SAP HANA Adminstration – Topics

SAP HANA Introduction

• SAP HANA – A short Introduction
• SAP HANA Information sources
• Revision strategy of SAP HANA

Preparing Installation

• Sizing of the SAP HANA
• Requirements

Installation

• Introduction SAP HANA Lifecycle Management Tools
• Advanced installation options
• SAP HANA Studio installation
• SHINE – SAP HANA Interactive Education
• Performing a Distributed System Installation

Post Installation

• Post-Installation Steps
• Updating SAP HANA

Architecture and Scenarios

• SAP HANA Memory Management and Data Persistence
• Software Packaging
• SAP HANA Roadmap and Scenarios
• Deployment Options

Admin Tools for SAP HANA

• SAP HANA studio for administrator
• DBA Cockpit
• HDBSQL command line tool

Operate SAP HANA

• Starting and stopping SAP HANA
• Configuring SAP HANA
• Periodic Manual  tasks
• Transporting changes

Backup and recovery

• Concepts of Backup and Recovery
• Data Area backup
• Log Area backup
• Additional backup topics
• Recovery & Database copy
• Backup and Recovery using storage snapshot

Monitoring and Troubleshooting

• Configuring Traces
• Working with Diagnosis Information and Diagnosis Files
• SQL Console & Query Analysis
• Remote Support

Maintaining Users and Authorizations

• User Management
• Types of Privileges
• Roles
• Administrative tasks
• Information Sources for Administrators
• SAP HANA Live Authorization Assistant

High Availability and Disaster

• High Availability
• SAP HANA Scale Out

Multitenant Database Containers

• Administration of Multitenant Database Containers
• Backup and Recovery of Multitenant Database Containers

Data Provisioning

• Configure data replication with SAP Landscape Transformation (SLT)

SAP BW HANA Migration

• SAP Certification Guidance

Delta Merge in SAP HANA

DELTA merge is an operation to move the data from WRITE optimized DELTAmemory to READ optimized and Compressed MAIN memory. This can be done automatically by HANAusing Smart Merge technology or manually using MERGE DETLA OF sql statement or using right click option on HANA studio.

The Delta Merge Operation is an operation on a table column store data structure.

The purpose of the delta merge operation is to move changes collected in the delta storage to the read-optimized main storage.

After the delta merge operation, the content of the main storage is persisted to disk and its compression recalculated and optimized if necessary.

A further result of the delta merge operation is truncation of the delta log (ie redo operations)

It is important to understand that even if a column store table is unloaded or partly loaded, the whole table is loaded into memory to perform the delta merge.


During the delta merge operation, every partition of a partitioned table is treated internally as a standalone table with its own data and delta store.

SAP HANA Dynamic Tiering

The dynamic tiering option in SAP HANA SPS09 gives the ability to keep the data in either memory or on the disk in a columnar format. Data is not duplicated. Dynamic tiering option helps users to choose memory for hot data and disk for warm data, helping to strike the right price/performance balance. To do so, you define the table as “extended table” using an SQL CREATE statement. These tables are like any other SAP HANA tables except for the fact that they are created on the disk and not in memory. From the application developer point of view, these tables can be queried and modified using standard SQL statements, like any other SAP HANA tables. You can join extended tables with in-memory tables and at any point of time, you can convert extended tables into in-memory tables and vice versa, with an SQL ALTER statement.

HOT---->Warm

Dynamic tiering also works with multitenant database containers and you can configure extended storage for each tenant.

Data Virtualization overview

 Data Virtualization:

Data virtualization is synonymous with information agility - it delivers a simplified, unified, and integrated view of trusted business data in real time or near real time as needed by the consuming applications, processes, analytics, or business users. Data virtualization integrates data from disparate sources, locations and formats, without replicating the data,  to create a single "virtual" data layer that delivers unified data services to support multiple applications and users. The result is faster access to all data, less replication and cost, more agility to change.

Data virtualization is modern data integration. It performs many of the same transformation and quality functions as traditional data integration (Extract-Transform-Load (ETL), data replication, data federation, Enterprise Service Bus (ESB), etc.) but leveraging modern technology to deliver real-time data integration at lower cost, with more speed and agility. It can replace traditional data integration and reduce the need for replicated data marts and data warehouses in many cases, but not entirely.

Data virtualization is also an abstraction layer and a data services layer. In this sense it is highly complementary to use between original and derived data sources, ETL, ESB and other middleware, applications, and devices, whether on-premise or cloud-based, to provide flexibility between layers of information and business technology.