell-Level Encryption
Encryption in transit Transport Layer Security (SSL/TLS)
Encryption in use (client) Always Encrypted
Protect Data
Encryption at rest Transparent Data Encryption
Backup Encryption
Cell-Level Encryption
Encryption in transit Transport Layer Security (SSL/TLS)*
Encryption in use (client) Always Encrypted
Control Access
Database access SQL Authentication
Active Directory Authentication*
Granular Permissions
Application access Row-Level Security
Dynamic Data Masking
*In progress
13. • Resilience against guest & OS
level failures
• Planned & unplanned events
• Minimum downtime for patching
and upgrades
• Minutes RTO
Simple HADR
VM Failure
• Protection against accidental or
malicious data corruption
• DR protection
• Minutes to hours RTO
Backup/Restore
• Instance level protection
• Automatic failure detection &
failover
• Seconds to minutes RTO
• Resilience against OS and SQL
Server failures
Standard HADR
Failover Cluster
• AG with 2 replicas
Basic Availability Groups*
• Warm standbys for DR
Log Shipping*
• Database level protection
• Seconds RTO
• No data loss
• Recover from unplanned outage
• No downtime for planned
maintenance
• Offload read/backup workload
to active secondaries
• Failover to geographically
distributed secondary site
Availability Groups*
Mission-Critical HADR
*In progress
14. • Windows-based SQL Server tools
like SSMS, SSDT, Profiler work
when connected to SQL Server on
Linux
• 3rd party tools continue to work
• Native command line tools:
sqlcmd, bcp, sqlpackage
• Visual Studio Code extension
• New cross-platform DB admin GUI
tool (planned)
• All existing drivers and frameworks
supported
16. SQL Platform Abstraction Layer
(SQLPAL)
RDBMS IS AS RS
Windows Linux
Windows
Host Ext.
Linux Host
Extension
SQL Platform Abstraction Layer
(SQLPAL)
Win32-like APIs
Host Extension mapping to OS system calls
(IO, Memory, CPU scheduling)
SQL OS API
SQL OS v2
Everything else
System Resource &
Latency Sensitive
Code Paths
17. sudo zypper addrepo -fc https://packages.microsoft.com/config/sles/12/mssql-server.repo
sudo zypper --gpg-auto-import-keys refresh
Paso 1: Download the Microsoft SQL Server SLES repository configuration file
Paso 2: Install SQL Server
sudo zypper install mssql-server
Paso 3: Setup SQL Server
sudo /opt/mssql/bin/mssql-conf setup
Paso 4: Verificar estado
systemctl status mssql-server
18. sudo zypper addrepo -fc https://packages.microsoft.com/config/sles/12/prod.repo
sudo zypper --gpg-auto-import-keys refresh
Paso 2: Add the Microsoft SQL Server repository to Zypper
Paso 3: Install mssql-tools with the unixODBC developer package.
sudo zypper install mssql-tools unixODBC-devel
sudo zypper install mssql-server-agent
sudo systemctl restart mssql-server
Paso 1: Install mssql-server-agent
Lets talk about SQL Server v.Next, the next major release of SQL Server, currently in public preview, which brings the power and innovation of SQL Server, for any application, to the platform of your choice.
The business landscape is becoming increasingly diverse
Development and deployment environments include Windows, Linux, macOS, and Docker
Data is no longer relational, with companies accessing diverse data, including video, streaming, documents, relational, both external data and data internal to their org
Languages and frameworks are also expanding with the poularity of Node.js, Python, Ruby and others
Companies deploy on-premises, in the cloud, or both, with a hybrid solution
Microsoft has delivered a number of products enabling choice for customers, including:
HD Insight on Linux - a managed Apache Hadoop, Spark, R, HBase, and Storm cloud service made easy
R Server on Linux - Use R—the powerful, statistical programming language—in an enterprise-class, big data analytics platform. Microsoft R Server is your flexible choice for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business (https://www.microsoft.com/en-us/cloud-platform/r-server)
Linux in Azure – run Linux-based virtual machines in Microsoft Azure (IaaS)
Customers can take advantage of Microsoft–created database connectivity drivers and open-source drivers that enable developers to build any application using the platforms and tools of their choice, including Python, Ruby, and Node.js
With SQL Server v.Next, now in public preview, and generally available to purchase targeting mid-calendar year 2017, the power of SQL Server is available on the platform of your choice.
