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A UX portfolio should begin with your personal details highlighting your skills specific and in the content of UX Design. You can consider following points as a strategy and alternative ways to present your UX portfolioEmpathise with viewer: Know who will possibly view your portfolio. Their profile and expectations. Consider the amount of time they will spend on this section. Probably decision makers or head of department look into it and only look those which are relevant to their opportunity.Portfolio Index/Master Page: Portfolio master page should have an index to navigate through various projects or categories of projects you worked on. For e.g. categories can be based on business domain like Banking and finance, Education, Technology, Food & Entertainment etc. or It can be based on type of project like Website, Application etc.Case Study Details:Project Brief: 2–3 linesClient Background: 1–2 linesBusiness Challenge & Business Goal: 3–5 linesConclusion of your discussion with client : Mapping your conclusions to solve challenge or meet business goal through UX designExpertise & skills engagedProblem StatementSolution StatementsRevised IA and Navigation systemDesign StrategyStep by step process with real shotsConclusion of every step as a deliverable to clientImpact of outcome at an important stepBefore after screenshotsEarly and later testing results (If you have done UT)Success metrics after launch (if its available)Client TestimonialLink to similar projects (same domain, industry or category)Hope this helps to you.

I would say yes. Organisations have realized the benefits of Big Data analytics. It helped them in getting better business insights, which enhanced their decision-making capabilities. Hence, the demand of jobs in Big Data and Hadoop is also rising rapidly. If someone is interested in analyzing data and wants to pursue his or her career in this field, for them now is the right time to start learning Hadoop and Spark and Edureka is one of the finest place which will provide you Big Data & Hadoop certification training. Let me take you through some of the facts:Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 - ForbesMcKinsey predicts that by 2018 there will be a shortage of 1.5M data experts - Mckinsey ReportAvg salary of Big Data Hadoop Developers is $135k - Indeed Salary DataYou should go through this Big Data Career blog to understand the benefits of adopting career in Big Data. I would also recommend you to go through this Big Data career video.This Edureka Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get edureka Hadoop certification after the project completion.The Edureka hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to-Master the concepts of HDFS and MapReduce frameworkUnderstand Hadoop 2.x ArchitectureSetup Hadoop Cluster and write Complex MapReduce programsLearn data loading techniques using Sqoop and FlumePerform data analytics using Pig, Hive and YARNImplement HBase and MapReduce integrationImplement Advanced Usage and IndexingSchedule jobs using OozieImplement best practices for Hadoop developmentUnderstand Spark and its EcosystemLearn how to work in RDD in SparkWork on a real life Project on Big Data AnalyticsI would also like to tell you that projects & hands-on practice are very important while going through the course. Some of the projects that you will be going through are:Project #1: Analyze social bookmarking sites to find insightsProject #2: Customer Complaints AnalysisProject #3: Tourism Data AnalysisProject #4: Airline Data AnalysisProject #5: Analyze Loan DatasetProject #6: Analyze Movie RatingsProject #7: Analyze YouTube dataand many more.At Edureka you get:Instructor-led Sessions: 30hrs of Online Live Instructor-led Classes. Weekend class:10 sessions of 3 hours each and Weekday class:15 sessions of 2 hours each.Real-life Case Studies: Live project based on any of the selected use cases, involving Big Data Analytics.Assignments: Each class will be followed by practical assignments which can be completed before the next class.Lifetime Access: You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.24 x 7 Expert Support: We have 24x7 online support team available to help you with any technical queries you may have during the course.Certification: Towards the end of the course, you will be working on a project. Edureka certifies you as an Big Data and Hadoop Expert based on the project.Forum: We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.—————————————————————————————————————You should go through these Big Data & Hadoop recordings in advance, so you would be clear about how Edureka is delivering content in the class as well as you will also get an idea about Big Data and Hadoop initially.I would suggest you to first understand Big Data and challenges associated with Big Data. So , that you can understand how Hadoop emerged as a solution to those Big Data problems. This What is Hadoop and Hadoop Tuorial blog will introduce you to that.Then you should understand how Hadoop architecture works in respect of HDFS, YARN & MapReduce.Further moving on you should install Hadoop on your system so that you can start working with Hadoop. This will help you in understanding the practical aspects in detail.Further moving on take a deep dive into Hadoop Ecosystem and learn various tools inside Hadoop Ecosystem with their functionalities. So, that you will learn how to create a tailored solution according to your requirements.Let us understand in brief:What is Big Data?Big Data is a term used for a collection of data sets that are large and complex, which is difficult to store and process using available database management tools or traditional data processing applications. The challenge includes capturing, curating, storing, searching, sharing, transferring, analyzing and visualization of this data.It is characterized by 5 V’s.VOLUME: Volume refers to the ‘amount of data’, which is growing day by day at a very fast pace.VELOCITY: Velocity is defined as the pace at which different