Cognitive Computing Trends

What exactly do you think as a human’s greatest possible skill? Solving problems is the greatest gift of God to the mankind because that brings us where we are today. Just a few decades ago when the revolutionary computers were introduced into the world, no one would have anticipated the machine to be solving our problems for us, only much better and with greater patience level.

Just as we’d have expected, we are advancing into the future faster than we invent all that we see today. The same is the case with C3PO which will be less to be seen in the future; that blessed us with Star Wars and Hal. The future, as we see it now, will be more about Cognitive Technologies, which comes with a greater ability to improve business management and the human-machine interaction.

How cognitive computing adds business value?

It is still a while before we put our best foot forward into the world of cognitive technologies, however, the value derived from cognitive computing cannot be disputed. Let us read the most probable changes it should bring to the business.

1. Increased productivity and better performance: With an impressive 360-degree view of financial and economic data for all the perspectives of a business, a better decision-making and forecasting is promised. Everything will be automated, including manufacturing, health diagnosis, failure detections in machines and lot more. Hence, the organizations embracing this change will definitely be more operational than before.
2. Improved customer experience: Just imagine the future of customer service with cognitive technology; a smart and customized service adapted by the preference of a customer would be a total win-win situation. An improved customer service means a leap in the number of customers, thus, a better and increased business. The cognitive technology also promises you to offer your customer a better buying experience. This will most probably be done by an analysis performed on the buyer’s personality by connecting with their social media profile.
3. Empowering employees with cognitive computing: If an organization has highly skilled professionals, then cognitive technology can help the organization to get best out of those skilled employees. As a skilled person can learn and grow simultaneously, the implementation of cognitive ecology will certainly help in achieving the task.
4. Consistent Analysis for the positive aspect of Business: With the implementation of cognitive technologies, the respective leaders can unfold a majority of their strategies to increase their business. With an effective level of accurate predictions, less number of untapped markets, and increase in product predictions, the business administration will work affluently than ever before.

The fascination for a better and an advanced future has greatly influenced lives around us. It is difficult to tell whether we are faster or still slower than what the previous generation might have imagined the life for the future. However, one thing is quite certain from the recent advancements – we are surely, entering another revolutionary era in human history. And cognitive computing is emerging as one of the biggest drivers of this change.

The Language Battle

Java and Python are two of most popular and powerful programming languages of present time. Beginner programmers are often confused about choosing the right one. Since we are a premier Java developing firm, our opinion is slightly leaned towards Java.

Although, hey! We love python too.

Java VS Python: Key Differences

• Braces vs Indentation
  • Python uses indentation to separate code into blocks. Java, like most other languages, uses curly braces to define the beginning and end of each function and class definition.
• Dynamic vs Static Typing
  • Java forces you to define the type of a variable when you first declare it and will not allow you to change the type later in the program. While Python uses dynamic typing, which allows you to change the type of a variable.
• Portability
  • Any computer or mobile device that is able to run the Java virtual machine can run a Java application, whereas to run Python programs you need a compiler that can turn Python code into code that your particular operating system can understand.
• Ease of use
  • Python is an easier language for novice programmers to learn. You will progress faster if you are learning Python as a first language than Java. However, the popularity of Java means that learning this powerful language is essential if you want your apps run everywhere.

Why People Choose Java

1. The strong java community

No matter how good a language is, it wouldn’t survive if there is no community to support. Java has a strong community who is ready to help throughout your career. I think its the reason why stackoverflow has the largest number of answers on java.

2. Java is free

If a programmer wants to learn a new language or an organization wants to use a technology, cost matters. This is why java achieved much popularity.

3. Huge collection of OpenSource libraries

Java is backed with a number of open source libraries that helps the developers to reduce their development time as well the lines of code. Some of these libraries are

• Apache Commons
• Google Guava
• JUnit
• SLF4J and Log4j

4. Powerful development tools

One can choose from several of the Development tools (IDE) that are available for java.

• Eclipse
• Netbeans
• IntelliJ IDEA
• jEdit
• JDeveloper

5. Java is platform independent

The main reason of Java’s popularity in the 1990s was the idea of platform independence.  Its tagline “write once run anywhere” attracted many developments into java. Most of the Java applications are developed in Windows environment and run in UNIX platform.

6. Java is Object Oriented and even supports functional programming with Java 8

Developing OOPS application is much easier, and it also helps to keep system modular, flexible and extensible.

