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8 Top key points in Apache Cassandra in the age of Big data

Hadoop Questions
(Hadoop questions...)
Decentralized: Every knot within the array has the similar part. There is no sole point of letdown. Data is dispersed athwart the array (so every one node holds dissimilar data), however there is no principal as any knot may facility whatever appeal.

Supports replication and multi information centre replication: Replication strategic plans are configurable. Cassandra is developed like a dispersed configuration, for distribution of great numerals of nodes athwart numerous information hubs. Key attributes of Cassandra’s dispersed design are especially custom-made for multiple-data centre distribution, for superfluity, for a procedure by which a system automatically transfers control to a duplicate system when it detects a fault or failure and calamity recuperation.

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Scalability: Read and record output either rise linearly as spic-and-span devices are appended, with no layoff either discontinuity to applications.

Fault-tolerant: Data is automatedly cloned to numerous knots for Fault-tolerance. Replication athwart numerous information hubs is maintained. Failed knots may be substituted with no layoff.

Tunable consistency: Writes and peruses proffer a tunable layer of consistence, altogether the way as of ‘writes not ever fail’ to ‘block for altogether replicas to be readable’, with the minimum number layer in the mid.

MapReduce support: Cassandra has Hadoop incorporation, with MapReduce aid. There is aid as well for Apache Pig and Apache Hive.

Query language: CQL (Cassandra Query Language) was instituted, a SQL-like alternate to the customary RPC user interface. Language drivers are accessible aimed at Java (JDBC), Python (DBAPI2) and Node.JS (Helenus).

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