Big data analytics uses tools like Hadoop and its components HDFS and MapReduce to store and analyze large datasets in a distributed environment. HDFS stores very large data sets reliably and streams them at high speeds, while MapReduce allows developers to write programs that process massive amounts of data in parallel across a distributed cluster. Other concepts discussed in the document include data preparation, visualization, hypothesis testing, and deductive vs inductive reasoning as they relate to big data analytics. The document aims to introduce readers to big data analytics using Hadoop and suggests the audience as data analysts, scientists, database managers, and consultants.

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Introduction to Big Data Analytics Hadoop

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What is Big Data?
Bigdataisapopulartermusedtodescribetheexponentialgrowthandavailabilityofdata,bothstructuredandunstructured.Andbigdatamaybeasimportanttobusiness–andsociety–astheInternethasbecome.

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What Is Hadoop?
Itisafree,Java-basedprogrammingframeworkthatsupportstheprocessingoflargedatasetsinadistributedcomputingenvironment.ItispartoftheApacheprojectsponsoredbytheApacheSoftwareFoundation.

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What is HDFS?
•The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications.
•HDFS is like the bucket of the Hadoop system: You dump in your data and it sits there all nice and cozy until you want to do something with it, whether that's running an analysis on it within Hadoop or capturing and exporting a set of data to another tool and performing the analysis there.

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Architecture Of HDFS

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About Map Reduce
MapReduceis a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers.
The framework is divided into two parts:
•Map, afunctionthat parcels out work to different nodes in the distributed cluster.
•Reduce, another function that collates the work and resolves the results into a single value.

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Pig Latin Statement
APigLatinstatementisacommandthatproducesaRelation.Arelationissimplyadatabagwithaname. Thatnameiscalledtherelation'salias.ThesimplestPigLatinstatementisLOAD,whichreadsarelationfromafileinthefilesystem.OtherPigLatinstatementsprocessoneormoreinputrelations,andproduceanewrelationasaresult.

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Data Preparation & Management
•Types of variables
•Identifying the business Y
•Basic Statistics
•Merging and Appending data –Primary key concept
•Missing values
•Outliers

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Data Visualization
•Data visualization is the presentation of data in a pictorial or graphical format. For centuries, people have depended on visual representations such as charts and maps to understand information more easily and quickly.
•Visualizations help people see things that were not obvious to them before. Even when data volumes are very large, patterns can be spotted quickly and easily.
•Visualizations convey information in a universal manner and make it simple to share ideas with others.

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Normal Distribution
•A normal distribution is an arrangement of a data set in which most values cluster in the middle of the range and the rest taper off symmetrically toward either extreme.
•Normal distribution curves are sometimes designed with ahistograminside the curve. The graphs are commonly used in mathematics, statistics and corporatedata analytics.

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Hypothesis Testing
Hypothesistestingreferstotheprocessofchoosingbetweencompetinghypothesesaboutaprobabilitydistribution,basedonobserveddatafromthedistribution.
Thetwomaintypesoftesting:-
•T Test
•Annova

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Deductive VsInductive Reasoning
•Deductive reasoninghappens when a researcher works from the more general information to the more specific. Sometimes this is called the “top-down” approach because the researcher starts at the top with a very broad spectrum of information and they work their way down to a specific conclusion.
•Inductive reasoningworks the opposite way, moving from specific observations to broader generalizations and theories. This is sometimes called a “bottom up” approach. The researcher begins with specific observations and measures, begins to then detect patterns and regularities, formulate some tentative hypotheses to explore, and finally ends up developing some general conclusions or theories.

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Become Big Data ExpertIn Just 2 days
BigData Analytics on Hadoop will teach you all you need to learn about BigData Analytics on Hadoop.
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Suggested Audience
•Data analysts / Data scientists who want to know how to use their expertise on Big Data
•Database Managers with a knowledge of Hadoop / Java who want to know what to do next in their career and how to manage and draw insights from their data
•Consultants who want to know what Big Data analytics is.
Syllabus

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