Storage Support of Azure for SAP Workloads

By Ashwin Venugopal -

 



Azure VM NFS Storage

Azure VM NFS storage is widely available for SAP workloads can be easily implemented by using Distributed Replicated Block Device (DRBD) with the help of Pacemaker clustering on Azure VMs running SUSE and also by using GlusterFS clustering on RHEL.

Managed NFS storage is available in the form of Azure NetApp Files which is an enterprise-class, high-performance, metered file storage service. Azure NetApp Files supports a wide range of workloads and is highly available by default. Snapshots set up and service as well as performance level can be selected through the service. Microsoft has already tested the SAP file shares with Azure NetApp Files for application-level high availability and plans to provide support for SAP HANA in the near future, after the Azure NetApp Files are certified by SAP.

Azure VM SMB Storage

SMB shares are highly available for SAP workloads in Azure VMs and can be readily implemented by using Storage Space Direct (S2D)-based clusters with the Scale-Out File Servers (SOFS) role. On the contrary, such shares can be generated by leveraging third party solutions (such as SIOS DataKeeper) that emulates shared disks by synchronously replicating the content of the local disks of the clustered Azure VMs. Azure also provides Azure Files that can deliver managed SMB file sharing.  

SAP HANA on Azure (Large Instance) Storage 

The storage layout for SAP HANA (Large Instances) is configured according to SAP recommended guidelines. These guidelines are documented in the SAP HANA storage requirements white paper available at https://www.sap.com/documents/2015/03/74cdb554-5a7c-0010-82c7-eda71af511fa.html  














Comments

Post a Comment

Popular posts from this blog

Query, Visualize, & Monitor Data in Azure Sentinel

By Ashwin Venugopal -
Image
  Azure Sentinel Workbooks Several ready for use templates are provided by the Azure Sentinel that can be used to create your own workbook and then modify them as needed for Contoso. Most of the data connectors it uses to ingest data come with their own workbooks, but better insight can be obtained by simply looking into the data that is being ingested by using tables and visualizations, including bar and pie charts. You can also make your own workbook easily by using this data from the beginning instead of using the predefined templates. Workbook Page You can easily access the workbook page from the Azure Sentinel from the navigation pane which consists of the: Workbook header- You can add a new workbook and review the saved workbooks as well as templates that are available on the workbook page. Templates section- You can access existing workbook templates on the Templates tab. You can save some of the workbooks for quick access and they will appear on the My Workbooks tab.  From the
Read more

Planning for Implementing SAP Solutions on Azure (Part 2 of 5)

By Ashwin Venugopal -
Image
  To read part 1 please click  here To read part 3 please click  here To read part 4 please click  here To read part 5 please click  here Load Balancing The load balancer uses probe ports to determine as well as route the traffic to an active DBMS node, but of there's any failover then the most common SAP application architecture can automatically reconnect against the private virtual IP address without the need for the SAP application to reconfigure it, while the load balancer redirect the traffic against the private virtual IP address without any need for the SAP application to reconfigure. The loaf balancer also offers an option of DirectServerReturn which if configured, can help in directing the traffic from the DBMS VM to the SAP application layer directly to the application layer without routing through the load balancer. But, if it isn't configured, then the return traffic to the SAP application layer is routed through the load balancer. You can also use Azure Load Balan
Read more

Work with String Data Using KQL Statements

By Ashwin Venugopal -
Image
  Extract Data from Unstructured String Fields Unstructured string fields often contains the Security log data and requires parsing to extract data. There are multiple ways of pulling information from string fields in KQL and the two primary operators used are extract and parse.  Extract It gets a match for a regular expression from a text string. You can convert the extracted substring to the indicated type.  Arguments regex- A regular expression. captureGroup- A positive int constant indicating the capture group to extract. 0 stands for the entire match, 1 for the value matched by the first '('parenthesis')' in the regular expression, 2 or more for subsequent parenthesis.  text- A string to search. typeLiteral- An optional type literal. If provided, the extracted substring is converted to this type.  Returns If regex finds a match in text- the substring matched against the indicated capture group captureGroup, optionally converted to typeLiteral. If there's no mat
Read more