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SAP HANA Appliance

Updated on Sep 30, 2020

SAP In-Memory Appliance (SAP HANA) software is a flexible, multipurpose, data source agnostic in-memory appliance that combines SAP software components optimized on hardware provided and delivered by SAP’s leading hardware partners.

SAP HANA Appliance is a combination well defined and validated stack of Hardware and Software components. SAP HANA 1.0 SPS 03 consists of SAP HANA Database, SAP HANA Client, SAP HANA Studio.

SAP HANA Appliance
SAP HANA Appliance

SAP HANA In-Memory Computing Engine is an ACID-compliant, Massively Parallel Processing hybrid Relational Database for Storing Data In-Memory.

SAP HANA Clients are provided for various operating systems, delivering the required database clients to connect to SAP HANA via JDBC, ODBC, ODBO or BICS.

SAP HANA Studio is an Eclipse based tool used to administer SAP HANA Database, create analytic models in SAP HANA and for data provisioning.

Software Components

SAP HANA Appliance software is available in different editions:

  • SAP HANA Appliance Software Platform Edition
  • SAP HANA Appliance Software Enterprise Edition
  • SAP HANA Appliance Software Enterprise Extended Edition
SAP HANA Software Components
SAP HANA Software Components

Process Flow- SAP HANA

SAP HANA software enables Organizations to instantly explore and analyze huge volumes of detailed transactional data in real time from virtually any data source. Operational data is captured inmemory while business is happening, and flexible views expose analytic information at the speed of thought. Below are the steps involved to gain analytical insight from the transactional data of the source system.

  1. Import Source System Metadata- Create Table Definition or Import table metadata from the source system.
  2. Data Provisioning- Initial Data Loading and Replication of the subsequent changes of the source system tables into SAP HANA target tables.
  3. Create Information Models- Create analytical data models on the top of the Physical Tables. Information models are used to create multiple database views of the transactional data that can be used for analytical purposes.
  4. Consume Analytic Data- Consume or retrieve data from SAP HANA database with a wide variety of client tools. Various connectivity options are provided by SAP HANA- ODBC, JDBC, ODBO, BICS or SQL DBC.
SAP HANA Landscape
SAP HANA Landscape

Data Provisioning

One of the promises of SAP HANA is to deliver real-time analytic insight on vast data volumes. For the real-time aspect, data acquisition in real time is required. This is the task of Sybase Replication Server. Tables from SAP ERP system are initially loaded into SAP HANA. All subsequent changes to these ERP tables are immediately replicated into the HANA server. To this end Replication Server makes use of the database logs in the ERP system. The tool that helps selecting the tables to be loaded and replicated is integrated into the In-Memory Computing Studio.

Data Modeling

Once the tables are created in HANA and loaded from the source system, the semantic relationships between the tables need to be modeled. Modeling can be done in several places.

  1. If Data Services is used to create and populate the table, first layer of modeling can be implemented here.
  2. Analytical Data Models can be created in the Information Modeler perspective of In-Memory Computing Studio.
  3. Depending on the front-end tool used to retrieve data from the In-Memory Computing Engine, further modeling decisions can be made in Universes (Information Design Tool) or other semantic layers.

Analytical Reporting

Due to choice of various connectivity options a wide variety of Reporting tools can be used for the purpose of showcasing data insights. Below are the reporting tools that cater to various needs of Enterprise wide reporting.

  • SAP BusinessObjects Explorer
  • SAP BusinessObjects Analysis, Office Edition
  • Microsoft Excel 2010,
  • SAP BusinessObjects BI 4.0 Suite (Web Intelligence, Dashboards, Crystal Reports)
Business Intelligence Clients and SAP HANA
Business Intelligence Clients and SAP HANA

We can configure SAP BusinessObjects BI suite for Reporting, Interactive Analysis and front-end data visualization. SAP BusinessObjects Universe Designer allows SQL-like access to Data tables and views stored in SAP HANA. Configure SAP BusinessObjects Query as a Web Service (QAAS) to expose query functionality in Universe as a Web Service.

Data Replication Methods

In-memory reporting and analyzing of business data requires the replication of the data from a source system to the SAP HANA database. This section provides an overview of the possible replication methods that are available for the SAP HANA appliance. Three main types of replication methods are Trigger, Log, and ETL based.

  • Trigger-Based Replication: Trigger-Based Data Replication Using SAP Landscape Transformation (LT) Replication Server is based on capturing database changes at a high level of abstraction in the source ERP system. This method of replication benefits from being database-independent, and can also parallelize database changes on multiple tables or by segmenting large table changes.
Trigger-Based Replication
Trigger-Based Replication
  • ETL-Based Replication: Extraction-Transformation-Load (ETL) Based Data Replication uses SAP BusinessObjects Data Services to specify and load the relevant business data in defined periods of time (Batches) from an ERP system into the SAP HANA database. We can reuse the ERP application logic by reading extractors or utilizing SAP function modules. In addition, the ETL-based method offers options for the integration of third-party data providers.
ETL-Based Replication
ETL-Based Replication
  • Log-Based Replication: Transaction Log-Based Data Replication Using Sybase Replication is based on capturing table changes from low-level database log files. This method is database-dependent. Database changes are propagated on a per database transaction basis, and they are then replayed on the SAP HANA database. This means consistency is maintained, but at the cost of not being able to use parallelization to propagate changes. Consider using dedicated private network to data acquisition systems.
Log-Based Replication
Log-Based Replication

We will discuss in detail more on Data Provisioning/Replication & Data Modeling in our next articles.

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