The Python Advantage 

1. Python requires no “set up.” A full python environment is already on every Linux machine, and on Macs. On Linux, the program yum, or the Yellowdog Updater, Modified is written in python, so python is here to stay. Java requires a substantial amount of setup. So if you want to get started with python programming, just type python at the prompt. To start with Java, call someone who knows it.

2. The systems written in Java that we have purchased all suffer from the need to have particular versions of Java installed, and thick clients of these systems also have that requirement. Support of Java appears to be expensive. We do not yet have a similar number of python systems, but no one is expecting configuration management to be an issue with them. From an educational standpoint, this sounds like a good way to become frustrated.

3. Python has its own idiosyncrasies. In Java, every object must be a representation of some class, but in python the “variables” are of a unique flavor. Variables do not represent objects [cf. object: something in memory that has an address] nor are they pointers, nor are they references. It is best to think of them as temporary “names” for an underlying reality, much like the Allegory of the Cave in The Republic (Plato). From a learning standpoint, this may be more difficult for those of us with 35 years of experience than it is for those first taking up programming.

4. A number of companies are stuck with a great deal of legacy code written in Python 2. Consequently, Python suffers from a misconception about how strict or loose the typing system may be, and how strictly it may be enforced. Keep in mind that because Python mainly works with “names” of objects, we are really not discussing the same thing when we discuss types of Python’s objects that we are discussing in other languages. Python does offer some rather seamless type conversions that can make it seem that the concept of types is less strict than it is in fact. Learning Python 3 first makes sense, but most of the employment is still in Python

5. Compared with Java, python is terse. Personally, the growing amount of arthritis in our hands welcomes this feature. In truth, my C++ code was frequently criticized for its overuse of operator overloading and the ternary operator. This may not make it a good learning experience, because for many people learning comes more easily when the material is spelt out.

Our Advice

If you have to absolutely choose only one of the two and are not from computer science stream, definitely Python and if you are from computer science stream, Java. If no restriction, choose both.

Couchbase Vs MongoDb

Both Couchbase and MongoDB are document oriented databases which carry a document for their storage unit. It is not unknown fact that data rules pretty much in all that we do today. Everything is witnessed and stored in documents, both online and offline, soft copy and hard copy. And now we need some tools those are essential for the management of the data. This is where NoSQL database comes in to act as a backbone for taming the data.

With an increase in the use of technology, the demands for more facility increases, compelling every business to meet customer satisfaction. So, for the businesses dealing with data storage and its management, CouchBase and MongoDB are two of highly sought-after database service providers.

A comparison between the two is much needed for a better insight on which one to use for the greater benefit as a whole.

1. Fragmentation

Couchbase: Couchbase server breaks the data to count it horizontally. It first spreads out the hash space to all the server in the community formed by the clustering of data. The placing of hash is later used to count it, which is determined by the key available in each document.

MongoDB: MongoDB uses the fragmenting method as it is entirely a document-based data model. Its key will lead you to the exact location of the document in the web of documents.  The difference here is that, MongoDB gives you the free will of choice for placing the fragment key; whereas, the Couchbase does the entire fragmenting itself without your intervention/choice.

Winner: – MongoDB

2. Performance

Couchbase: Very impressive to note is the simple architecture of Couchbase, which efficiently performs all the write operations when applied to a large number of data centers without any breakdown, whatsoever. This directly shows its fluent performance and minimized or rare cases for inactivity.

MongoDB: Due to its architecture, sadly, MongoDB fails to meet your business expectation of assessing the workload when a large number of data centers are applied.

Winner: Couchbase

3. External Cache

Couchbase: Couchbase is completely incorporated and organized within itself. It raises no action for the use of an external cache, at all. Its application is really easy to execute and has no complexities with multi-parts. A user can never lose his path somewhere between learning the adjustments!

MongoDB: MongoDB which cannot serve concurrent customers in its list of services, does need to add an external cache for assistance to serve the users. This not only costs more money but even raises complications while working. MongoDB needs a lot of physical attention of its user, as well as a number of needful added arrangements.

Winner: Couchbase

From the above given comparison, it is evident to see that Couchbase narrowly edges MongoDB and comes out a winner of this comparison. It is hard not to appreciate the user-friendly built and easy to execute usage of it. Couchbase, for its simple format and subtle structure, is easy to deploy because tasks like installation, configuration, and records management pretty simplified.

