SAP HANA stands for High Performance Analytical Appliance- in-memory computing engine. HANA is
linked to ERP systems; Frontend modeling studio can be used for replication server management and load
control.

The two types of relational data stored in HANA includes

• Row Store
• Column Store

SAP HANA has an in-memory computing engine and access the data straightaway without any backup. To
avoid the risk of losing data in case of hardware failure or power cutoff, persistence layer comes as a savior
and stores all the data in the hard drive which is not volatile.

Modeling studio in HANA performs multiple task like

• Declares which tables are stored in HANA, first part is to get the meta-data and then
• schedule data replication jobs
• Manage Data Services to enter the data from SAP Business Warehouse and other
• systems
• Manage ERP instances connection, the current release does not support connecting to
• several ERP instances
• Use data services for the modeling
• Do modeling in HANA itself
• essential licenses for SAP BO data services

There are three different compression techniques

• Run-length encoding
• Cluster encoding
• Dictionary encoding

Latency is referred to the length of time to replicate data from the source system to the target system.

Transformation rule is the rule specified in the advanced replication setting transaction for the source table
such that data is transformed during the replication process.

• SAP SLT works on trigger based approach; such approach has no measurable performance impact in the

source system

• It offers filtering capability and transformation
• It enables real-time data replication, replicating only related data into HANA from non-
• SAP and SAP source systems
• It is fully integrated with HANA studios
• Replication from several source systems to one HANA system is allowed, also from one source system to

multiple HANA systems is allowed.

To avoid un-necessary information from being stored, you have to pause the replication by stopping the
schema-related jobs

The job is arranged on demand and is responsible for

• Creating database triggers and logging table into the source system
• Creating Synonyms
• Writing new entries in admin tables in SLT server when a table is replicated/loaded

If the replication is suspended for a longer period of time, the size of the logging tables increases.

The transaction manager co-ordinates database transactions and keeps a record of running and closed
transactions. When transaction is rolled back or committed, the transaction manager notifies the involved
storage engines about the event so they can run necessary actions.

You can avoid un-necessary logging information from being stored by pausing the replication by stopping
the schema-related jobs.

In the HANA database, each SQL statement is implemented in the reference of the transaction. New
session is allotted to a new transaction.

• SAP HANA DB
• SAP HANA Studio
• SAP HANA Appliance
• SAP HANA Application Cloud

The information to create the connection between the source system, SLT system, and the SAP HANA
system is specified within the SLT system as a Configuration. You can define a new configuration in
Configuration & Monitoring Dashboard (transaction LTR).

SLT stands for SAP Landscape Transformation which is a trigger based replication. SLT replication server
is the replication technology to pass data from source system to the target system. The source can be either
SAP or non-SAP. Target system is SAP HANA system which contains HANA database.

It is a set of SQL extensions for SAP HANA database which allow developers to push data intensive logic
into the database.

It should be used in cases where other modeling constructs of HANA such as Attribute views or Analytic
views are not sufficient.

When a user creates a new procedure, the HANA database query compiler first:
Parse the statements
Check the statement semantic correctness
Optimize the code for Declarative and Imperative logic
Code generation creates Calculation models for Declarative logic and L nodes for Imperative logic
Compiled procedure creates Content in the database Catalog and in Repository.

During compilation, the call to the procedure is rewritten for processing by the calculation engine.

It binds the actual parameters to the calculation models generated in the definition phase.

SQLScript allows the definitions of table types and also supports primitive data types such as Integer,
Decimal(p,q), Varchar, Blob, Timestamp, etc.

No they are not same as HANA database supports compression.

If the table size in HANA database exceeds 2 billion records, the table should be splitted by using
portioning features by using "Advanced replication settings" (transaction IUUC_REPL_CONT, tab page
IUUC_REPL_TABSTG).

Transformation rules are defined by using “Advanced replication settings” (transaction
IUUC_REPL_CONT, tab page IUUC ASS RULE MAP)

HANA is the name for the current BI appliance (HANA 1.0) and the BW Data Warehouse appliance
(HANA 1.0 SP03). Both use the SAP IMDB Database Technology (SAP HANA Database) as their
underlying RDBMS. SAP can be expected to start to differentiate this more clearly as it starts to position
the technology for use cases other than Analytics.

The information can be retrieved from Regular RDBMS technologies put the information on spinning
plates of iron (hard disks). The information is stored in electronic memory, which is some 50x faster
(depending on how you calculate). It stores a copy on magnetic disk, in case of power failure or the like.
Also to add in, most SAP systems have the database on one system and a calculation engine on another,
and the information is passed between them. With HANA, this all happens within the same machine.

The information required to create the connection between the source system, SLT system, and the SAP
HANA system is specified within the SLT system as a Configuration. A new configuration in
Configuration & Monitoring Dashboard (transaction LTR) can hence be defined.

DMIS add-on is installed in SLT replication server for SAP source systems. The role
IUUC_REPL_REMOTE is assigned to the user for RFC connection but not DDIC. DMIS add-on is not
required and grants a database user sufficient authorization for data replication for a non-SAP source
system.

They are applications that run on SLT replication server to specify configuration information (such as
source system, target system, and relevant connections) so that data can be replicated. The replication
status (transaction LTR) can also be monitored. Status Yellow occurs due to triggers which are not yet
created successfully. Status Red occurs if master job is aborted (manually in transaction SM37).

Latency is the length of time to replicate data (a table entry) from the source system to the target system.

A logging table in the source system that can record any changes to a table that is being replicated. This
thereby ensures that SLT replication server can replicate these changes to the target system.

