NoSQL databases offer a less structured way to manage large amounts of data. One example is a key–value database. Key data (say a string) is stored and is searchable, and the value is a reference to where the rest of the data can be found. There are no constraints on how that data is structured, so these databases are very flexible. Other examples are document databases and big table databases (in which the rows can all have different structures.) While these types of databases have many advantages for storing large amounts of data, the downside is the ease of querying and finding connections between different pieces of data. Without a rigorous structure you cannot have the operations available in SQL such as joins. This kind of logic is generally handled in NoSQL databases with front–end programming. Graph databases can reflect associations between different pieces of data by maintaining links between them. Once again the actual data can be structured in any way and the prescribed links can provide fast access between items of data. Ad hoc queries are difficult.
SQL databases provide efficient storage and querying for well–structured data. NoSQL databases offer flexible ways of managing large amounts of data of many different types.
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Big Data Training for 5-days at Bangalore on 27th July' 2013 - 31st July 2013 - Course Completion Certificate + BigData Associate Certificate - unique verification code
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What is Big Data & Why Hadoop?
Hadoop Overview & it’s Ecosystem
HDFS – Hadoop Distributed File System
Map Reduce Anatomy
Developing Map Reduce Programs
Advanced Map Reduce Concepts
Advanced Map Reduce Algorithms
Advanced Tips & Techniques
Monitoring & Management of Hadoop
Using Hive & Pig ( Advanced )
Deploying Hadoop on Cloud
Extensively a practical oriented training
Key Features :
* Practice on production level Cloud Servers
* Primary focus - hands-on sessions
* Real time Task Assignment - POC / our Big Data Analytics Platform
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