Fraud Detection Trends in 2017

Before we welcome the hysterical upheavals of 2018, let us look back at some of the trending online scandals that have created a turmoil in the online market. Unsurprisingly, 2017 was yet another year filled with eerie and troublesome e-commerce and online frauds.  Thus, before you go ahead to start your own retail online business, read on to prepare for self-prevention against potential frauds coming your way.

This post will discuss the frauds and then their detection trend one at a time.

Fraud 1: Cross-border frauds

Now, the major growth is happening overseas, especially in Asia, which is also a hub for most of the online frauds. Retailers all around the globe are looking ahead to capitalize their boundaries towards Asia for its growing market. Yet, they will have to take care of rise in cross-border frauds, which again is a Red Cross sign for keeping check.

Trend 1: Remove fraud filters

According to a fraud survey, most of the internationally available merchants rely on Address Verification System (AVS) for order validation, which proved more or less redundant for validation anymore. Therefore, retailers are not much relying on AVS. About 70% of the merchants have set their own rules for international orders, such as, limited payment methods and banned IP addresses from several countries.

Fraud 2: Clean fraud

A criminal mind usually moves two-steps ahead. Privileges of the smarter technology, fraudsters now have more refined methodologies allowing the most sophisticated fraud, popularly termed as, Clean Fraud. In this phenomenon, it is not possible to know until before a chargeback is incurred.

Trend 2: Crosschecking the shopper’s transactions

This means a regular and consistent crosschecking of previous transactions to the ongoing ones. Look for repeated orders; it will help you to identify die-hard shoppers and distinguish the swindlers.

Fraud 3: Fraudsters as a disguise of mobile shoppers

With growing technology, shoppers are moving from desktops to mobile phones. In Japan, 50% sale is marked by mCommerce that included fraudsters as well. Therefore, to avoid this, retailers have to be very vigilant.

Trend 3: Tracking frauds by approval rates

Merchants are worried about frauds via mobile shopping and need something very secure to avoid it. The safest way is to track the CNP frauds, keep a check on order approval with a particular shopper. The social apps, GPS location, basic details will definitely help in identifying the fraudster.

Fraud 4: Omni-Channel exodus

Omni channel is spread all across the web, attached to every prestigious brand and pundit thinking. As many would say, it means ‘cross channel being done just well’. This means creating a website and mobile application to give the customer in-store and a better customer experience. However, sometimes a fraudster takes the place of genuine buyer and intervene in customer’s satisfaction.

Trend 4: Synchronize data to personalize the shopping experience

The entire concept of identifying a fraudster from a genuine customer is – to first understanding your customer. An Omni-channel provides you with information, which is required to have a good customer-retailer relation. It is well suited to keep a track of a customer’s entire history, which can leverage the geographic locations, demographic trends, etc., which are necessary to know that your customer is not a fraudster.

While one cannot stop digital frauds completely in e-commerce, methods like these can help in preventing them for sure.

The Amazing Cassandra

The Apache Cassandra- an open source distributed database has been a much-anticipated topic of debate, lately. It is a prime choice for the significant amount of App development and data management companies, working at fresh new start-ups and traditional legendary enterprises. It has become remarkably easy to perform the complete transition of a traditional database to an open source database since NoSQL took a leap by introducing the Apache Cassandra.

With a unique yet considerably efficient ability to offer a real-life performance and experience, Cassandra has been making the life of companies in web developments, software engineering, and Data analysis easy. No wonder why back in 2008 Prashant Malik and Avinash Lakshman initially originated the Cassandra at Facebook!

How Cassandra benefits businesses?

With an increasing number of businesses opting for the Apache Cassandra, let us have a brief at it advantages for a growing business.

1 – Elastic Scalability

Many say that elastic scalability is the biggest advantage of using the Apache Cassandra. As we have read above, as number of servers can be added, therefore, the now formed Cassandra cluster can be scaled up and down as you please without much hassle, i.e. without any downtime or pause occurring to the applications.

2 – Open Source

We have been hearing a lot about Apache Cassandra offering an Open Source service. What exactly does it mean? Being open source means- it is available to businesses for FREE! Yes, you read that right; you can download it without giving much thought to your pocket. It is not the end of the awesomeness that open sourcing offers, it has a huge Cassandra community where niche-specific people can come together and discuss various aspects of this huge open source projects. It is even compatible to be used with other Apache projects.

