Why data warehouse professional should move to big data and hadoop

Why data warehouse professional should move to big data and hadoop Why Should a Data Warehouse Professional Move to Big Data Hadoop? Back Home Categories Online Courses Mock Interviews Webinars NEW Community Write for Us Categories Artificial Intelligence AI vs Machine Learning vs Deep LearningMachine Learning AlgorithmsArtificial Intelligence TutorialWhat is Deep LearningDeep Learning TutorialInstall TensorFlowDeep Learning with PythonBackpropagationTensorFlow TutorialConvolutional Neural Network TutorialVIEW ALL BI and Visualization What is TableauTableau TutorialTableau Interview QuestionsWhat is InformaticaInformatica Interview QuestionsPower BI TutorialPower BI Interview QuestionsOLTP vs OLAPQlikView TutorialAdvanced Excel Formulas TutorialVIEW ALL Big Data What is HadoopHadoop ArchitectureHadoop TutorialHadoop Interview QuestionsHadoop EcosystemData Science vs Big Data vs Data AnalyticsWhat is Big DataMapReduce TutorialPig TutorialSpark TutorialSpark Interview QuestionsBig Data TutorialHive TutorialVIEW ALL Blockchain Blockchain TutorialWhat is BlockchainHyperledger FabricWhat Is EthereumEthereum TutorialB lockchain ApplicationsSolidity TutorialBlockchain ProgrammingHow Blockchain WorksVIEW ALL Cloud Computing What is AWSAWS TutorialAWS CertificationAzure Interview QuestionsAzure TutorialWhat Is Cloud ComputingWhat Is SalesforceIoT TutorialSalesforce TutorialSalesforce Interview QuestionsVIEW ALL Cyber Security Cloud SecurityWhat is CryptographyNmap TutorialSQL Injection AttacksHow To Install Kali LinuxHow to become an Ethical Hacker?Footprinting in Ethical HackingNetwork Scanning for Ethical HackingARP SpoofingApplication SecurityVIEW ALL Data Science Python Pandas TutorialWhat is Machine LearningMachine Learning TutorialMachine Learning ProjectsMachine Learning Interview QuestionsWhat Is Data ScienceSAS TutorialR TutorialData Science ProjectsHow to become a data scientistData Science Interview QuestionsData Scientist SalaryVIEW ALL Data Warehousing and ETL What is Data WarehouseDimension Table in Data WarehousingData Warehousing Interview QuestionsData warehouse architectureTalend T utorialTalend ETL ToolTalend Interview QuestionsFact Table and its TypesInformatica TransformationsInformatica TutorialVIEW ALL Databases What is MySQLMySQL Data TypesSQL JoinsSQL Data TypesWhat is MongoDBMongoDB Interview QuestionsMySQL TutorialSQL Interview QuestionsSQL CommandsMySQL Interview QuestionsVIEW ALL DevOps What is DevOpsDevOps vs AgileDevOps ToolsDevOps TutorialHow To Become A DevOps EngineerDevOps Interview QuestionsWhat Is DockerDocker TutorialDocker Interview QuestionsWhat Is ChefWhat Is KubernetesKubernetes TutorialVIEW ALL Front End Web Development What is JavaScript â€" All You Need To Know About JavaScriptJavaScript TutorialJavaScript Interview QuestionsJavaScript FrameworksAngular TutorialAngular Interview QuestionsWhat is REST API?React TutorialReact vs AngularjQuery TutorialNode TutorialReact Interview QuestionsVIEW ALL Mobile Development Android TutorialAndroid Interview QuestionsAndroid ArchitectureAndroid SQLite DatabaseProgramming Hadoop? Last updated on May 22,2019 6.7K Views Sudhaa Gopinath5 Comments Bookmark Become a Certified Professional Theabovevideo is the recorded session of the webinar on the topic Hadoop for Data Warehouse Professionals, which was conducted on 31st May14.Why should a Data Warehouse professional move to Big Data Hadoop?All Data Warehousing folks out there, are you aware of Hadoop and the Data warehousing paradigm? Do you realize how important it is to know Big Data and Hadoop, as Data Warehouse professionals? If the answer is Yes, then you can read this post to endorse your awareness. If No, then read ahead:Let us quickly look at the paradigm shift of Data Warehouse and Hadoop.Organizations across all industries are growing extremely fast, resulting in high volume, complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of d ata is so much more than what the existing Data Warehousing platforms can absorb and analyze. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate todays huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-structured or unstructured), it is cost effective as well.Watch our Presentation on Hadoops role in Data Warehousing:Hadoops role in Data Warehousing:Hadoops role in Data Warehousing is evolving rapidly. Initially, Hadoop was used as a transitory platform for extract, transform, and load (ETL) processing. In this role, Hadoop is used to offload processing and transformations performed in the data warehouse. This replaces an ELT (extract, load, and transform) process that required loading data into the data warehouse as a means to perform complex and large-scale transformations. With Hadoop, data is extracted and loaded into the Hadoop cluster where it can then be transformed, potentially in near-real time, with the results loaded into the data warehouse for further analysis.Offloading transformation processing to Hadoop frees up considerable capacity in the data warehouse, thereby postponing or avoiding an expensive expansion or upgrade to accommodate the relentless data deluge.Hadoop has a role to play in the front end of performing transformation processing as well as in the back end of offloading data from a data warehouse. With virtually unlimited scalability at a per-terabyte cost that is more than 50 times less than traditional data warehouses, Hadoop is quite well-suited for data archiving. Because Hadoop can perform analytics on the archived data, it is necessary