Showing posts with the label data processing

HBASE Vs. RDBMS Top Differences You can Unlock Now

HBASE in the Big data context has a lot of benefits over RDBMS. The listed differences below make you understandable why HBASE is popular in Hadoop (or Bigdata) platform. Let us check one by one quickly. HBASE Vs. RDBMS Differences Random Accessing HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database. Database Rules RDBMS strictly follow Codd's 12 rules with fixed schemas and row-oriented manner of database and also follow ACID properties. HBase follows BASE properties and implement complex queries. Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS. Storage From small to medium storage application there is the use of RDBMS that provide the solution with MySQL and PostgreSQL whose size increase with concurrency and performance.  Codd'

SAP HANA: Top Data Processing Interview Questions

1. How parallel processing is achieved in SAP HANA? The phrase "divide and conquer" (derived from the Latin saying divide et impera) typically is used when a large problem is divided into a number of smaller, easier-to-solve problems. Regarding performance, processing huge amounts of data is a problem that can be solved by splitting the data into smaller chunks of data, which can be processed in parallel. 2.How data portioning will happen in SAP HANA? Although servers that are available today can hold terabytes of data in memory and provide up to eight processors per server with up to 10 cores per processor, the amount of data that is stored in an in-memory database or the computing power that is needed to process such quantities of data might exceed the capacity of a single server. To accommodate the memory and computing power requirements that go beyond the limits of a single server, data can be divided into subsets and placed across a cluster of servers,