sources generate the data every day. This flow of data is massive and continuous.VARIETY: As there are many sources which are contributing to Big Data, the type of data they are generating is different. It can be structured, semi-structured or unstructured.VALUE: It is all well and good to have access to big data but unless we can turn it into value it is useless. Find insights in the data and make benefit out of it.VERACITY: Veracity refers to the data in doubt or uncertainty of data available due to data inconsistency and incompleteness.What is Hadoop & it’s architecture?The main components of HDFS are NameNode and DataNode.NameNodeIt is the master daemon that maintainsand manages the DataNodes (slave nodes). It records the metadata of all the files stored in the cluster, e.g. location of blocks stored, the size of the files, permissions, hierarchy, etc. It records each and every change that takes place to the file system metadata.For example, if a file is deleted in HDFS, the NameNode will immediately record this in the EditLog. It regularly receives a Heartbeat and a block report from all the DataNodes in the cluster to ensure that the DataNodes are live. It keeps a record of all the blocks in HDFS and in which nodes these blocks are stored.DataNodeThese are slave daemons which runs on each slave machine. The actual data is stored on DataNodes. They are responsible for serving read and write requests from the clients. They are also responsible for creating blocks, deleting blocks and replicating the same based on the decisions taken by the NameNode.For processing , we use YARN(Yet Another Resource Negotiator). The components of YARN are ResourceManager and NodeManager.ResourceManagerIt is a cluster level (one for each cluster) component and runs on the master machine. It manages resources and schedule applications running on top of YARN.NodeManagerIt is a node level component (one on each node) and runs on each slave machine. It is responsible for managing containers and monitoring resource utilization in each container. It also keeps track of node health and log management. It continuously communicates with ResourceManager to remain up-to-date .So, you can perform parallel processing on HDFS using MapReduce.MapReduceIt is the core component of processing in a Hadoop Ecosystem as it provides the logic of processing. In other words, MapReduce is a software framework which helps in writing applications that processes large data sets using distributed and parallel algorithms inside Hadoop environment. In a MapReduce program, Map() and Reduce() are two functions.The Map function performs actions like filtering, grouping and sorting.While Reduce function aggregates and summarizes the result produced by map function.The result generated by the Map function is a key value pair (K, V) which acts as the input for Reduce function.You can go through this video to understand Hadoop & it’s architecture in detail.Install Hadoop Single Node and Multi Node ClusterThen you can go through this Hadoop Ecosystem blog to learn Hadoop Ecosystem in detail.You can also go through this Hadoop Ecosystem tutorial video.PigPIG has two parts: Pig Latin, the language and the pig runtime, for the execution environment. You can better understand it as Java and JVM. It supports pig latin language.As everyone does not belong from a programming background. So, Apache PIG relieves them. You might be curious to know how?Well, I will tell you an interesting fact:10 line of pig latin = approx. 200 lines of Map-Reduce Java codeBut don’t be shocked when I say that at the back end of Pig job, a map-reduce job executes. The compiler internally converts pig latin to MapReduce. It produces a sequential set of MapReduce jobs, and that’s an abstraction (which works like black box). PIG was initially developed by Yahoo. It gives you a platform for building data flow for ETL (Extract, Transform and Load), processing and analyzing huge data sets.HiveFacebook created HIVE for people who are fluent with SQL. Thus, HIVE makes them feel at home while working in a Hadoop Ecosystem. Basically, HIVE is a data warehousing component which performs reading, writing and managing large data sets in a distributed environment using SQL-like interface.HIVE + SQL = HQLThe query language of Hive is called Hive Query Language(HQL), which is very similar like SQL. Hive is highly scalable. As, it can serve both the purposes, i.e. large data set processing (i.e. Batch query processing) and real time processing (i.e. Interactive query processing). Hive gets internally gets converted into MapReduce programs.It supports all primitive data types of SQL. You can use predefined functions, or write tailored user defined functions (UDF) also to accomplish your specific needs.You can store data in HBase based on you requirements.HBaseHBase is an open source, non-relational distributed database. In other words, it is a NoSQL database. It supports all types of data and that is why, it’s capable of handling anything and everything inside a Hadoop ecosystem. It is modelled after Google’s BigTable, which is a distributed storage system designed to cope up with large data sets.The HBase was designed to run on top of HDFS and provides BigTable like capabilities. It gives us a fault tolerant way of storing sparse data, which is common in most Big Data use cases. The HBase is written in Java, whereas HBase applications can be written in REST, Avro and Thrift APIs.For better understanding, let us take an example. You have billions of customer emails and you need to find out the number of customers who has used the word complaint in their emails. The request needs to be processed quickly (i.e. at real time). So, here we are handling a large data set while retrieving a small amount of data. For solving these kind of problems, HBase was designed.Edureka provides a good list of Hadoop Tutorial videos. I would recommend you to go through this Hadoop tutorial video playlist as well as Hadoop Tutorial blog series. Your learning should be aligned with Big Data Certifications.