3 – Peer to Peer Architecture

Unlike a master-slave form of working, the Apache Cassandra follows a peer-to-peer architecture for execution, thus, resulting in rare chances for failure. This makes it possible to add as many servers as your business wants in data centers to make a Cassandra cluster. It means that all the servers are at the same level and any machine can answer the request from a client.

4 – Fault Tolerance

Usually, what worries any business the most is whether or not the stored data is safe? Well, Apache Cassandra not only secures the data but stores it at multiple locations. Even if one server fails, or someone hacks it, the user is able to retrieve the data with utmost ease from another location. It is up to your choice how many replications you want to create which is then activated by the high-level backup and recovery competencies of Cassandra.

5 – Great Analytics possibilities

There are 4 key methods of carrying out analytics on Cassandra

1. Solr based integrated search
2. Batch analytics integrating Hadoop with Cassandra
3. External batch analytics powered by Hadoop and Cloudera/Hortonworks
4. Spark based near real time analytics

This significantly expands the range and usage of analytics using Cassandra.

So, it is safe to say that Apache Cassandra is a total win-win situation for any organization using the solution. Be it high performance, predictable scaling, distributed database characteristic, or 100% uptime, Cassandra scores heavily on these parameters and emerges as the preferred open-source distributed NoSQL database management system.

Red Is Incredible

Redis is an in-memory key value datastore written in ANSI C programming language by Salvatore Sanfilippo.  Redis not only supports string datatype but it also supports list,  set, sorted sets, hashes datatypes, and provides a rich set of operations to work with these types. If you have worked with Memcached, an in-memory object caching system, you will find that it is very similar, but Redis is Memcached++.  Redis not only supports rich datatypes, it also supports data replication and can save data on disk.  The key advantages of Redis are :

1. Exceptionally Fast : Redis is very fast and can perform about 110000 SETs per second, about 81000 GETs per second. You can use the redis-benchmark utility for doing the same on your machine.
2. Supports Rich data types : Redis natively supports most of the datatypes that most developers already know like list, set, sorted set, hashes. This makes it very easy to solve a variety of problems because we know which problem can be handled better by which data type.
3. Operations are atomic : All the Redis operations are atomic, which ensures that if two clients concurrently access Redis server will get the updated value.
4. MultiUtility Tool : Redis is a multi utility tool and can be used in a number of usecases like caching, messaging-queues (Redis natively supports Publish/ Subscribe ), any short lived data in your application like web application sessions, web page hit counts, etc.  There are a lot of people using Redis and they can be found at the owner website.

Here are a few things we suggest thinking about when you are utilising the superpowers of Redis.

• Choose consistent ways to name and prefix your keys.  Manage your namespace.
• Create a “registry” of key prefixes which maps each to your internal documents for those application which “own” them.
• For every class of data you put into your Redis infrastructure: design, implement and test the mechanisms for garbage collection and/or data migration to archival storage.
• Design, implement and test a sharding (consistent hashing) library before you’ve invested much into your application deployment and ensure that you keep a registry of “shards” replicated on each server.

Let us explain each of these points in brief.

You should assume, from the outset, that your Redis infrastructure will be a common resource used by a number of applications or separate modules.  You can have multiple databases on each server numbered 0 through 31 by default, though you can increase the number of these.  However, it’s best to assume that you’ll need to use key prefixes to avoid collisions among various different application/modules.

Consistent key prefixing & Managing your namespace:

Your applications/modules should provide the flexibility to change these key prefixes dynamically.  Be sure that all keys are synthesized from the application/module prefix concatenated with the key that you’re manipulating; make hard-coding of key strings verboten.

Registry: Document and Track your namespace

We suggest that you have certain key patterns (prefixes or glob patterns) as “reserved” on your Redis servers.  For example you can have __key_registry__ (similar to the Python reserved method/attribute names) as a hash of key prefixes to URLs into your wiki or Trac or whatever internal documentation site you use.  Thus you can perform housekeeping on your database contents and track down who/what is responsible for every key you find in any database.  Institute a policy that any key which doesn’t match any pattern in your registry can/will be summarily removed by your automated housekeeping.

Garbage Collection: 

In a persistent, shared, key/value store, and in the case of Redis, in particular the collection of garbage is probably the single major maintenance issue. 

