Big Data Technologies

In this tutorial we are going to discuss big data technologies

We know that in case of our big data technologies we required that data processing,��

data ysis and report generation. Also we should have the predictions and forecasting

And if you can do this one in an very efficient way, then obviously the uncertainties will get decreased

and certainty will increase and the risk of failure will be decreasing as well

So, that's why we required data ysis in our big data using multiple different technologies

Mainly we are having two different kinds of problems. One is the ytical big data, another one is the operational big data

So ytical big data and operational big data. So let us go for further discussion on this topic, that is big data technologies

In this session, we are discussing big data technologies. So at first we shall discuss what is big data technologies and we know that big data technologies

can be divided into two categories. One is the operational big data and another one is the ytical big data

So we'll be discussing this what are the features of operational big data and ytical

big data accordingly. So let us discuss what is a big data technology

We know that big data is nothing but comprising of this operational plus ytic which is consisting of no SQL plus Hadoop So what is big data technology So big data technology is an important in providing more accurate ysis which may lead to more concrete decision making resulting in greater operational efficiencies

Obviously we shall go for cost reductions and reduced risk for our business because whatever the predictions we are going to make

whatever the operational efficiencies we're expecting and cost reductions we are expecting in the decision making resulting for the for the better implementation everything can be obtained in our big data technologies so there are various technologies in the market for from different vendors including our amazon we are having IBM Microsoft etc to handle big data and basically there are two classes to handle big data so what are the two classes so as we have

explained as we have discussed that is the operational big data and ytical big data

so at first we're going for this operational big data which works on real-time

interactive databases no SQL will be there and we'll be having the huge

velocity that means huge data will be produced the rate of production of the

data will be to huge and which will be working online for the web mobile and

iot applications and millions of customers and consumers will be related with this operational big data

So this technology is actually the no SQL database like our MongoDB that provides operational compatibility for the real applications And NoSQL big data systems are designed to take the advantage of new cloud computing

architectures that have emerged over the past decade to allow massive computations to be run

inexpensively and also efficiently. So that is one of the main advantage of this operational

big data and some no skill systems can provide insights into the pattern and the trains based on the real time data with minimal coding and without the need for data scientists and for also additional infrastructure so you can find out the respective pattern or we can provide some insights of the pattern in our data without having any additional expenses without having any additional infrastructure and data scientists

now what is the ytical big data so ytical big data is actually the batch

oriented ytic databases we're going to have our huge volume of data to

be handled offline ytical applications will be there and hundreds of business ysts will be working on this and this about ytical big data so

these include systems like our massively parallel processing so remember this term very carefully that is a massively parallel processing in abbreviated form we usually call it as MPP massively parallel processing database systems and

MapReduce that provide ytical capabilities for retrospective and complex ysis that may touch most or all of our data and the map produce provides a new

method for yzing data that is complementary to the capabilities provided by SQL

was having some limitations and this MapReduce provides a new method for

yzing data and obviously that is complementary to the capabilities provided by

SQL and its system based on MapReduce can be scaled up from say single

servers to hundred thousands of high and low end missions and then the scalability

the scalability in MapReduce can be increased to manifold whenever the workload will be high and there is

one of the very main advantages of this ytical big data implemented throughout

our map produce which provides a new method for yzing data and that is obviously

the complementary of the capabilities of our of our SQL the capabilities which

is being provided by our SQL so in our discussion we have discussed what is a

big data technologies and in which sections that is the operational and ytical

big data in these two sections this big data technologies can be divided

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