Organisations have realized the benefits of Big Data analytics. It helped them in getting better business insights, which enhanced their decision-making capabilities. As organisations are showing interest in Big Data and are adopting Hadoop to analyse it. Hence, the demand of jobs in Big Data and Hadoop is also rising rapidly. If someone is interested in analyzing data and wants to pursue his or her career in this field, for them now is the right time to start learning Hadoop and Spark. This would always give you a good start either as a fresher or experienced. Let me take you through some of the facts:Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 - ForbesMcKinsey predicts that by 2018 there will be a shortage of 1.5M data experts - Mckinsey ReportAvg salary of Big Data Hadoop Developers is $135k - Indeed Salary DataYou should go through this Big Data Career blog to understand the benefits of adopting career in Big Data. I would also recommend you to go through this Big Data career video.This Edureka Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get edureka Hadoop certification after the project completion.The Edureka hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to-Master the concepts of HDFS and MapReduce frameworkUnderstand Hadoop 2.x ArchitectureSetup Hadoop Cluster and write Complex MapReduce programsLearn data loading techniques using Sqoop and FlumePerform data analytics using Pig, Hive and YARNImplement HBase and MapReduce integrationImplement Advanced Usage and IndexingSchedule jobs using OozieImplement best practices for Hadoop developmentUnderstand Spark and its EcosystemLearn how to work in RDD in SparkWork on a real life Project on Big Data AnalyticsI would also like to tell you that projects & hands-on practice are very important while going through the course. Some of the projects that you will be going through are:Project #1: Analyze social bookmarking sites to find insightsProject #2: Customer Complaints AnalysisProject #3: Tourism Data AnalysisProject #4: Airline Data AnalysisProject #5: Analyze Loan DatasetProject #6: Analyze Movie RatingsProject #7: Analyze YouTube dataand many more.At Edureka you get:Instructor-led Sessions: 30hrs of Online Live Instructor-led Classes. Weekend class:10 sessions of 3 hours each and Weekday class:15 sessions of 2 hours each.Real-life Case Studies: Live project based on any of the selected use cases, involving Big Data Analytics.Assignments: Each class will be followed by practical assignments which can be completed before the next class.Lifetime Access: You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.24 x 7 Expert Support: We have 24x7 online support team available to help you with any technical queries you may have during the course.Certification: Towards the end of the course, you will be working on a project. Edureka certifies you as an Big Data and Hadoop Expert based on the project.Forum: We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.—————————————————————————————————————You should go through Big Data & Hadoop in advance, so you would be clear about the topics that are going to be discussed in the class.I would suggest you to first understand Big Data and challenges associated with Big Data. So , that you can understand how Hadoop emerged as a solution to those Big Data problems. This What is Hadoop and Hadoop Tuorial blog will introduce you to that.Then you should understand how Hadoop architecture works in respect of HDFS, YARN & MapReduce.Further moving on you should install Hadoop on your system so that you can start working with Hadoop. This will help you in understanding the practical aspects in detail.Further moving on take a deep dive into Hadoop Ecosystem and learn various tools inside Hadoop Ecosystem with their functionalities. So, that you will learn how to create a tailored solution according to your requirements.Let us understand in brief:What is Big Data?Big Data is a term used for a collection of data sets that are large and complex, which is difficult to store and process using available database management tools or traditional data processing applications. The challenge includes capturing, curating, storing, searching, sharing, transferring, analyzing and visualization of this data.It is characterized by 5 V’s.VOLUME: Volume refers to the ‘amount of data’, which is growing day by day at a very fast pace.VELOCITY: Velocity is defined as the pace at which different sources generate the data every day. This flow of data is massive and continuous.VARIETY: As there are many sources which are contributing to Big Data, the type of data they are generating is different. It can be structured, semi-structured or unstructured.VALUE: It is all well and good to have access to big data but unless we can turn it into value it is useless. Find insights in the data and make benefit out of it.VERACITY: Veracity refers to the data in doubt or uncertainty of data available due to data inconsistency and incompleteness.What is Hadoop & it’s architecture?The main components of HDFS are NameNode and DataNode.NameNodeIt is the master daemon that maintainsand manages the DataNodes (slave nodes). It records the metadata of all the files stored in the cluster, e.g. location of blocks stored, the