SAP HANA is an in-memory database that allows for real-time analytics by storing and processing large amounts of data in memory. It uses columnar data storage and parallel processing to perform analytics faster. The SAP HANA architecture includes index servers that process queries, a persistence layer for durability, and other components like name and statistics servers. Developers can access SAP HANA using the SAP HANA Studio tool to complete administration, modeling, and development tasks. SAP's roadmap is to move more applications and data into the in-memory platform for real-time analytics and consolidated processing.

1. SAP HANA Basic Introduction

2. Contents
• Overview of SAP HANA
• SAP HANA came into the picture
• What is SAP HANA
• Hardware & Software Innovations
• Data Storage and Processing
• SAP HANA Architecture
• SAP HANA Studio
• SAP HANA ROADMAP

3. Business Example:
Today, a lot of companies need to deal with an amazing amount of data and are not
able to report on them due to the big volume of Data. The purpose of SAP HANA is
to enable easy storages and efficient processing of these data. In particular, SAP
HANA Combines In-Memory Data Storage and Columnar Data Storages.
These are two modern and extremely powerful features of SAP HANA.
Overview of SAP HANA
Now a days we hear a lot of buzz around SAP HANA and more customers planning to
opt for it. What does SAP HANA mean to us? Before we go into its details, we shall
first know the Business Scenario.

4. Information Explosion
The Challenge is the information explosion. Massive amount of data is being created
every year, and how fast your business reacts to it determines whether you succeed of
fail.

5. Major challenges faced by most of the customers with current systems:
• Massive growth of Data Volume
• Immediate results
• Complex system landscape
• High Flexibility
• Skilled workforce
Consequences of these challenges result in:
• Increased response time
• Decreased data transparency
• Reactive systems

6. Traditional Database and System landscape
 Disk Based Storage
 Bottleneck operation
 Traditional database landscape has
several layers in it resulting it to be
complex system.
 Operations – applying logics,
calculations, indexing, aggregations
etc. are performed in application
layer.

7. Traditional Database Storage:
Traditional databases uses row storage alone, which might be useful if table is small or
if a query needs the entire table data. But in case of analytical applications involving
huge data, where aggregations are needed for faster response, row storage holds back.

8. SAP HANA came into the picture
 Top 10 predictions (Released in November 2015) estimates that the “Digital Universe”
will reach 4ZB (1 zettabyte= 1 trillion gigabytes).
 People want instant access to information.
 Data is growing. Demand is increasing.
 People want instant answers. They want them to be right. They want them any
where, any time.
This put IT in a tough place, IT can not deliver what the business needs, Why ?

9. SAP HANA is the Composition of three separate products:
 TREX :(Text Retrieval and Extraction) is a search engine.
 P*Time : (Online Transaction processing ) OLTP, is an OLTP Relation Database
Management System.
 MaxDB :(formerly SAP DB) is Relational Database management System (RDBMS),
MaxDB is targeted for large SAP environments.

10. TREX P*TIME MaxDB

11. What is SAP HANA
SAP HANA -- High Performance Analytic Appliance
 SAP HANA is a modern In-Memory platform that can be deployed on premise and on
the cloud.
 SAP HANA is the In-memory relational Database that made to process the real-time
data in In-memory Computing Engine.
 SAP HANA is not only a Database but also a Platform where we can create application
and can deploy on HANA Cloud Platform.

12. As the appliance SAP HANA is the combination of special kind of software and
hardware. SAP has partnered with leading hardware vendors (HP, Fujitsu, IBM, Dell etc)
to sell SAP certified hardware for HANA.

13. Hardware & Software Innovations
HANA runs on Multi-core CPUs, having very fast communication between each
processor core.
Each server holds up to 2TB.

14. Data resides in main memory avoiding performance
on disk I/O.
operations are performed in database layer instead of
application layer
Columnar data storage allows high compression
(dictionary encoding, run-length encoding)
Operations on one column can be parallelized by
partitioning the column into multiple sections
that can be processed by different processor
cores. Helps analyze large data sets with complex
operations.

16. Data Storage and Processing
 A row based approach store a table as a sequence of records, each of which
Contain the fields of one row.
 In a column based table, the entries of a column are stored in continuous
memory location.

17. Column Store:
• High data compression
• Read optimized – Query Results On the fly
• Useful with tables with huge data volumes
• Supports Parallel Processing – Operations run in parallel using multiple core processors.

18. SAP HANA Architecture
 The SAP HANA database is
developed in C++.
 runs on SUSE Linux Enterprise
Server.

19.  Index Server:
• Index server is the main SAP HANA database component.
• It 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
authenticated sessions and transactions.
 Persistence Layer: The database persistence layer is responsible for durability and
atomicity of transactions. It ensures that the database can be restored to the most recent
committed state after a restart and that transactions are either completely executed or
completely undone.
 Preprocessor Server: The index server uses the preprocessor server for analyzing text
data and extracting the information on which the text search capabilities are based.
 Name Server: The name server owns the information about the topology of SAP HANA
system. In a distributed system, the name server knows where the components are
running and which data is located on which server.
 Preprocessor Server: The index server uses the preprocessor server for analyzing text
data and extracting the information on which the text search capabilities are based.

20.  Name Server: The name server owns the information about the topology of SAP HANA
system. In a distributed system, the name server knows where the components are
running and which data is located on which server.
 Statistic Server: The statistics server collects information about status, performance and
resource consumption from the other servers in the system.. The statistics server also
provides a history of measurement data for further analysis.
 Session and Transaction Manager: 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.
XS Engine:XS Engine is an optional component. Using XS Engine clients can connect to
SAP HANA database to fetch data via HTTP.

21. SAP HANA Studio
The SAP HANA studio is an Eclipse-
based development and
administration tool for working with
HANA.
SAP HANA Studio is a kind of front-
end of SAP HANA Database, where
you can manage the data.
It enables technical users to manage
the SAP HANA database, to create and
manage user

22. To open a perspective, go to Window → Open Perspective.
 Modeler: Modeler Used for creating various types of In formation views and
analytical privileges. Modeler is mainly used for modeling the database by creating
below type of views:
• Attribute View
• Analytic View
• Calculation View
 SAP HANA Development: Development Used for programming applications for
creating development objects to access or update data models such as Server-side
Java script, XSJS, XSODATA files.
 Administration: Used to monitor the system and change settings.

23. SAP HANA ROADMAP
Today’s system landscape
• ERP running on traditional database.
• BW running on traditional DB.
• Data extracted from ERP and load into
BW.
• BWA accelerates Analytic modals.
• Analytic data consumed in BI or pulled
into data marts.

24. • Operational Data in traditional DB is
replicated into memory for operational
reporting.
• Analytic modals from production EDW can be
brought into memory for agile modeling and
reporting.
• Third party data can be brought into memory
for agile modeling and reporting.
SAP HANA ROADMAP
Step-1 In-Memory in parallel

25. SAP HANA ROADMAP
Step-2 Primary Data store for BW
• In Memory Computing used
for as a primary persistence
for BW
• BW manage the analytic
metadata.
• Detailed operational Data
replicated from applications is
the basis for all process.
• SAP HANA 1.x will be able to
provide of the functionality
BWA

26. SAP HANA ROADMAP
Step-5 Platform Consolidation
• All Application(BW & ERP)
run on data residing in-
memory.
• Analytics and operation
work on data real-time.
• In-memory computing
executes all transactions,
transformation and
complex data processing.