Hello Everyone,
How are you? It's been a long time since I wrote my last blog. My intent in writing is to always explain the technology in the simplest of language.
For this blog let us try to understand HANA Database terminologies, that we may or may not know while we get busy with our day-to-day tasks.
So below are the terms I am planning to explore. In this blog, I will cover the first 5 terms. The rest will be covered in part 2.
So if I say I am storing a lot of data in Excel in the form of rows and columns, can I call this data as a database. Technically yes.
DBMS stands for Database Management System, and RDBMS is the acronym for the Relational Database Management system. In DBMS, the data is stored as a file, whereas in RDBMS, data is stored in the form of tables.
Below will give you a glimpse of the difference.
Our HANA Database falls under the category of RDBMS. So when we say that we have a HANA database, we are referring to the software used to access the database. So there can be any database software Oracle or HANA; its primary goal is to access the data most efficiently.
Below is the diagrammatic illustration for the same
HANA Database is the database instance that is installed to manage your data. Its primary task is to fetch the data from the storage data and provide you a computed results.
But as HANA is an in-memory database it holds your data in persistence memory and DRAM (Volatile Memory). We will cover this topic later.
The operating system serves as a platform where the database is installed and the data is stored in the form of relational tables. Currently, SAP HANA is available on SUSE Linux and Red Hat Linux.
Physical Machine storage has your data and log files. It is basically your SSD or hard disk.
Scale-out means combining multiple independent servers into one large SAP HANA database system. The main reason for distributing an SAP HANA database system across multiple hosts (scaling out) is to overcome the hardware limitations of a single physical server. This allows an SAP HANA database system to distribute the load between multiple servers.
Scale-up means increasing the size of one physical machine by increasing the amount of RAM available for storing and processing data. Increasing the RAM also means increasing the number of CPUs, which means increasing the processing power of SAP HANA.
Scale-out and scale-up are the two general approaches you can take to increase the processing power and storage capacity of your SAP HANA system.
Data tiering means arranging or organising something in tiers. Seats in the theatre are arranged in a tiered fashion. So we can segment our data as hot, warm, and cold.
Hot Tier -
Data classified as hot is accessed frequently and/or needs very high performance. The storage basis for hot data is the HANA memory. This includes both DRAM- Dynamic Random Access Memory (Volatile Memory/Hot+++) and PMEM-Persistence Memory (Non-Volatile Memory/Hot++)
Warm Tier -
For warm data, the SAP HANA solution is a native storage extension (NSE) such as SSD or hard disk. NSE is a general-purpose, built-in warm data store in SAP HANA that lets you manage less-frequently accessed data without fully loading it into memory.
Cold Tier -
Cold data can be stored on the lowest cost storage tiers but still remain accessible to SAP HANA via SQL on request. A number of options are available such as SAP IQ.
I hope I was able to provide some meaningful explanations for all terms, technology itself is not very typical, if we try to understand it's all ifs and buts.
Please stay connected for Part 2.
I will update the link in the same blog as well.
Please provide your feedback on the quality of the content and the understanding.
It takes me a lot of time to get these minute details and then simplify it.
Happy Learning !!
How are you? It's been a long time since I wrote my last blog. My intent in writing is to always explain the technology in the simplest of language.
For this blog let us try to understand HANA Database terminologies, that we may or may not know while we get busy with our day-to-day tasks.
So below are the terms I am planning to explore. In this blog, I will cover the first 5 terms. The rest will be covered in part 2.
- Database
- HANA Database
- Scale-out
- Scale-up
- Data Tiering
- Persistence Memory
- MDC, MCOD
- HANA Server Components
- High Availability
- Disaster Recovery
Database
So if I say I am storing a lot of data in Excel in the form of rows and columns, can I call this data as a database. Technically yes.
DBMS stands for Database Management System, and RDBMS is the acronym for the Relational Database Management system. In DBMS, the data is stored as a file, whereas in RDBMS, data is stored in the form of tables.
Below will give you a glimpse of the difference.
HANA Database
Our HANA Database falls under the category of RDBMS. So when we say that we have a HANA database, we are referring to the software used to access the database. So there can be any database software Oracle or HANA; its primary goal is to access the data most efficiently.
Below is the diagrammatic illustration for the same
HANA Database is the database instance that is installed to manage your data. Its primary task is to fetch the data from the storage data and provide you a computed results.
But as HANA is an in-memory database it holds your data in persistence memory and DRAM (Volatile Memory). We will cover this topic later.
The operating system serves as a platform where the database is installed and the data is stored in the form of relational tables. Currently, SAP HANA is available on SUSE Linux and Red Hat Linux.
Physical Machine storage has your data and log files. It is basically your SSD or hard disk.
Scale-out
Scale-out means combining multiple independent servers into one large SAP HANA database system. The main reason for distributing an SAP HANA database system across multiple hosts (scaling out) is to overcome the hardware limitations of a single physical server. This allows an SAP HANA database system to distribute the load between multiple servers.
Scale-up
Scale-up means increasing the size of one physical machine by increasing the amount of RAM available for storing and processing data. Increasing the RAM also means increasing the number of CPUs, which means increasing the processing power of SAP HANA.
Scale-out and scale-up are the two general approaches you can take to increase the processing power and storage capacity of your SAP HANA system.
Data Tiering
Data tiering means arranging or organising something in tiers. Seats in the theatre are arranged in a tiered fashion. So we can segment our data as hot, warm, and cold.
Hot Tier -
Data classified as hot is accessed frequently and/or needs very high performance. The storage basis for hot data is the HANA memory. This includes both DRAM- Dynamic Random Access Memory (Volatile Memory/Hot+++) and PMEM-Persistence Memory (Non-Volatile Memory/Hot++)
Warm Tier -
For warm data, the SAP HANA solution is a native storage extension (NSE) such as SSD or hard disk. NSE is a general-purpose, built-in warm data store in SAP HANA that lets you manage less-frequently accessed data without fully loading it into memory.
Cold Tier -
Cold data can be stored on the lowest cost storage tiers but still remain accessible to SAP HANA via SQL on request. A number of options are available such as SAP IQ.
I hope I was able to provide some meaningful explanations for all terms, technology itself is not very typical, if we try to understand it's all ifs and buts.
Please stay connected for Part 2.
I will update the link in the same blog as well.
Please provide your feedback on the quality of the content and the understanding.
It takes me a lot of time to get these minute details and then simplify it.
Happy Learning !!
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