SAP HANA training in Chandigarh, Cruisecoders
SAP HANA is the latest ERP solution from SAP. HANA stands for High performance Analytical Appliance
or Hybrid Analytical Appliance.
3 core concepts which are a part of HANA and have revolutionized the reporting and querying –
IMDB – In Memory Database.
CS – Column Store
MPP – Massive Parallel Processing.
Due to these 3 concepts, the queries which used to take around 8 hours now take few seconds to
complete. Let’s understand these concepts one by one.
IMDB – All the data which is required by the CPU is fetched from the disks into RAM for processing.
Disks speed bottlenecks have always been prevalent making the processing slower. In HANA, all the data
resides in the RAM making the data processing faster as there is no longer any need to fetch the data
from disks into the RAM.
CS – In available databases, the data is stored in row format i.e. the data is stored at the disk level row
by row making the searches slower as if some particular data has to be searched, the database engine
has to go through all the rows to find the matching record and then has to manipulate on top of that.
Now, if the storage is done on the column level i.e. each column is stored in a separate data file then
searching becomes faster. ANA database is capable of storing tables both in column store and row store
format.
MPP – SAP HANA utilizes all the available processing power making it possible to process at faster rate.
An SAP HANA consultant is expected to have clear knowledge of the following 3 concepts –
Data Modeling.
Data Reporting.
Data Positioning and Provisioning.
Data Provisioning/Positioning which constitutes 20% of the complete task involved can be done using
any of the below possible tools/solutions made available by SAP based on the requirement.
SLT – SAP Landscape transformation is use if there is a need for the real time data migration
from the Non-SAP system ready to be consumed by HANA.
Clients which have already set up SAP ECC R/3 systems can use the SAP HANA processing
capabilities. All the newly added data is replicated to the HANA database at the real time.
BODS – Batch jobs can be created in SAP BODS (Business Objects Data Services) to migrate the
data into SAP HANA database.
DXC – Direct extractor connector is used if there is a requirement to move the different OLAP
and OLTP systems into a single platform.
SDA – Smart Data Access is the fastest way to migrate the data into the SAP HANA database.
This solution takes the data into intermediate tables and do not need any specific storage space.
Presence of SAP HANA architecture can be felt in one of the following 2 ways –
Side car scenario
Main car scenario
In side car, the legacy system is not disturbed and SAP HANA is used to accelerate the analytical
capabilities and SAP HANA can be used in parallel to the existing architecture.
For instance, legacy system will keep working as usual but, there can be 2 target database systems out
of which can be any database running parallel and storing data with SAP HANA database.
Data Migration optimization (DMO) is used to connect the legacy system and SAP HANA.
If the legacy system is ECC and SAP HANA is used as a way to enhance the performance, this is known as
ECC on HANA. This improves the business processes significantly but, still the organization is not using
the true power of SAP HANA.
Now, Main car is if the entire architecture is replaced by SAP HANA and the existing legacy system is
completely replaced.
Now, this is S/4 HANA as the existing applications use the entire power which SAP HANA has to offer.
There are currently 3 functional modules that have been developed and optimized to run on SAP HANA
are-
Simple Finance
Simple logistics
Simple HR
SAP S/4 HANA is available as an on cloud solution.
Now, moving onto the Data modeling which constitutes to 60% of the total share of SAP-HANA task and
also known as the heart of HANA –
SAP HANA database is capable of storing both the OLAP and OLTP data.
Data Modeling is done on top of views which are created on top of existing database tables –
There are 3 types of Information views –
Attribute view
Analytical view
Calculation view
Attribute views are used to define joins between the tables. They can be used to select the subset of
columns and rows from multiple tables to create a single dimension.
Analytical views are typically defined on at least one fact table that contains transactional data.
Attribute views can be joined using these joins to add more meaning. Analytical views can contain 2
types of columns - measures and key figures. Measures are the attributes for which the aggregation
must be defined. Analytical views model the structure of a star schema and can access the dimension
tables to filter or label the data.
Calculation views are used to provide the composites of other views.
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