Cassandra:
Apache Cassandra is column oriented No SQL Data base for processing large amount of data that is spawned across multiple clusters and nodes. Cassandra process unstructured data and data is going to store in terms of key-values pairs. It has some unique features comparing to other data models.
Features
· High available service
· No single point of failure
· Linear scale performance
· Easy data distribution across multiple data centers
Some of differences in key features: RDBMS Vs Cassandra
Feature
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RDBMS
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Cassandra
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Type of Data
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Only deals with structured data
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Deals with Unstructured data
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Schema
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Fixed schema
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Flexible schema can be designed according to data
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Relationships
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Through joins and foreign keys between tables
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In this it will represent through collections
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Data Storage
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In terms of tables by rows and columns
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In terms of Nested key-value pairs
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Data model
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Database-> tables
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Keyspaces->column families
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Row Representation
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Row is nothing but individual record present in table
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Row represents replication
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Column Representation
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Series of relations
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It represents storage
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The top 5 most common use cases are:
1. Internet of Things
Cassandra is a perfect fit for scaling time-series data from users, devices, and sensors.
2. Personalization
Use Cassandra to ingest and analyze for custom, fast, low cost, scalable user experiences.
3. Messaging
Cassandra’s original Facebook use case; storing, managing, and analyzing messages requires sophisticated systems and massive scale.
4. Fraud detection
Staying a step ahead of fraud has become best solved at the database level. Apache Cassandra lets you analyze patterns quickly, accurately, and effectively.
5. Playlist
Product catalogs; movie ratings; you name it. Storing a collection of user selected items has massive performance and availability demands.
Next Article we are going to see how we work with Key spaces in Cassandra
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