Collapse BI and AA into one single row
AA: “single R integration
BI: Corporate business intelligence and mobile BI
So that’s High Availability and Disaster Recovery for SQL Server on Linux. Let’s look at another aspect of SQL Server that is also critically important for enterprises – security. Our goal is for SQL Server on Linux to support the same enterprise-grade security capabilities that customers rely on with SQL Server on Windows today.
[click]
We think about security for SQL Server in terms of layers. At the center, you have your data and how you protect the data itself, typically using encryption. SQL Server supports a variety of encryption features to help protect your data against different types of threats.
Transparent Data Encryption (TDE) encrypts your whole database at rest, without requiring any application changes. Backup Encryption encrypts your backup files, and Cell-Level Encryption gives you granular control over the encryption of individual cells of data.
To encrypt data in transit to and from the database, SQL Server supports the industry-standard TLS 1.2 protocol.
And finally, Always Encrypted enables you to encrypt sensitive data client-side, so that even privileged SQL Server administrators are unable to see it in plaintext.
[click]
The next layer is about controlling access to the data.
SQL Authentication allows users to authenticate via a username and password, while Active Directory Authentication allows users to authenticate using single sign-on through Active Directory and Kerberos. Granular permissions enable you to control access to individual tables or even columns of data.
Row-Level Security allows you to control read- and write-access to individual rows of data based on a customizable policy, and Dynamic Data Masking allows you to easily mask fields (such as account numbers) so that only part of the data can be seen.
[click]
The last layer is about monitoring who accesses the data.
SQL Server’s Fine-Grained Audit feature allows you to enforce a data audit policy and track which users are doing which actions.
[click]
So that’s where we’re going with security for SQL Server on Linux. Almost all of these features are already available for you to try in the CTP1 preview release, please try them out and share your feedback with us. Support for TLS is in progress and will become available in an upcoming build. Similarly, Active Directory Authentication is one of our most requested features and will become available in an upcoming build.
Add diagram from Travis
Se ha rediseñado la forma en que SQL realiza la transferencia de los cambios en el transaction log para poder utilizar major la red y que los cambios se aplique mas rapido en los secondaries
Antes se podia direccionar el trafico read-only solo al primer secondary, ahora se puede armar un grupo de secondaries para hacer load-balancing de todo el trafico read-only
High Availavility basico en SQL Standard Edition. Es el reemplazo de database mirroring. 2 replicas – 1 primary 1 secondary y 1 base por grupo. No read-only access en el secondary . Async o Sync
Se ha rediseñado la forma en que SQL realiza la transferencia de los cambios en el transaction log para poder utilizar major la red y que los cambios se aplique mas rapido en los secondaries
Antes se podia direccionar el trafico read-only solo al primer secondary, ahora se puede armar un grupo de secondaries para hacer load-balancing de todo el trafico read-only
High Availavility basico en SQL Standard Edition. Es el reemplazo de database mirroring. 2 replicas – 1 primary 1 secondary y 1 base por grupo. No read-only access en el secondary . Async o Sync
Se ha rediseñado la forma en que SQL realiza la transferencia de los cambios en el transaction log para poder utilizar major la red y que los cambios se aplique mas rapido en los secondaries
Antes se podia direccionar el trafico read-only solo al primer secondary, ahora se puede armar un grupo de secondaries para hacer load-balancing de todo el trafico read-only
High Availavility basico en SQL Standard Edition. Es el reemplazo de database mirroring. 2 replicas – 1 primary 1 secondary y 1 base por grupo. No read-only access en el secondary . Async o Sync
Se ha rediseñado la forma en que SQL realiza la transferencia de los cambios en el transaction log para poder utilizar major la red y que los cambios se aplique mas rapido en los secondaries
Antes se podia direccionar el trafico read-only solo al primer secondary, ahora se puede armar un grupo de secondaries para hacer load-balancing de todo el trafico read-only
High Availavility basico en SQL Standard Edition. Es el reemplazo de database mirroring. 2 replicas – 1 primary 1 secondary y 1 base por grupo. No read-only access en el secondary . Async o Sync
Se ha rediseñado la forma en que SQL realiza la transferencia de los cambios en el transaction log para poder utilizar major la red y que los cambios se aplique mas rapido en los secondaries
Antes se podia direccionar el trafico read-only solo al primer secondary, ahora se puede armar un grupo de secondaries para hacer load-balancing de todo el trafico read-only
High Availavility basico en SQL Standard Edition. Es el reemplazo de database mirroring. 2 replicas – 1 primary 1 secondary y 1 base por grupo. No read-only access en el secondary . Async o Sync