SAP is now considered as a database vendor along with being a successful business application vendor. Vying with illustrious database vendors such as Oracle, SAP has developed many database products which have merging characteristics. SAP HANA outperforms the Sybase IQ-data warehouse because it is a column-oriented application that utilizes data compression through the method of parallel processing. The biggest difference between the two applications is how they store and process their memory. Sybase IQ uses RAM as a temporary cache while the data is physically stored on hard disks, whereas SAP HANA requires all the data on RAM. This helps SAP HANA execute programs 10 times faster than Sybase IQ, even if Sybase IQ is programmed to load all the data from the cache simultaneously. The important question that arises is- what are the major differences between SAP HANA and Hadoop?
SAP HANA
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Memory is expensive. RAM is ten times more expensive than physical disk memory and RAM is the unit of data size in SAP HANA applications.
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Searching and OLAP can be performed quickly and efficiently. Data analysis occurs faster.
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It has been designed to perform data analysis (OLAP) as well transaction processing (OLTP) and solve any problems in these fields in the same database. This means that a chunk data does not need to be removed and transferred to another storage area for analysis like most OLTP databases.
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The cost of correlating another data mart to the application for analysis is eliminated as opposed to traditional OLTP DB.
Hadoop
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Incredibly sized data arrays can be easily incorporated in Hadoop because the cost of unit size of data is very less. It helps in the analysis of large volumes of input data which were previously ignored.
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It provides slower data analysis. This can be improved using Shark and Impala, but they add more RAM to the current memory which makes the application expensive.
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It is more suitable for data analysis and transformation.
Integration of SAP HANA and Hadoop
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The initial large volumes of data are sent to Hadoop for pre-processing and clustering purposes. The results of the data transformation performed by Hadoop are then uploaded on SAP HANA for detailed higher-level operational analysis. That being mentioned, the downside includes limited drill-down analysis.
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The data is set is divided into 2 halves: – hot data and cold data. Hot data includes data such as the sales of the previous year, and cold data includes the rest of the company history. Hadoop is used to carefully scrutinize and analyse the cold data, whereas SAP HANA works on the hot data.
The better career choice
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A better career choice lies in the realm of Hadoop Interview Questions. It is the most powerful Big Data platform and many software vendors are using Hadoop to target distributed file storage systems and processing architecture and development. There is an increasing demand for Hadoop programmers.
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It is important to understand that Hadoop is a very tempting option for most globally recognized companies to procure, mine, analyse, store, transfer, and share massive chunks of data in any field of business. Hadoop is up and rising as a global tool for data mining and processing.
