SAP HANA (high-performance analytic appliance) is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading it from a disk.
SAP HANA is an in-memory computing engine (IMCE) used for real-time processing of huge volumes of data and building and deploying real-world applications. Adopting the row-based and column-based DB technology, SAP HANA is an advanced relational DB product serviced by SAP SE. With this high-performance analytic (HANA) system, the big data exists on the main memory and not on the hard disk. It replaces the onus of data maintenance separately on the legacy system and simplifies the tasks of administrators in this digital world.
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.
Sikka, the company’s top technology leader, was the leading advocate of SAP’s Hana in-memory platform. He was also the protégé of SAP Supervisory Board Chairman Hasso Plattner, Hana’s earliest proponent and inventor.
The two types of relational data stored in HANA includes
More than 70% of customers run their SAP workloads on Linux with the use of SUSE Linux Enterprise Server, which is the best OS choice for SAP HANA.
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.
With the HANA technology, you can create gen-next applications giving effective and efficient results in the digital economy.
By using singe data-in memory, SAP HANA supports smooth transaction process and fault-tolerant analytics
Easy and simple operations using an open-source, unified platform in the cloud
High-level Data Integration to access massive amounts of data
Advanced tools for in-depth analysis of present, past and the future.
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
No data approach can be faster than row-based if you want to analyze, process and retrieve one record at one time.
Row-based tables are useful when there is specific demand of accessing complete record.
It is preferred when the table consists of less number of rows.
This data storage and processing approach is easier and effective without any aggregations and fast searching.
The data retrieval and processing operations involve the complete row, even though all the information is not useful.
There are three different compression techniques
Allows smoother parallel processing of data as the data in columns is stored vertically. Thus, to access data from multiple columns, every operation can be allocated to a separate processor core.
Only specific columns need to be approached for Select query and any column can be used for indexing.
Efficient operations since most columns hold unique values and thus, high compression rate.
Latency is referred to the length of time to replicate data from the source system to the target system.
Since analytic applications require massive aggregations and agile data processing, column-based tables are preferred in SAP HANA as the data in column is stored consequently, one after the other enabling faster and easier readability and retrieval. Thus, columnar storage is preferred on most OLAP (SQL) queries. On the contrary, row-based tables force users to read and access all the information in a row, even though you require data from few and/or specific columns.
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.
Index Server consists of actual data engines for data processing including input SQL and MDX statements and performs authentic transactions.
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.
The persistence layer in SAP HANA handles all logging operations and transactions for secured backup and data restoring. This layer manages data stored in both rows and columns and provides steady savepoints. Built on the concept of persistence layer of SAP’s relational database, it ensures successful data restores.
Besides managing log data on the disk, HANA’s persistence layer allows read and write data operations via all storage interfaces.
To avoid un-necessary information from being stored, you have to pause the replication by stopping the schema-related jobs
Modeling Studio is an operational tool in SAP HANA based on Eclipse development and administration, which includes live project creation.
It also handles various data services to perform data input from SAP warehouse and other related databases.
Responsible for scheduling data replication tasks.
The job is arranged on demand and is responsible for
Creating database triggers and logging table into the source system
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.
SLT expands to SAP Landscape Transformation referring to trigger –based replication. SLT replication permits data transfer from source to target, where the source can be SAP or non-SAP while the target system has to be SAP HANA with HANA database. Users can accomplish data replication from multiple sources. The three replication techniques supported by HANA are:
SAP Business Objects Data Services (BODS)
SAP HANA Direct Extractor Connection (DXC)
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.
During a regular operation, data is by default stored to the disk at savepoints in SAPHANA. As soon a there is any update and transaction, logs become active and get saved from the disk memory. In case of power failure, the database restarts like any other DB returning to the last savepoint log state. SAP HANA requires backup to protect against disk failure and reset DB to the previous state. The backups simultaneously as the users keep performing their tasks.
In the HANA database, each SQL statement is implemented in the reference of the transaction. New session is allotted to a new transaction.
Configuration is the meaningful information to establish a connection between source, SLT system and SAP HANA architecture as stated in the SLT system. Programmers are allowed to illustrate a new Configuration in Configuration and Monitoring Dashboard.
The waiting process for data to load from the main memory to the CPU cache is called Stall.
There are primarily three types of information views in SAP HANA, which are all non-materialized.
They are SLT Replication Application Servers to provide configuration information for data replication. This replication status can also be monitored.
Logging table records all replicated changes in the table, which can be further replicated to the target system.
Using advanced replication settings, transformation rules are specified to transfer data from source tables during replication process. For instance, setting rules to covert fields, fill vacant fields and skip records. These rules are structured using advanced replication settings (transaction IUUC_REPL_CONT)
SAP HANA DB
SAP HANA Studio
SAP HANA Appliance
SAP HANA Application Cloud
SAP HANA transaction manager synchronizes database transactions keeping the record of closed and open transactions. When a transaction is committed or rolled back, the manager informs all the active stores and engines about the action so that they can perform required actions in time.
Each SQL statement in SAP HANA is carried out in the form of a transaction. Every time, a new session is allocated to a new transaction. An operator is a special character that is used in SQL statement with WHERE clause for performing operations. The various operators in SAP HANA are logical, arithmetic, comparison, and set.
A Master-controller job is responsible to build database logging table in the source system. It further creates synonyms and new entries in SLT server admin when the table loads / replicates.
Pause the replication process and terminate the schema-related jobs.
The number of data transfer jobs change when the initial loading speed or latency replication time is not up to the mark. At the end of the initial load, the number of initial load jobs may be reduced.
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