What is big data technologies & Hadoop | explain

What is big data technologies & Hadoop | explain

Big data technologies is huge volumes of data that can be of structured, semi structured and unstructured and they are generated in multi terabytes through various digital channels like mobile, internet and social media etc and these are not able to be processed using traditional applications.

So now unlike traditional technologies like RDBMS, Big Data actually processes large volume of data at a faster pace and also provides you an opportunity to store the data with different tools, technologies and methodology.

Now Big Data solutions actually provide the techniques to capture, store and analyze even search the data in seconds that make it easy to find insights and relationships for innovation and competitive game. So with suitable analytics, big data can be used to determine the causes of business failure, cost reduction, time-saving, better decision making and new product creation.

So individual with knowledge of Big Data are referred to as Big Data Specialist and hence Big Data Specialist will have expertise in let'ssay Hadoop, Mapreduce, Spark, NO SQL and DB tools like HBase, Cassandra and MongoDBetc

We all use smartphones, but have you ever thought about how much data it generates as text phone call email, email video video search.

Every month about 40 exabytes of data is generated by a single smartphone user, now imagine multiplying this number by 5 billion smartphones. For users, which is a lot of process for our brain, in fact this amount of data is not enough to handle traditional computing systems and this huge amount of data is what we call big data.

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2 million 1 minute snapshots generated per minute on the internet are shared on Snapchat, 3.8 million search queries are done on Google, 1 million people log in to Facebook, 4 million 5 million videos are viewed on YouTube, 188 million Emails are sent, which is a lot of data, how can you classify any data as big data.

This is possible with the concept of volume velocity variation of 5 V and value let us understand it with an example from the healthcare industry. Ls and clinics around the world generate massive amounts of 233 data, as patient records annually. The data is collected and the test results are generated at a very high speed. Which explains the velocity of big data diversity, which refers to different types.

Examples of data types such as structured semi-structured and unstructured data include Excel record log files and X-ray images are referred to as the accuracy and reliability of the generated data. Analysis of all this data will benefit the medical sector by enabling faster disease detection treatments and treatments The reduced cost is known as the value of Big Data.

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But how we store and process this big day to do this work. We have different frameworks like Cassandra Hadoop and Spark. Let us take Hadoop as an example and see how Hadoop stores and processes grow. Does.

Data Hadoop uses a distributed file system, known as a distributed file system by Hadoop, to store large data. If you have a large file, your file will break. Noto small chunks And stored in different machines not only that when you break the file, you also make copies of it which go to different nodes.

In this way you store your big data in a distributed manner and make sure that even if a machine fails your data. Another MapReduce technique is used to process secure big data. A long task is broken into a smaller task, BC and D now take three machines each instead of one machine and complete it in a parallel fashion. And finally collect the results. This processing becomes easy and fast. This is known as parallel processing.

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Now that we have stored and processed our big data we can analyze this data for many applications in games like Halo 3 and Call of Duty designers. Users can analyze the data, to understand at what level most users resume or omit this insight can help rework the game's story and improve the user experience The data, which in turn reduced customer churn rates, also helped disaster management during Hurricane Sandy in 2012, and was used to gain a better understanding of hurricane impacts on the US east coast. Were made, that it could predict the storm five days before the landing, which was not possible before.

These are some clear indications of how big data can be, once properly processed and analyzed,  So here is a question for you that which of the following statements is not true about Hadoop distributed file system HDFS is a storage layer of hdfs.

Hadoop B data is stored in a distributed way in HDFS C, performs parallel processing of data D Small portions of data are stored on multiple data nodes in HDFS, give it an idea and in the comments section below three lucky winners Leave your answer, Amazon will be received.