So you need to consider how you’re going to select the data that needs to be migrated out of Redis perhaps into your SQL/RDBMS or into some other form of archival storage, and how you’re going to track and purge data which is out-of-date or useless. 

The obvious approaches involve the use of the EXPIRE or EXPIREAT features/commands.  This allows Redis to manage the garbage collection for you, either relative to your manipulation of any given key, or in terms of an absolute time specification.  The only trick about Redis expiration is that you must reset it every single time.

Sharding: 

Redis doesn’t provide sharding.  You should probably assume that you’ll grow beyond the capacity of a single Redis server. Slaves are for redundancy, not for scaling, though you can offload some read-only operations to slaves if you have some way to manage the data consistency, for example the ZSET of key/timestamp values describe for expiry can also be used for some offline bulk processing operations; also the pub/sub features can be used for the master to provide hints regarding the quiescence of selected keys/data.

So you should consider writing your own abstraction layer to provide sharding.  Basically imagine that you have implemented a consistent hashing method and you run every synthesized key through that before you use it.  While you only have a single Redis server then the hash to server mapping always ends up pointing to your only server.  Later if you need to add more servers then you can adjust the mapping so that half or a third of your keys resolve to your other  servers.  Of course you’ll want to implement this so that the failure on a primary server causes your library/service module to automatically retry on the secondary and possibly any tertiary server.   Depending on your application you might even have the tertiary attempts fetch certain types of data from an another data source entirely.

The Complexity Of Facebook

Lets describe a single event at Facebook, “Like & Share.” This is very simple functionality, whenever you see a beautiful chick you like her pic and share.

Let’s go technical here.

1. Design a thumb for a “Like” and also design a share button.
2. Every post should have like and share button in the bottom.
3. Whenever someone likes or share, notification should go to the owner of the post.
4. Whenever someone likes, the post should boost up to the all friend circle who follows you.
5. There should be a counter for number of likes and share.
6. Whoever likes and share, save that information.
7. Privacy setting should also be there, if someone doesn’t want likes from unknown, hide it.
8. If someone clicks “Like”, they shouldn’t like it more than once. But if they want to share they can share multiple times.
9. If someone clicks “Like”, turn that button into “Unlike”.
10. There should also a like button on comments but there shouldn’t be any share button.

Now, this is just a high level design of simple functionality of “Like and Share”, which may not cover all the test cases which is presently live at Facebook.

If I will go for a coding side only for “Like and Share” Then this answer will become the longest answer of Quora.

Now Let’s see interesting facts:

1. There are approx 42 functionalities/features at Facebook like connecting with friends to Live Streaming. Not to forget image detection algorithm to tag a friend.
2. What you see in today’s Facebook is not created in one day but an evolution of 13 years of continuous coding.
3. “TheFacebook” which was created by Mark Zuckerburg is not running from a dorm anymore.
4. Today it has 18,770 Employees (As of March, 2017), just to manage single website.
5. When Facebook was created it has one server at Mark’s dorm and It had 30,000 Servers in 2008 in just 4 years from its inception. It is very difficult to maintain one server, and when it comes to maintain these many servers, buddy the cost is beyond your imagination.
6. It’s security is invincible that world’s renowned and well known Hacker Group “Anonymous” tried to hack it. They failed miserably. Same group who is responsible for many hacking and leaking an information.
7. Facebook stores approximately 300 PETABYTES of user data on its servers. There are 1 million gigabytes in a petabyte. The entire written works of humankind, in every known language (including Latin and other historical languages) from the dawn of recorded history, would occupy approximately 50 petabytes. Think about that for a minute. Can you handle the cost of these much storage devices?

The value of Facebook is NOT in the software. It is not in the design. The value of Facebook is in the billions of existing users. The value of Facebook is the brand that they have managed to build.

Sure, a programmer could create a website like Facebook. It might not scale initially, but as its user base grows, it could raise capital and hire engineers to make it scale. The patterns are established. It is not rocket science.

The same question applies to Google, don’t you think so ?

Well, it is not a matter of lack of human resource. A team can gather and develop it.

The real trick isn’t coding up another social media site.

The real trick is getting people to use it.

By their nature, such platforms they need a large user-base to work. and Facebook already dominates the social media space. Even Google was unable to unseat them. It’s not because google plus didn’t function.Its because they couldn’t steal away enough market share. The nature of their product makes it hard for someone else to come along and usurp them.

