Big data is the new CRAZE. Reports suggest that investments in big data have surpassed $57 billion in 2017, and are expected to rise by 10% for the next three years.
Customers are happy – those who have applied advanced capabilities to predictive analytics, machine learning, customer analytics, customer profiles, inventory management and tracking, and more – as big data implementation across many verticals has resulted in measurable positive results.
Designing Big Data architecture is no mean feat; rather it is a very challenging task, considering the variety, volume and velocity of data in today’s world. Coupled with the speed of technological innovations and drawing out competitive strategies, the job profile of a Big Data architect demands him to take the bull by the horns.
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By mobilizing the volume and wealth of information in an organization, Big Data leads to improved customer perceptiveness, competitive advantage and operational efficiency. In the current data-centric era, big data is the buzzword. Nevertheless, how many of you actually know what it entails?
In this blog, we have compiled few FAQs, which will instantly shed some light about the basics of Big Data and its implementation.
Substantially complex, big data involve hundreds and thousands of terabytes or exabytes of data (starts with 1 and has 18 zeros after it, or 1 million terabytes) per single data set. If explained in simple words, big data is a collection of data sets, which comes from a variety of sources, like customer data, Internet of Things and social media. If compiled and analyzed in the right manner, it helps in understanding the nature of the lifestyle and purchasing habits of people and customers better.
The answer to this question is a bit challenging. Depending on the infrastructure of the market, the threshold limit of big data is determined. In most of the cases, the lower boundary of big data is limited to 1 to 3 terabytes.
However, using big data technologies for small databases can prove to be effective. Netezza brings about 200 built-in computer programs, like Python and Revolution R, which gained immense appreciation for being applicable to small databases.
Intuition is consequential, as ever. Staring at the humongous amount of data compels us to start from somewhere. As there is so much data, intuition is important, like never before. If you ask me, big data hasn’t yet replaced intuition, in fact the latter somehow complements the former. Both of them share a continuum relationship, instead of binary.
Transactional data, social data and machine data- are the chief sources of big data. Top-notch retailers like Amazon and Dominos boasting of more 1 million customer transactions per day results in to the generation of petabytes of transactional big data. Social media data comprise of 230 million tweets on Twitter per day, more than 60 hours of video uploaded every minute on YouTube and 2.7 billion Likes and Comments on Facebook appearing every day. Lastly, machine data can be boiled down to various modes, including the information generated by industrial equipment, web logs tracking behavioural data and real-time data emanating from sensors.
Adopt interactive data visualization tools and take your business to new heights. These tools are rewarding, say thanks to Big Data! Big conglomerates, like Google, Netflix, Amazon, Apple, Facebook and Twitter embraced the tools to visualize data. And this goes beyond the basic usage of graphs, excel charts and pivot tables.
Well, yes, very much so. Big Data is leading the future and is going to stay HERE AND NOW. It is right on its way to fundamentally transform the ways in which companies function and regard their competitors, customers and overall business.
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Based on Java, Hadoop is a free open source framework for programming where dealings with huge amounts of processed data in a computing environment is said to be distributed. None other than the Apache Software Foundation is sponsoring it. If you are looking for information about Hadoop, you will like to get in-depth information about the framework and its associated functions. To get you up to the mark with the concepts, the eBooks listed below will prove to be of invaluable help.
If you are looking forward to get started with Hadoop, and maximize your knowledge about Hadoop clusters, this book is of right fit. The book is loaded with information on how t o effectively use the framework to scale apps of the tools provided by Hadoop. This ebook lets you get acquainted with the intricacies of Hadoop with instructions provided on a step-by-step basis and guides you from being a Hadoop newbie to efficiently run and tackle complex Hadoop apps across a large number of machine clusters.
If you are looking for a reference from which you may learn more about Apache Pig, which happens to be the engine powering executions of parallel flows of data on the Hadoop framework which also is open source, the Programming Pig is meant for you. Not only does it serve the interests of new users but also provides advanced users coverage on the most important functions like the “Pig Latin” scripting language, the “Grunt” shell and the functions defined by users for extending Pig even further. After reading this book, analyzing terabytes of data is a far less tedious task.
This book covers a gamut of topics such as that how to store data with Hbase and HDFS, processing the data with the help of MapReduce and data processing automation with Oozie. Not limiting to that the book further covers the security features of Hadoop, how it goes along with Amazon Web Services, the best related practices and how to automate in real time the Hadoop processes. It provides code examples in XML and Java and refers to them in-depth along with what has been added to the Hadoop ecosystem of late. The eBook positions itself as comprehensive resource with API coverage and exposition of the deeper intricacies, which allow developers and architects to better customize and leverage them.
This guide allows the user to use Sqoop from Apache with emphasis on application of parameters that are enabled by the Command Line Interface when dealing with cases that are used commonly. The authors offer Oracle, MySQL as well as PostgreSQL examples of databases on GitHub that lend themselves to be easily adapted for Netezza, SQL Server, Teradata etc relational systems.
The preface of the book claims that the book enables readers to know how to process complex and large datasets. The book starts simple but still gives detailed knowledge about Hadoop. Further, the book claims to be a simple guide on getting things done in one place. It consists of 90 recipes that are offered simply and in a straightforward manner, coupled with systematic instructions and examples from the real world.
If you want to know how to maintain and build distributed systems that are both scalable and reliable within the framework of Hadoop then this book is for you. It is intended for – programmers who want to analyze datasets, irrespective of size; and – administrators, who seek to know the setting up and running of Hadoop Clusters, alike. New features like Sqoop, Hive as well as Avro are dealt with in the new second edition. Case studies are also included that may help you out with specific problems.
If one is to go by the book’s preface, the book is a blend of familiarity and uniqueness. The book is dedicated to design patterns by which we refer to the general guides or templates for solving problems. It is however more open-ended in nature than a “cookbook” as problems are not specified. You have to delve more in the subject matter than mere copying and pasting, but a pattern will get you covered about 90% of the whole way regardless of the challenge at hand.
This book is necessary for those who seek to maintain complex and large clusters of Hadoop. Map Reduce, HDFS, Hadoop Cluster Planning. Hadoop Installation as well as Configuration, Authorization and authentication, Identity, Maintenance of clusters and management of resources are all dealt in it.
Knowledge on programming in Hive provides an SQL dialect in order to query data, which is stored in HDFS, which makes it an indispensable tool at the hands of Hadoop experts. It also works to integrate with other file systems, which may be associated with Hadoop. Examples of such file systems may be MapR-FS and the S3 from Amazon as well as Cassandra and HBase.
The preface of this eBook illustrates its use. It lets developers get acquainted and become proficient at problem solving in the Hadoop space. The reader will also get acquainted with varied tools related to Hadoop and the best practices to be followed while implementing them. The tools included in this cookbook are inclusive of Pig, Hive, MapReduce, Giraph, Mahout, Accumulo, HDFS, Ganglia and Redis. This book intends to teach readers what they need to know to apply Hadoop knowledge to solve their own set of problems.
So, happy reading!
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Interested in a career in Data Analyst?
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To learn more about Big Data Course – Enrol Now.To learn more about Machine Learning Using Python and Spark – Enrol Now.
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