Transformation rule is a rule specified in the Advanced Replication settings transaction for source tables
such a way that the data is transformed during the replication process. For example one can specify rule to
Convert fields, Fill empty fields and Skip records

The database connection is automatically created along with GUID and Mass transfer id (MT_ID).
Configurations with the same schema name can be created which is ensured by schema GUID. The naming
of SLT jobs and the system can uniquely identify a schema where the Mass transfer ID is used.

The factors that influence the change or increase in the number of jobs are number of configurations
managed by the SLT replication server, number of tables to be loaded/ replicated for each configuration,
expected speed of initial load, expected replication latency time. As a thumb rule, one BDG job should be
used for each 10 tables in replication to achieve acceptable latency times.

The number of data Transfer jobs changes when the speed of the initial load/replication latency time is not
satisfactory, if SLT replication server has more resources than that is initially available. The number of
initial load jobs may be reduced after the completion of the initial load

The main jobs involved in the replication process are stated as below:

Master Job (IUUC_MONITOR_<MT_ID>), Master Controlling Job (IUUC_REPLIC_CNTR_<MT_ID>)
Data Load Job (DTL_MT_DATA_LOAD_<MT_ID>_<2digits>), Migration Object Definition Job
(IUUC_DEF_MIG_OBJ_<2digits>), Access Plan Calculation Job
(ACC_PLAN_CALC_<MT_ID>_<2digits>)

Platform and Enterprise edition.

Platform edition is intended for customers who want to use ETL-based replication and already have a
license for SAP BO Data Services.
Enterprise edition is intended for customers who want to use either trigger-based replication or ETL-based
replication and do not already have all of the necessary licenses for SAP BO Data Services.

We create hierarchies between attributes to improve analysis by displaying attributes according to their
defined relationships. There are two types of hierarchies:
Level Hierarchy: The root and child nodes are accessed only in the defined order. It consists of one or
more levels of aggregation.
Example: We can drill down from Country to State and to City etc.
Parent/Child Hierarchy: This hierarchy contains a parent attribute and constructed from a single parent
attribute.
Example: Employee master (employee and manager).

One reason is the number of CPU cycles per second is increasing and the cost of processors is decreasing.
For managing the data in memory, there is five-minute rule which is based on the suggestion that it costs
more to wait for the data to be fetched from disk than it costs to keep data in memory so it depends on how
often you fetch the data.
For example there is a table and no matter how large it is and this table is touched by a query at least once
every 55 minutes, it is less expensive (in hardware costs) to keep it in memory than to read it from memory
and if it is frequently accessed it is less expensive to store it in memory.

SAP HANA 1.0 is an analytics appliance which consists of certified hardware, and In Memory Database
(IMDB), an Analytics Engine and some tooling for getting data in and out of HANA. The logic and
structures are built by the user and a tool is used e.g. SAP BusinessObjects, to visualize or analyze data.

The limitations are very less. It can only replicate certain data from certain databases and in certain
formats using the Sybase Replication Server. The Batch loading is done using SAP BusinessObjects Data
Services 4.0 and is optimized only for SAP Business Objects BI 4.0 reporting.

They are all the same thing, and 1.0 SP03 is touted to be the final name which should go into RampUp
(beta) in Q4 2011. This allows any SAP NetWeaver BW 7.3 Data Warehouse to be migrated into a HANA
appliance. HANA 1.0 SP03 specifically also accelerates BW calculations and planning, which means even
more performance gains can be attained.

HANA is the name for the current BI appliance (HANA 1.0) and the BW Data Warehouse appliance
(HANA 1.0 SP03). Both use the SAP IMDB Database Technology (SAP HANA Database) as their
underlying RDBMS. SAP can be expected to start to differentiate this more clearly as it starts to position
the technology for use cases other than Analytics.

The most important thing to note is, HANA brings to the table is the ability to aggregate large data
volumes in near real-time - and to have the data updated in near real-time. Demos show hundreds of
billions of records of data being aggregated in a matter of seconds. Hence SAP has built a set of Analytics
Apps on top of HANA and these are set to be great point use cases to get customers up and running
quickly.

Clients connect to the database system which forms a session within the database in the form of SQL
statements. In the HANA database, each SQL statement is processed in the context of a transaction. New
sessions are assigned to a new transaction.
Traditional database applications uses JDBC and ODBC interface to communicate with the database
management system over a network connection and application uses SQL to manage and query the data
stored in the database. In the HANA database Index server is the main component of database management
which contains the actual data stores and the engines for processing the data. The index server processes
incoming SQL or MDX statements in the context of transaction.
The Transaction manager coordinates database transactions, and keeps track of running and closed
transactions. When a transaction is committed or rolled back, the transaction manager informs the involved
storage engines about this event so they can execute necessary actions.
The HANA database has its own scripting language called SQLScript that is designed to enable
optimization and parallelization. HANA supports the Business Functional Library (BFL) and Predictive
Analysis Library (PAL) and can be called directly from within SQLScript. It also supports the
development of programs written in R language.

SQL and SQLScript are implemented using a common infrastructure of Built-in functions. That have
access to various meta definitions such as definitions of relational tables, columns, views, and indexes,
definitions of SQLScript procedures. This Metadata is stored in one common catalog (row store or column
store).
The Persistence layer ensures that the database after a restart is restored to the most recent committed state.
It uses a combination of write-ahead logs, shadow paging and save points. The persistence layer also
contains Logger that manages the transaction log.
The Index server uses the Preprocessor Server for analyzing the text data and extracting the information
based on text search capabilities. The Name Server knows where the components are running and which
data is located on which server. The Statistics Server collects information about status, performance and
resource consumption from other servers in the system.

Central information repository for application and system data. The ABAP Dictionary contains data definitions
(metadata) that allow you to describe all of the data structures in the system (like tables, views, and data types) in one
place. This eliminates redundancy.