Mobile Banking Is On The Rise

Just a simple question to start. When did you last visit your bank? Most of you would be hard pressed remembering it. If today every activity is happening online then it is logical that banking can also be executed online. With the tremendous advancement in technology the mobile apps built for banking nowadays are very secure and up-to-date thus providing a hassle free banking experience.

Also the mobile banking apps have become very intuitive and easy to use compared to its earlier bulkier versions. A person who does not have any previous experience in operating a technical device can also easily transact using a mobile app. There are many other benefits of mobile apps which have drastically transformed the banking sector and made banking immensely easy. Some of them are enumerated below:

1) No waiting in queues

If you are one of those people whose first work of the day was to visit a bank, then you would whole heartedly appreciate how banking apps have changed your life. The days of large queues outside a bank are over. By just clicking on a few icons your banking work is done within seconds with the help of a mobile banking app.

2) Tremendous convenience to consumers

Most of the banks have their working hours between 9am and 5pm and that is also the time people go to offices for work. Previously if a person had some work in the bank then he would had to take the day off from work or had to leave early, but with the advent of mobile apps this scenario has completely vanished. With the help of his bank’s mobile app he can transact anywhere in the world and anytime of the day.

3) Immediate transfers

Normally in India it takes three days to clear a cheque issued from a different bank. This led to tremendous wastage of time and resources. Now with the help of a banking app, a businessman can transfer money immediately to his supplier or any other person in just few seconds.

How is technology driving this radical shift?

A well deployed banking solution needs to take care of universal UI/UX experience for all bank customers. Also a secure platform needs to be provided in order that customers get peace of mind by using banking apps on their mobile. Disruptive technologies like e-wallets, BHIM app, and UPI are further expanding the reach and coverage of banking

Additionally the servicing front too has received a fillip with technology offerings like cloud deployment, mobile apps, big data, AI, and IoT. These are largely driving the business strategy of private and public banks alike.

Banks which were thinking of opening thousands of branches and ATMs just two years back are now giving more importance to their banking websites and apps and are brainstorming to bring in new apps with innovative technologies which would rapidly alter the scenario further. The leap of technology has already created virtual banks and in the future a new physical bank would be a rarity.

Coucbase Vs MongoDb

Couchbase and MongoDB are both document situated databases. They both have a report as their stockpiling unit. That is basically where the similarities stop. 

Couchbase is a blend of couchdb + membase. It utilizes a strict HTTP convention to question and communicate with objects. Items (reports) are put away in basins. To question records in Couchbase, you characterize a view with the segments of the report you are keen on called the guide; and after that alternatively can characterize some total capacities over the information i.e the decrease step. 

In the event that you are putting away client information and need to inquiry all clients that have not purchased any products for as far back as three months; you would first need to compose a view (the guide) that channels these clients; once this view is distributed – couchbase will improve seeks on this and you can utilize this view (outline) your source on which you execute questions. 

You can make numerous perspectives over your reports and these perspectives are profoundly enhanced by the framework and are just reindexed when the basic record has noteworthy changes. MongoDB has a completely extraordinary way to deal with a similar issue. 

It has an idea of SQL-like inquiries, and databases and accumulations. In MongoDB, records live in an accumulation, and accumulations are a piece of a database. Much the same as Couchbase, you can store any subjectively settled report; and simply like Couchbase a programmed key is created for you.

Be that as it may, with MongoDB the way you recover archives is more similar to how you compose SQL inquiries; there are administrators for most boolean matches, and example coordinating and (with 3.0) full content hunt also. You can likewise characterize lists to help accelerate your outcomes.

An In Depth Analysis

Generally, MongoDB is less demanding to get acquainted with on the off chance that you are now alright with conventional SQL. MongoDB additionally gives the typical replication abilities and it is fit for ace replication (albeit such an arrangement isn’t empowered as a matter of course). It can most effortlessly supplant your customary social database needs; as it has similar ideas of keys/tables (“accumulations”) and question parameters – alongside the advantage of being without schematic heavy.

Couchbase and MongoDB both give business support to their databases – MongoDB’s business offering is called MongoDB Enterprise and Couchbase has Enterprise Edition (EE).

One distinction you’ll instantly discover amongst MongoDB and Couchbase is that MongoDB does not accompany a default organization reassure/GUI – in certainty a GUI and an entire facilitated administration benefit is offered as an installment choice.