size of the files, permissions, hierarchy, etc. It records each and every change that takes place to the file system metadata.For example, if a file is deleted in HDFS, the NameNode will immediately record this in the EditLog. It regularly receives a Heartbeat and a block report from all the DataNodes in the cluster to ensure that the DataNodes are live. It keeps a record of all the blocks in HDFS and in which nodes these blocks are stored.DataNodeThese are slave daemons which runs on each slave machine. The actual data is stored on DataNodes. They are responsible for serving read and write requests from the clients. They are also responsible for creating blocks, deleting blocks and replicating the same based on the decisions taken by the NameNode.For processing , we use YARN(Yet Another Resource Negotiator). The components of YARN are ResourceManager and NodeManager.ResourceManagerIt is a cluster level (one for each cluster) component and runs on the master machine. It manages resources and schedule applications running on top of YARN.NodeManagerIt is a node level component (one on each node) and runs on each slave machine. It is responsible for managing containers and monitoring resource utilization in each container. It also keeps track of node health and log management. It continuously communicates with ResourceManager to remain up-to-date .So, you can perform parallel processing on HDFS using MapReduce.MapReduceIt is the core component of processing in a Hadoop Ecosystem as it provides the logic of processing. In other words, MapReduce is a software framework which helps in writing applications that processes large data sets using distributed and parallel algorithms inside Hadoop environment. In a MapReduce program, Map() and Reduce() are two functions.The Map function performs actions like filtering, grouping and sorting.While Reduce function aggregates and summarizes the result produced by map function.The result generated by the Map function is a key value pair (K, V) which acts as the input for Reduce function.You can go through this video to understand Hadoop & it’s architecture in detail.Install Hadoop Single Node and Multi Node ClusterThen you can go through this Hadoop Ecosystem blog to learn Hadoop Ecosystem in detail.You can also go through this Hadoop Ecosystem tutorial video.PigPIG has two parts: Pig Latin, the language and the pig runtime, for the execution environment. You can better understand it as Java and JVM. It supports pig latin language.As everyone does not belong from a programming background. So, Apache PIG relieves them. You might be curious to know how?Well, I will tell you an interesting fact:10 line of pig latin = approx. 200 lines of Map-Reduce Java codeBut don’t be shocked when I say that at the back end of Pig job, a map-reduce job executes. The compiler internally converts pig latin to MapReduce. It produces a sequential set of MapReduce jobs, and that’s an abstraction (which works like black box). PIG was initially developed by Yahoo. It gives you a platform for building data flow for ETL (Extract, Transform and Load), processing and analyzing huge data sets.HiveFacebook created HIVE for people who are fluent with SQL. Thus, HIVE makes them feel at home while working in a Hadoop Ecosystem. Basically, HIVE is a data warehousing component which performs reading, writing and managing large data sets in a distributed environment using SQL-like interface.HIVE + SQL = HQLThe query language of Hive is called Hive Query Language(HQL), which is very similar like SQL. Hive is highly scalable. As, it can serve both the purposes, i.e. large data set processing (i.e. Batch query processing) and real time processing (i.e. Interactive query processing). Hive gets internally gets converted into MapReduce programs.It supports all primitive data types of SQL. You can use predefined functions, or write tailored user defined functions (UDF) also to accomplish your specific needs.You can store data in HBase based on you requirements.HBaseHBase is an open source, non-relational distributed database. In other words, it is a NoSQL database. It supports all types of data and that is why, it’s capable of handling anything and everything inside a Hadoop ecosystem. It is modelled after Google’s BigTable, which is a distributed storage system designed to cope up with large data sets.The HBase was designed to run on top of HDFS and provides BigTable like capabilities. It gives us a fault tolerant way of storing sparse data, which is common in most Big Data use cases. The HBase is written in Java, whereas HBase applications can be written in REST, Avro and Thrift APIs.For better understanding, let us take an example. You have billions of customer emails and you need to find out the number of customers who has used the word complaint in their emails. The request needs to be processed quickly (i.e. at real time). So, here we are handling a large data set while retrieving a small amount of data. For solving these kind of problems, HBase was designed.Edureka provides a good list of Hadoop Tutorial videos. I would recommend you to go through this Hadoop tutorial video playlist as well as Hadoop Tutorial blog series. Your learning should be aligned with Big Data Certifications.