Big Data
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Understanding Big Data summarizes big data and popular big data technologies. It discusses how big data is generated from various sources and is too large to be processed by traditional databases. Popular technologies like Hadoop, HDFS, MapReduce, Hive, Pig, HBase, and Mahout are able to collect, store, process, and analyze big data. Companies are using big data to gain insights from customer data, optimize operations, prevent fraud, and make recommendations.
Big Data
- 1. UNDERSTANDING BIG DATAS THINK BIG MEHMET BURAK AKGÜN
- 2. First of All What is Big Data? • BIG-DATA is collected as non-structural from different sources Such as Social media sharing, network logs, blogs, photos, videos, log files, etc.. • BIG-DATA is Can not be analyzed before Enormous size and / or diversity information over the data
- 3. 1) Why Big Data ? How did we come this point? 2) What is the Big Data’s Components? 3) How will the integration with existing systems? 4) Is there any BIG Data Platform / Applications?
- 4. • Almost all data scientists believed that too important advantages of companies using with the ability to analyze information collected from all sources . • For example, retailers; according to scientists analyze they can through data can improve their operations by 60 percent profit margin. Similar rates are also valid for the public sector. • According to research, The US healthcare using to data scientists can save 300 million dollars a year. • According to estimates in 2020, from household appliances to cars and phones and about 50 billion devices will produce data and will be silently communicate with each other. • For companies which wish to take decisions in the forward-looking , predictions should be critically correct from these «Big Data».
- 5. ● Energy companies, using smart grids and meters, consisting of the use of data relating to individual subscribers, store, handle the event. ● Banks has become 7/24 branch as according to the information they store regarding collecting customers , recognizing the user, the Internet branch knows the day nor to enter and accordingly the main page menu that makes the most efficient, customers who reminders, offers customizable interfaces, rich, fast and convenient branch . ● Hospitals saving datas on their databases for effective to provide medical services ● This information will be stored as "Big Data"
- 7. Google without using conventional methods, created their technologies of requirenments,improving itself was a success. Google has billions of web pages on the Google File System keeps uses Big Table in the database, using MapReduce for processing Big Data. See http://www.google.com/about/datacenters Google and Amazon publishes academic articles are related to their work. Some developers who inspired by the articles such as Doug Cutting created similar technology as secret. These are usually the most beautiful examples of the Apache Lucene, Left, Hadoop, HBase such projects. Each of these projects can successfully use the Big Datas . The second generation of the companies such as Facebook, Twitter, Linkedin, are going a step forward by publishing them store it as open-source projects developed for Big data. Cassandra, Hive, Pig, Voldemort, Storm, indextank projects are examples.
- 8. Peki Büyük Veri nasıl depolanacak? Nasıl işlenecek? Nasıl analiz edilecek?
- 9. Volume of Tweets create daily. 12+ terabytes Variety of different types of data. 100’s Veracity decision makers trust their information. Only 1 in 3 4 «components» for being Big trade events per second. 5+ million Velocity
- 11. • It is expecting to reach 3 billion users.In August 2015, 12TB / day «log data» is producing. The end of 2014 expected reach is 500+ million users. 160 million users are online. • • • 100 million active users. 12+ TB of data tweets / day! .. Social Networks and Social Work
- 12. Google process 24 Petabytes data every day 4.6 Billion mobile phone exist 2 Billion Internet users annual traffic in 2014 equals 667 Exabytes Social Networks and Social Work
- 13. Users are not just people! ..
- 14. Each engine 10TB / 30 Mins data produces.
- 15. “Data generated by machines and sensors will exceed that generated by social media by at least a factor of 10.” * Leon Katsnelson Program Director, Big Data & Cloud Computing IBM
- 16. By the way, We don't have enough space to store all this data!
- 17. We actually appear as a game company.In fact we are data analysis company.. Ken Rudin, Zynga VP of Analytics • Offers completely free game facilities. • Gaining revenue by selling virtual goods. • The monthly average has 232m active users. • 95% of players never visited shop! • With Using Big Data analysis they disturbed to the game world.
- 18. Four Entry Points of Big Data Unlock Big Data Simplify Your Warehouse Preprocess Raw Data Analyse Streaming Data IBM Big Data Platform Systems Management Application Development Visualization & Discovery Accelerators Information Integration & Governance Hadoop System Stream Computing Data Warehouse BI / Reporting Exploration / Visualization Functional App Industry App Predictive Analytics Content Analytics Analytic Applications
- 19. Applications for Big Data Analytics Homeland Security FinanceSmarter Healthcare Multi-channel sales Telecom Manufacturing Traffic Control Trading Analytics Fraud and Risk Log Analysis Search Quality Retail: Churn, NBO
- 20. "Companies which give importance to Social media is gaining" In Turkey Organized by the Teradata "Big Data: Great Opportunity" themed event brought together senior executives of the company in Istanbul. President of Teradata EMEA Hermann Wimmer, ‘companies that want to increase their profitability by preventing competitors from social media by analyzing the data obtained in a short time, said he helped to make quick decisions.’
- 21. Technological solution is preferring when it provides advantages
- 23. Big Data and Open Source Nested • Open Source Community contribution made over the years - Apache Hadoop ve Jaql, Apache Derby, Apache Geronimo, Apache Jakarta - Eclipse: was founded by IBM. - Lucene ;IBM Lucene Extension Library (ILEL) - DRDA, XQuery, SQL, XML4J, XERCES, HTTP, Java, Linux... • Open source IBM Softwares – WebSphere: Apache – Rational: Eclipse and Apache – InfoSphere: Eclipse and Apache • IBM’s BigInsights (Hadoop) is %100 open source software.
