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Showing posts with the label RDBMS

5 HBase Vs. RDBMS Top Functional Differences

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HBASE in the Big data context has a lot of benefits over RDBMS. Clear difference makes you feel great why you need HBASE in Hadoop or Bigdata platform. Let us check one by one quickly. HBASE Vs. RDBMS 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. RDBMS strictly follow Codd's 12 rules with fixed schemas and row-oriented manner of database and also follow ACID properties, while 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. 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's rules always need to keep in mind while extending the size of th

RDBMS Vs Key-value Four Top Differences

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This post tells you differences between rdbms and distributed key-value storage. Rdbms is quite  different from key-value storage. RDBMS (Relational Database) You have already used a  r elational  d atabase  m anagement  s ystem — a storage product that's commonly referred to as  RDBMS .  It is basically a structured data. RDBMS systems are fantastically useful to handle moderate data. The BIG challenge is in scaling beyond a single server.  You can't maintain redundant data in rdbms. All the data available on single server. The entire database runs on single server. So when server is down then database may not be available to normal business operations. Outages and server downs are common in this rdbms model of database. Key-Value Database Key-value storage systems often make use of redundancy within hardware resources to prevent outages. This concept is important when you're running thousands of servers because they're bound to suffer hardware bre

RDBMS Vs NOSQL awesome differences to read now

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NoSQL and RDBMS or SQL are different from each other. You may ask what is the difference. Below explained in a way that you can understand quickly. đŸ’¡Traditional Database A schema is required. All traditional data warehouses using RDBMS to store datamarts. Databases understand SQL language. It has a specific format and rules to interact with traditional databases. Less scalable. It has certain limitations.  Expensive to make the databases as scalable Data should be in a certain format. Data stored in row format. NoSQL database The growing internet usage and involving a number of devices caused to invent databases that have the capability to store any kind of data. More: MongoDB 3.2 fundamentals for Developers-Learn with Exercises NoSQL Special Features The schema is not required. Ability to handle multiple data types. This is the power of NoSQL. NoSQL is much suitable for analytical databases. Since those should be flexible, scalable, and able to store any f