Hadoop NoSQL Certification Training -BigDataTraining.IN

What is NoSQL

NoSQL databases and data-processing frameworks are primarily utilized because of their speed, scalability and flexibility. 

Features of NoSQL databases

 One major difference between traditional relational databases and NoSQL is that the latter do not generally provide guarantees for atomicity, consistency, isolation and durability (commonly known as ACID property), although some support is beginning to emerge. Instead of ACID, NoSql databases more or less follow something called "BASE". 

The other major difference is, NoSQL databases are generally schema-less - that is records in these databases do not require to conform to a pre-defined storage schema.

In a relational database, schema is the structure of a database system described in a formal language supported by the DBMS and refers how the database will be constructed and divided into database objects such as tables, fields, relationships, views, indexes, packages, procedures, functions, queues, triggers and other elements.

In NoSQL databases, schema-free collections are utilized instead so that different types and document structures such as {“color”, “blue”} and {“price”, “23.5”} can be stored within a single collection.

Schema-less

"Tables" don't have a pre-defined schema. Records have a variable number of fields that can vary from record to record. Record contents and semantics are enforced by applications.

Shared nothing architecture

  Instead of using a common storage pool (e.g., SAN), each server uses only its own local storage. This allows storage to be accessed at local disk speeds instead of network speeds, and it allows capacity to be increased by adding more nodes. Cost is also reduced since commodity hardware can be used.

Elasticity

Both storage and server capacity can be added on-the-fly by merely adding more servers. No downtime is required. When a new node is added, the database begins giving it something to do and requests to fulfill.

Sharding

Instead of viewing the storage as a monolithic space, records are partitioned into shards. Usually, a shard is small enough to be managed by a single server, though shards are usually replicated. Sharding can be automatic (e.g., an existing shard splits when it gets too big), or applications can assist in data sharding by assigning each record a partition ID.

Asynchronous replication

Compared to RAID storage (mirroring and/or striping) or synchronous replication, NoSQL databases employ asynchronous replication. This allows writes to complete more quickly since they don't depend on extra network traffic. One side effect of this strategy is that data is not immediately replicated and could be lost in certain windows. Also, locking is usually not available to protect all copies of a specific unit of data.

BASE instead of ACID

NoSQL databases emphasize performance and availability. This requires prioritizing the components of the CAP theorem (described elsewhere) that tends to make true ACID transactions implausible.

Types of NoSQLdatabases

NoSQL databases are often categorized according to the way they store data.

• Key-value stores
• Columnar (or column-oriented) databases
• Graph databases
• Document databases

Big Data to create a new boom in job market
 

The 'Big Data' industry - the ability to access , analyze and use humungous volumes of data through specific technology - will require a whole new army of data workers globally. India itself will require a minimum of 1,00,000 data scientists in the next couple of years, in addition to scores of data managers and data analysts , to support the fast emerging Big Data space.

The exponentially decreasing costs of data storage combined with the soaring volume of data being captured presents challenges and opportunities to those who work in the new frontiers of data science. Businesses, government agencies, and scientists leveraging data-based decisions are more successful than those relying on decades of trial-and-error. But taming and harnessing big data can be a herculean undertaking. The data must be collected, processed and distilled, analyzed, and presented in a manner humans can understand. Because there are no degrees in data science, data scientists must grow into their roles. If you are looking for resources to help you better understand big data and analytics, We have the knowledge and experience needed to help make your systems contribute to the success of your business. Form a tandem with us and take advantage of our capacity to manage, process and analyze big data effectively, quickly and economically.

BigDataTraining.IN has a strong focus and established thought leadership in the area of Big Data and Analytics. We use a global delivery model to help you to evaluate and implement solutions tailored to your specific technical and business context.

http://www.bigdatatraining.in/hadoop-training-chennai/

http://www.hadooptrainingchennai.in/hadoop-training-in-chennai/

http://www.bigdatatraining.in/machine-learning-training/

http://www.hadooptrainingchennai.in/bigdata-hadoop-projects/

http://www.bigdatatraining.in/hadoop-training-chennai/

http://www.bigdatatraining.in/launching-apache-cassandra-training/  

 http://www.bigdatatraining.in/contact/

Contact us:

email : info@bigdatatraining.in

Phone: +91 9789968765, 044-42645495

#67,2nd Floor, 1st Main Road, Gandhi Nagar, Adyar, Chennai- 600020