NoSQL: A Relational Database For Distributed Storage

NoSQL database is not a relational database, Its designed for distributed storage for huge data needs (like facebook,linkedin) , they don't have any fixed schema and no joins.

Here they grow horizontally in cluster whereas RDBMS depends on computation power of hardware. Here it should be noted that it does not mean 'No SQL at all' rather 'Not Only SQL'.

Means SQL and No SQL will exist together to serve the business requirements.

Its not that that relational databases lack anything , they just have their limitations:

- They allow joins and indexes
- Fixed Schema
- They allow update of records

Four categories of No SQL Databases :

Key-Value Store : Its the simplest and easiest to implement  No SQL Database , everything has key
here from hash table .

Examples: Redis , Voldemort
Applications : Logging    
Data Model : Collection of Key-Value Pair.
Weakness : Data has no schema.
Strength : Its used for Fast lookups.

Column Family Store : They were created to  process and store very huge amount of data distributed   over many machines/nodes. Here keys point to multiple columns. Columns are grouped by column family.

Examples : Cassandra , Hbase
Applications : Distributed storage
Data Model : Columns to Column Families
Strength : Good distributed storage of data along with fast lookup
Weakness : Very low level API

Document Databases : It pairs each key value with data structure known as Documents. Documents can contain many key value pairs or key-array pairs .

Examples : MongoDB
Applications : Web Applications
Data Model : Grouping of Key-Value Collections.
Strength : Incomplete data can also be handled.
Weakness : No standard query syntax for Documents.

Graph Databases : Its  used to store and analyse information about networks such as travel portal , social connections.

Examples : Neo4J , Infinite Graph
Applications : Social Media , Travel Portal
Data Model : 'Graph'- Nodes
Strengths : Shortest Path , Degree Relationship
Weakness : Not  easy for cluster to handle as it needs to traverse whole graph for an answer.

Generally we should implement NoSQL where data model is dynamic and horizantal scalability is more important .

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How IoT can Support Better Decision Making

Internet of Things (IoT) has been the colossal presence nowadays – organizations putting resources into sensors, systems administration and applications with the desire that this speculation will build incomes, lower expenses and enhance benefit over the short and long haul, with the help of an advanced excel course in Gurgaon.

Even as the term of IoT was thought up by Kevin Ashton in 1999, at Procter & Gamble, the standard use of IoT is simply beginning. Why have I turned out to be so enchanted with the capacity of the IoT to convey business esteem? It’s truly very straightforward – the IoT conveys esteem through the information that the things accommodate for choice making.

This information is gathered by different gadgets and sensors. What’s more, it is currently accessible on a streaming or constant premise. This gives the capacity to gain from patterns in that information rapidly and follow up on those patterns inside of seconds.

Gone are the days when information is gathered over the compass of weeks or months. Along these lines, with a specific end goal to exploit the gushing Big Data originating from the sensors, we must reevaluate how we utilize analytics. Keep in mind that I didn’t pronounce that the analytics themselves should alter. By and large, we can utilize the similar analytics connected to spilling data as we utilized as a part of a bunch model.

What we require is a decent comprehension of where the analytics should be applied – at rest, in the center or on the rim. Let’s clarify:

• Analytics at rest indicates information hauled out of the stream and utilized for elite expository model advancement 
• Analytics in the center happens to the information as it is spilling. It is important on the grounds that it can be a blend of spilling information itself enhanced by sitting information, so that we can identify more intricate occasions faster. 
• Analytics on the rim implies analysis at the particular sensor or gadget.

Now we have touched base of a better place in the analytic gamut. The ideal analytics skill is a wide examination experience. It incorporates constant inquiries on information in movement and at the border, with increasing advanced outcome. This innovative development stirs analytics from brought-together information distribution centers to edging analytics – which are closer to the events’ occurrence.

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