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Is map reduce NoSQL?
MapReduce is a way of processing data where NoSQL is a way of structuring data. Even HBase, a popular NoSQL database in the Hadoop ecosystem, only requires HDFS. It doesn’t need to process data using MapReduce. A MapReduce job could use a NoSQL store as either a source or sink for data to process.
Which is more scalable SQL or NoSQL?
Scalability. Most SQL databases are vertically scalable, which means that you can increase the load on a single server by increasing components like RAM, SSD, or CPU. In contrast, NoSQL databases are horizontally scalable, which means that they can handle increased traffic simply by adding more servers to the database.
What is MapReduce in NoSQL?
In MongoDB, map-reduce is a data processing programming model that helps to perform operations on large data sets and produce aggregated results. The map function is used to group all the data based on the key-value and the reduce function is used to perform operations on the mapped data.
What is the relationship between map-reduce and NoSQL?
NoSQL = [Not only SQL] database are the types of databases that can have structural, semi-structural (XML, json) or non-structural data (textual data). Yes it can help for processing large data sets. Where as Map-reduce is an algorithm.
Why are NoSQL databases more scalable than SQL?
They are not inherently less efficient than NoSQL databases because the (possible) performance bottlenecks are introduced by things NoSQL (sometimes) lacks (like joins and where restrictions) which you can opt not to use. Clustered SQL RDBMS’s can scale reads by introducing additional nodes in the cluster.
How is data stored in a NoSQL database?
A NoSQL database features a dynamic schema for unstructured data and the data can be stored in many different ways, whether it be graph-based, document-oriented, column-oriented, or organized as a KeyValue store. This extreme flexibility allows you to: Create documents without first having to carefully plan and define their structure
Why are NoSQL databases good for non relational workloads?
If you don’t need the horizontal scalability found in NoSQL data stores, these databases are also good for many non-relational workloads. This makes them useful for working with relational and unstructured data without the complexity of different data stores.