to move only the specific result sets to the data warehouse (and not the full, large set of raw data) for further analysis.Appfluent, a da ta usage analytics provider calls this the Active Archive an oxymoron that accurately reflects the value-added potential of using Hadoop in todays data warehousing environment. They have found that for many companies, about 85 percent of their tables go unused, and that in the active tables, up to 50 percent of the columns go unused. The combination of eliminating dead data at the ETL stage and relocating dormant data to a low-cost Hadoop Active Archive can be considerable, resulting in truly extraordinary savings.Hadoops original MapReduce framework purpose-built for large-scale parallel processing is also eminently suitable for data analytics in a data warehouse.Hadoop effectively makes ETL integral to, and seamless with, data analytics and archival processing. It is this beginning-to-end role in Data Warehousing that has given impetus to what is Hadoops ultimate role as an enterprise data management hub in a multi-platform data analytics environmentNow that we have understood the Hadoop and Data Warehousing paradigm, let us get to know why Data Warehouse professionals should move to Big Data and Hadoop.With the numerous benefits offered by Hadoop, all leading organizations are moving their data management system from the traditional Data Warehousing to Big Data and Hadoop. When considering Data Warehousing as a career, it is better to be updated with the latest trends and products of database management. Hadoop will not replace relational databases or traditional Data Warehouse platforms at the moment, but its superior price/performance ratio will give organizations an option to lower costs while maintaining their existing applications and reporting infrastructure. Saying this, it leaves loads of possibility for Hadoop to take over the duties of a traditional Data Warehouse in the near future.How will Hadoop help you as a Data Warehousing professional?Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, v ariety and velocity of data, flawlessly and comparably in less time.As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a Hadoop-er.The other reasons are:Hadoop technology is certainly a mega trend in the IT industry. As a techie, I am sure you will find Hadoop interesting to learn.If your company has not currently implemented Hadoop, it does not mean it never will. If you manage to learn Hadoop beforehand, you can be an internal Hadoop and Big Data expert.Hadoop and Big Data have BIG opportunities and real potential to enhance your career in the data management sector.Hadoop codes are short and simple, so you can learn them very easily.So, why not make it Big with Big Data and Hadoop?Got a question for us? Please mention them in the comments section and we will get back to you.Related Posts:10 Reasons Why Big Data Analytics is the Best Career Move4 Practical Reasons to Learn Hadoop 2.07 Ways Big Data Training Can Change Your OrganizationRecommended videos for you Secure Your Hadoop Cluster With Kerberos Watch Now What Is Hadoop All You Need To Know About Hadoop Watch Now MapReduce Design Patterns Application of Join Pattern Watch Now 5 Scenarios: When To Use When Not to Use Hadoop Watch Now Spark SQL | Apache Spark Watch Now Ways to Succeed with Hadoop in 2015 Watch Now Apache Spark Will Replace Hadoop ! Know Why Watch Now Big Data Processing With Apache Spark Watch Now Filtering on HBase Using MapReduce Filtering Pattern Watch Now Apache Spark Redefining Big Data Processing Watch Now Improve Customer Service With Big Data Watch Now Hadoop Cluster With High Availability Watch Now Power of Python With BigData Watch Now Top Hadoop Interview Questions and Answers Ace Your Interview Watch Now Apache Kafka With Spark Streaming: Real-Time Analytics Redefined Watch Now Hadoop-A Highly Available And Secure Enterprise Data Warehousing Solution Watch Now Administer Hadoo p Cluster Watch Now Boost Your Data Career with Predictive Analytics! Learn How ? Watch Now Is It The Right Time For Me To Learn Hadoop ? Find out. Watch Now Hadoop Tutorial A Complete Tutorial For Hadoop Watch NowRecommended blogs for you Stateful Transformations with Windowing in Spark Streaming Read Article 4 Practical Reasons to Learn Hadoop 2.0 Read Article 5 Reasons to Learn Apache Spark Read Article Introduction to Spark with Python PySpark for Beginners Read Article Top Big Data Certifications Read Article Hadoop YARN Tutorial Learn the Fundamentals of YARN Architecture Read Article Big Data In Healthcare: How Hadoop Is Revolutionizing Healthcare Analytics Read Article Pig Tutorial: Apache Pig Architecture Twitter Case Study Read Article Everything About Cloudera Certified Developer for Apache Hadoop (CCDH) Read Article What are Kafka Streams and How are they implemented? Read Article Spark vs Hadoop: Which is the Best Big Data Framework? Read Article What is Hadoop? Int roduction to Big Data Hadoop Read Article Introduction to Hadoop 2.0 and Advantages of Hadoop 2.0 over 1.0 Read Article Explaining Kerberos Read Article Anatomy of a MapReduce Job in Apache Hadoop Read Article Apache Pig Installation on Linux Read Article How essential is Hadoop Training? Read Article Hadoop Administration Interview Questions and Answers For 2020 Read Article How To Create User In MongoDB? Read Article A Beginners Guide to Understanding Big Data Hadoop Read Article Comments 5 Comments Trending Courses in Big Data Big Data Hadoop Certification Training158k Enrolled LearnersWeekend/WeekdayLive Class Reviews 5 (62950)