By Vikas Kukreti, Module Lead, 3 Pillar Global
3Pillar Global is a product lifecycle management and software product development company that helps drive top-line revenue growth for our clients.It offers a unique and flexible approach for clients through its innovative Virtual Development Centers that deliver transformative levels of productivity.
There is an immense fuss in the software development space about big data nowadays. The imperative question: Is it a technique and concept that is involved in capturing, storing, and manipulating large amounts of data; or is there more to big data than that?
Users speak superfluously about data preservation, which means storing historic data. Why do they need to do that? If readers thought, “For predictive analysis and data mining”, they are on the right track. To elaborate, one can look at the data’s relationship to data science, statistics, and programming, as well as its usage in marketing, scientific research and, above all, the ethical issues that lie behind its use.
What are some potential innovative applications of big data?
There are many answers, some of which are given below:
It can help spot problem areas in a network and add throughput to help prepare for future demand.
It is able to analyze traffic details for various devices.
Big data can give insights into the type of content customers prefer, which enables them to make more accurate suggestions as to what subscribers might prefer.
Ancestry.com is performing DNA processing with the help of big data to help clients make connections. With some saliva in a tube, it can sequence a client’s DNA and match the client with other people in its database, like distantly-removed cousins.
A medical institute in the US is using big data in research that includes more than one million DNA variants in an effort to understand why some strains develop resistance to antibiotics.
Las Vegas is using big data to aggregate data from various sources into a single real-time 3D model. The model includes both above- and below-ground utilities and is being used to visualize the location and performance of critical assets located under the city.
The six points above articulate the magnificence of big data.
It’s time to move onto some specific flavours that big data uses. For this, one needs to shed some light on the insights of MongoDB.
Most readers may be aware about MongoDB. Briefly, MongoDB is an open-source document database that provides high performance, high availability and automatic scaling.
High performance and high availability are two things every other database talks about it, but what is automatic scaling? MongoDB’s key ingredient is automatic scalability, also known as horizontal scalability, with two main features:
Sharding – which is automatic in nature by default – distributes data across the cluster of machines.
Replica sets – used for low-latency, high-throughput deployments.
Some details about the above two steps:
Sharding: This is a method for storing data across multiple machines. Larger data sets exceed the storage capacity of a single machine. Finally, working with set sizes larger than the system’s RAM stresses the I/O capacity of disk drives. To address these issues of scale, big data systems have a basic approach to handling large amounts of data – sharding.
Sharding in MongoDB: Sharding is a horizontal scaling that, by contrast, divides the data set and distributes the data over multiple servers, or shards. Each shard is an independent database and, collectively, the shards make up a single logical database. MongoDB supports sharding through the configuration of sharded clusters.
Replication: This is the process of synchronizing data across multiple servers. Replication provides redundancy and increases data availability. With multiple copies of data on different database servers, replication protects a database from the loss of a single server. If there are additional copies of the data, the user can dedicate one to disaster recovery, reporting, or backup.
Replication in MongoDB: A replica set is a group of MongoDB instances that host the same data set. One MongoDB, the primary, receives all write operations. All other instances – secondaries – apply operations from the primary so that they have the same data set.
To conclude, a MongoDB deployment hosts numerous databases. A database holds a set of collections. A collection holds a set of documents. A document is a set of key-value pairs. Documents have dynamic schema. Dynamic schema denotes that documents in the same collection do not need to have the same set of fields or structure, and common fields in a collection’s documents may hold different types of data.