Brk2051 sql server on linux and docker

Editor's Notes

• #4: That’s not all it can do. SQL Server 2017 continues to deliver industry-leading capabilities: Our latest performance benchmarks on Windows and Linux blow away our old records. OLTP – We have #1 OLTP TPC-E performance DW: And, we have the fastest performing DW. With best price/performance. We offer the most secure database. According to US National Institute of Standards and Technology (NSIT), we have had fewer vulnerabilities over the last 7 years than Oracle or IBM Fewer vulnerabilities mean less patching for you! SQL Server is the first commercial database with Advanced Analytics using R and Python built-in. Why does this matter to you? Now you can use SQL Server to operationalize your data science models in a secure and performant way Use native T-SQL commands to score data in near real-time And unlike our competitors, mobile BI on every device comes built-in. Or add access to powerful, self-service BI visualizations through Power BI - at a fraction of the cost of our competitors. SQL Server 2017 gives you your choice of platform and language, and the most consistent on-prem to cloud environment. And it does all this for 1/10th the cost of Oracle
• #9: 3 mins
• #12: 1 #1 non-clustered 1TB TPC-H (http://www.tpc.org/3331)
• #14: Available on Windows, but the config and setup has changed on Linux. On Windows – password policy was the one that was available on your machine. On linux we have a hard coded password policy (complexity, expliration time etc.). Use MIT’s implementation of Kerberos Open source modules of id mapping
• #16: Lastly, let's consider the example of an ISV that has a solution on Windows and now with SQL Server available on Linux they want to certify their solution on Linux as well. Or the example of an enterprise moving their SQL Server workload to a Linux infrastructure. But due to their business requirements related to availability SLA they need the migration to be live with minimum downtime.   Using SQL Server 2017 customers can migrate their workloads cross platform, you can setup a DAG with primary AG on Windows for example and secondary on Linux. Again, we do not recommend running in this configuration in a steady state as there is no cluster manager for cross-platform orchestration, but it is the fastest solution for a cross-platform migration with minimum downtime. TODO: Research how this works further to explain
• #31: Uncover insights buried in your data to optimize the way you do business. Get insight you need to make the right decisions Make data-driven decisions to drive business results.​ Businesses need easy-to-use analytics products that deliver complex analysis and insights with a low cost of ownership Integrate big data from across the enterprise value chain and use advanced analytics in real time to optimize supply-side performance and save money. Embrace proactive measures with a live view into your supply chain—assess inventory levels, predict product fulfillment needs, and identify potential backlog issues. Bring together all of the data you need Data volumes are exploding—from traditional point-of-sale systems and e-commerce websites to new customer sentiment sources like Twitter and IoT sensors that stream data in real time using Apache Hadoop and Spark. By analyzing a diverse dataset from the start, you’ll make more informed decisions that are predictive and holistic rather than reactive and disconnected. Deliver better experiences and make better decisions by analyzing massive amounts of data in real time. Get the insight you need to deliver intelligent actions that improve customer engagement, increase revenue, and lower costs. Take charge of your data overload with big data Better understand customers, enhance decision-making, and increase productivity with insights gained from structured, semi-structured, and unstructured data. By analyzing vast quantities of data that were previously inaccessible, you transform your business culture into one that shapes the future with predictive analytics, rather than hindsight. Create new services, Reinvent processes, and capture new revenue
• #39: SQL Server runtime and associated libraries: /opt/mssql/bin/ /opt/mssql/lib/ Data and log files for SQL Server databases: /var/opt/mssql/data/ /var/opt/mssql/log/
• #40: NB: This slide is correct as at SQL Server 2017 RC2. The list of unsupported features might change in later releases. For more information, see: https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-release-notes.
• #43: Recently moved to the openssl stack. This allows us to use certificates from the linux file system.
• #44: Recently moved to the openssl stack. This allows us to use certificates from the linux file system. Instead of using windows crypto, we now us openssl crypto. This allows customers to run in FIPS mode.
• #45: As stated since our first public preview release back in November 2016, our goal for Sql Server on Linux is to enable all spectrum of HADR solutions available on Windows today. CTP1 release included Backup/Restore and shared disk FCI and with CTP1.3 we released Always On AG capabilities (all flavors). Log shipping is in progress to being released as well.   The focus of this session is Always On AGs, as a mission critical HADR solution for SQL Server on Linux. So let's go through a series of scenarios that AGs are enabling for SQL Server 2017.
• #46: For start, let's assume you are an organization with an all Linux infrastructure. You are building a mission critical application and now with SQL Server being available on the new platform, you are considering using SQL Server on Linux. To enable such scenarios we are bringing our flagship HA solution Always On AG on Linux.   This feature is now available on all Linux OS distributions SQL Server v.Next supports — Red Hat Enterprise Linux, Ubuntu and SUSE Linux Enterprise Server. Also, all capabilities that make Availability Groups a flexible, integrated and efficient HADR solution are available on Linux as well: multidatabase failover, fast failure detection and failover, transparent failover using floating IP resource, multiple sync/async secondary replicas, manual/automatic failover with no data loss, active secondary replicas available for read/backup workloads, automatic seeding, read-only routing, database level health monitoring and failover trigger, disaster Recovery configurations.   On Windows, Always On depends on Windows Server Failover Cluster (WSFC) for distributed metadata storage, failure detection and failover orchestration. On Linux, we are enabling Availability Groups to integrate natively with Pacemaker, a popular Linux clustering technology. Users can add a previously configured SQL Server Availability Group as a resource to a Pacemaker cluster and all the orchestration regarding monitoring, failure detection and failover is taken care of. To achieve this, customers will use the SQL Server Resource Agent for Pacemaker available with the mssql-server-ha package, that is installed alongside mssql-server.
• #47: Replace notes
• #48: In a typical database application, there are multiple clients running various types of workloads that can lead to bottlenecks due to resource constraints. To free up resources and achieve higher throughput for the OLTP workload or to deliver higher performance and scale on read-only workloads, customers can leverage the fastest replication technology for SQL Server and create a group of replicated databases to offload read workload. They can do this by using Availability Groups. With Availability Groups, one or more secondary replicas can be configured to support read-only access to secondary databases and/or to permit backups on secondary databases. The secondary replicas allow direct read-only querying or can enforce connections that specify ‘ReadOnly’ as their Application Intent. Or SQL Server offer load balancing capabilities by routing incoming connections to an availability group listener to a secondary replica that is configured to allow read-only workloads. From SQL Server 2016, we have an option to create a group of secondary replicas as part of the routing list that can be used in round-robin fashion. I mention before offloading reporting and analytics workloads to the secondary replicas. In addition, secondary replicas can also be used to offset backup operations or DBCC checks.
• #49: Q: This is great! What happens if the read-only clients are spread across various geographic areas? The solution for this scenario is using Distributed Availability Groups. This allows offloading read workloads from the primary to replicas on sites that are closer to the source of the read workloads. This not only reduces the utilization of resources on the primary, but also helps with read throughput. Network latency is reduced and read only workloads will benefit from the dedicated resources. A single distributed availability group can have up to 17 readable secondary replicas. For increased scaling capacity, user can daisy chain multiple availability groups and increase the number of readable replicas even further. Or can also do two distributed availability groups from the same primary availability group.