- 24. Hadoop • Open Source • Distributed Computing • Very Simply MapReduce • Connected computers
- 25. • Ebay – 532 node 532x8 Core – 4256 Core • Facebook – 1100 node 8800 core 12 PB Storage • LinkedIn – 1200 + 580 + 120 node • Quantcast – 3000 node 3.5 PB 1PB+ daily data Example Hadoop Installations
- 26. Hadoop • Components: – DataStorage-HDFS-Distributed DiskFileSystem – DataProcessing-MapReduce
- 27. HDFS • Google File System • Distributed Disk File System • File Clustering(Usually Files sizes are over then GBs • Fault tolerant • Replication • HDFS is knowledge about the location of physical.
- 28. HDFS • Accessible by with Hadoop Shell , Java API or Web UI • Four application node: – NameNode – manages the metadata of the file – SystemJob Tracker – MapReduce – Task Tracker – MapReduce – Data Node – InformationshidingwithNameNode
- 29. BIG DATA is not just HADOOP Manage & store huge volume of any data Hadoop File System MapReduce Manage streaming data Stream Computing Analyze unstructured data Text Analytics Engine Data WarehousingStructure and control data Integrate and govern all data sources Integration, Data Quality, Security, Lifecycle Management, MDM Understand and navigate federated big data sources Federated Discovery and Navigation
- 30. MapReduce • Big Data Processing -‐Google • Distributed Calculation Model • Key - the value binary data processing • Easy programming framework – for use:you should improve map() ve reduce() functions
- 31. Map: First Step • Make ready the next processing elements while matching with a key – Data Cleaning – Simply Calculating – Split Strings
- 32. Reduce: Last Step • Takes the list of values for Same key with Iterators – Filtering – Combining – Samping So reduced.. The result is written to HDFS or HBase
- 33. Architecture:MapReduce HDFS CLUSTER K1,V1 K1,V1 ….. K1,V1 K1,V1 … K1,V1 K1,V1 …. K1,V1 K1,V1 … M K2,V2 K2,V2 M K2,V2 K2,V2 M K2,V2 K2,V2 K2,V2 M … M M M M K2,V2 K2,V2 K2,V2 K2,V2 K2,V2 K2,V2 K2,V2 K2,V2 K2,V2 … GROUPING RANKING K2, Iterable<V2> K2, Iterable<V2> K2, Iterable <V2> R K3,V3 K3,V3 …
- 34. Apache Pig • Yahoo! • Designed to easy analysis of large data sets • Easy than Map Reduce function in Java – Pig Lan coding – Can be improved • Similar with each languages • 10 lines of Pig code may be equivalent to hundreds of lines Java
- 35. Run To Pig ! • Grunt – Shell • Java interface • Eclipse and IntelliJ IDEA plugins %tweets = load ‘/today/tweets’ as (user, mention, tweet) %twitters = group tweets by mention
- 36. Apache Hive • Data Warehouse Project for Hadoop • SummarizingData • Instant queries • SQL-like language –HiveQL – Allows to define custom mapper and reducer Special Note * : Hive compiler translates the MapReduce operations to SQL queries
- 37. HBase Every time we do not need to relational databases We need Scalability Table size can be very large so We want very fast access Distributed Key-‐Sorted Persistent Map
- 38. HBase Google – Big Table clon Works on HDFS *fault tolerance *scalability *MapReduces input-output Hbase = HDFS + Random read/ write
- 39. HBase Where to Use: Social Media Recommended Systems Search Engines Intelligence and Monitoring Services Financial Systems - fraud
- 40. Apache Mahout • Let us go beyond the simple analysis • Classification - Email Spam - Call Center • Clustering - finding new news • Recommended Systems
- 41. Apache Mahout Ready Algorithms:: • Classification: • Logisac Regression • Bayesian • Random Forests – Application • Call forwarding • Face recognition
- 42. Apache Mahout Ready Algorithms: Recommendation: • Distributed Item based • Matrix Factorizaaon • Non distributed item/user based – Application: • eCommerce - Amazon • Movie recommendation - netflix • Music recommendation - LastFM • Venue Recommendation - foursquare
- 43. In summary:Some Big Data Applications Log Analytics (IT for IT) Smart Grid / Smarter Utilities RFID Tracking & Analytics Fraud / Risk Management & Modeling 360° View of the Customer Warehouse Extension Email / Call Center Transcript Analysis Call Detail Record Analysis IBM Watson
- 44. The IBM Big Data Platform
- 45. IBM Big Data Platform IBM Big Data & Netezza Product Group InfoSphere BigInsights Hadoop-based, low latency, diverse and high-volume data analysis Hadoop IBM Netezza High Capacity Appliance Archived questionable structural data IBM Netezza 1000 BI+Ad Hoc Structured Data Analysis IBM Smart Analytics System Structural analysis of operational data IBM Informix Timeseries Time-structured analytics IBM InfoSphere Warehouse High volume, structural veri analizi Stream Computing InfoSphere Streams Fluid analysis for low latency data MPP Data Warehouse Information IntegrationInfoSphere Information Server High volume data integration and transformation
- 46. Big Data Exploration: Value & Diagram File Systems Relational Data Content Management Email CRM Supply Chain ERP RSS Feeds Cloud Custom Sources DataExplorerApplication/ Users Find, Visualize & Understand all big data to improve business knowledge • Greater efficiencies in business processes • New insights from combining and analyzing data types in new ways • Develop new business models with resulting increased market presence and revenue
- 47. Data Analysis in Different Diversity Making the analysis on the data in mixed feature. Dinamic Data Analysis High volume flow data, ad-hoc analysis Explore and experiment Data on Ad-hoc analysis, discovery and inspection data Manage and Plans Data Rules,Data integrity checking and application Very High Volume Data Analysis The data in the PB scale appropriate price / performance criteria for analysis What does IBM platform?