You can introduce any number of outsider GUI to rapidly peruse your archives, yet having one as a matter of course would have been decent. Couchbase furnishes an astounding GUI with their free item.

Regarding simultaneousness, the Couch base server is both hopeful and in addition cynical locking while the MongoDB server is of both idealistic and skeptical bolting yet with a discretionary store machine called as the WiredTiger.

MongoDB’s work quality quickly embarrasses with developing number of clients. MongoDB can’t engage a ton of clients, the occasion the quantity of client builds, MongoDB begins performing contrarily. We have to include more devices for serving a great deal of clients through MongoDB which is truly exorbitant. Couchbase can bolster countless with the single hub without influencing its execution by any means.

The Couchbase server has the limit of putting away paired esteems till around 20 MB, yet the MongoDB server has a definitive limit of putting away gigantic records into an enormous number of archives. Despite the fact that the MongoDB server can store bigger pairs, yet one can keep on using Couchbase server with a different stockpiling administration for holding metadata on the doubles.

The Couchbase pieces the information and after that checks on a level plane by spreading hash space to every one of the hubs in the group of the information. The setting of the hash space to a specific hub is chosen by the key present in each report. For fracture of the information utilizing the MongoDB, a dividing strategy, and a key must be chosen since its information display is altogether archive base. This section key will reveal to you the correct area of the archive in the group.

The contrast between the two is that the MongoDB depends on us for picking the section key and the dividing technique, while the couchbase server does all the dividing independent from anyone else without human intercession.

In the end, it all depends on your requirements and the amount of flexibility your business can afford or needs. 

The Three Pillars Of Modern Technology

These three terms have been doing the tech rounds now for a long time, and most of us think that they are quite similar to each other. However, therein lies the basic difference between these emerging platforms.

Let us understand these platforms better to acknowledge the essential differences and their usage.

Apache Hadoop

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Big Data

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, techniques and frameworks.

Data Science

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.

At the core is data. Piles of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value.

Understanding Big Data

Big Data is a huge collection of data sets that can’t be store in a traditional system.

Big data is a complex sets of data. It’s size can be vary up to peta-bytes.

According to Gartner – Big data is huge-volume, fast-velocity, and different variety information assets that demand innovative platform for enhanced insights and decision making.

A Revolution, authors explain it as – Big Data is a way to solve all the unsolved problems related to data management and handling, an earlier industry was used to live with such problems. With Big data analytics, you can also unlock hidden patterns and know the 360-degree view of customers and better understand their needs.

Big data gets generated in multi terabyte quantities. It changes fast and comes in varieties of forms that are difficult to manage and process using RDBMS or other traditional technologies. Big Data solutions provide the tools, methodologies, and technologies that are used to capture, store, search & analyze the data in seconds to find relationships and insights for innovation and competitive gain that were previously unavailable.

80% of the data getting generated today is unstructured and cannot be handled by our traditional technologies. Earlier, an amount of data generated was not that high. We kept archiving the data as there was just need of historical analysis of data. But today data generation is in petabytes that it is not possible to archive the data again and again and retrieve it again when needed as Data scientists need to play with data now and then for predictive analysis unlike historical as used to be done with traditional.

Understanding Hadoop

Hadoop is an open source, Scalable, and Fault tolerant framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is not only a storage system but is a platform for large data storage as well as processing.

It provides an efficient framework for running jobs on multiple nodes of clusters. Cluster means a group of systems connected via LAN. Apache Hadoop provides parallel processing of data as it works on multiple machines simultaneously.

What is Data Science?

Data Science is a field that encompasses related to data cleansing, preparation, and analysis. Data science is an umbrella term in which many scientific methods apply. For example mathematics, statistics, and many other tools scientists apply to data sets. Scientist applies the tools to extract knowledge from data.

It is a tool to tackle Big Data. And then extract information from it. First Data scientist gathers data sets from multi disciplines and compiles it. After that, apply machine learning, predictive and sentiment analysis. Then sharpen it to a point where he can derive something. At last, he extracts the useful information from it.

Data scientist understands data from a business point of view.His work is to give the most accurate prediction. He takes charge of giving his predictions. The prediction of data scientist is very accurate. It prevents a businessman from future loss.

Although, these three tech platforms are related, but there is a major difference between them. Understanding them clearly can help us exploit and appreciate them better.