Presentation on theme: "Introduction to Hadoop Capabilities, Accelerators and Solutions."— Presentation transcript:
Introduction to Hadoop Capabilities, Accelerators and Solutions
Big Data 2 Google processed over 400 PB of data on datacenters composed of thousands of machines in September 2007 alone *** Today, every organization has it’s own big data problem and most are using Hadoop to solve it. *** MapReduce: Simplified Data Processing on Large Clusters, Communications of the ACM, vol. 51, no. 1 (2008), pp. 107-113, Jeffrey Dean and Sanjay Ghemawat
Where is Big Data? 3 Big Data Has Reached Every Market Sector Source – McKinsey & Company report. Big data: The next frontier for innovation, competition and productivity. May 2011.
Big Data Value Creation Opportunities Financial Services Detect fraud Model and manage risk Improve debt recovery rates Personalize banking/insurance Products Healthcare Optimal treatment pathways Remote patient monitoring Predictive modeling for new drugs Personalized Medicine Retail In-store behavior analysis Cross selling Optimize pricing, placement, design Optimize inventory and distribution Web/Social/Mobile Location-based marketing Social segmentation Sentiment analysis Price comparison Services Manufacturing Design to value Crowd-sourcing “Digital factory” for lean manufacturing Improve service via product sensor Data Government Reduce fraud Segment populations, customize action Support open data initiatives Automate decision Making 4
What is Hadoop? Hadoop is an open-source project overseen by the Apache Software Foundation Originally based on papers published by Google in 2003 and 2004 Hadoop is an ecosystem, not a single product Hadoop committers work at several different organizations – Including Facebook, Yahoo!, Twitter, Cloudera, Hortonworks 5
Hadoop - Inspiration 6 Google calls it:Hadoop equivalent GFSHDFS MapReduceHadoop MapReduce SawzallHive, Pig BigTableHBase ChubbyZooKeeper PregelGiraph You Say, “tomato…” Google was awarded a patent for “map reduce – a system for large scale data processing” in 2010, but blessed Apache Hadoop by granting a license.
Hadoop Timeline Started for Nutch at Yahoo! by Doug Cutting in early 2006 Hadoop 2.x, released in 2012, is basis for all current, stable Hadoop distributions Apache Hadoop 2.0.xx CDH4.* HDP2.* 7
Typical Data Strategy 8 ETL ToolsDW / MartsBIAnalytics Commercial Informatica Teradata Microstrategy SAS Oracle Data Integrator Oracle OBIEE TIBCO Spotfire IBM Datastage DB2, Netezza Cognos SPSS Microsoft SSIS SQL server Microsoft SSRS EMC Greenplum Open source Talend mySQL Pentaho, Jaspersoft R, RapidMiner
How Hadoop fits in? 9 Hadoop can complement the existing DW environment as well replace some of the components in a traditional data strategy.
How Hadoop fits in? 10 Storage HDFS – It’s a file system, not a DBMS HBase - Columnar storage that serves low-latency read / write request Extract / Load Source / Target is RDBMS - Sqoop, hiho Stream processing - Flume, Scribe, Chukwa, S4, Storm Transformation Map-reduce (Java or any other language), Pig, Hive, Oozie etc. Talend and Informatica have built products to abstract complexity of map-reduce Analytics RHadoop, Mahout BI – All existing players are coming up with Hadoop connectors
Hadoop Map Reduce What happens during a Map-reduce job’s lifetime? Clients submit MapReduce jobs to the JobTracker, a daemon that resides on “master node” The JobTracker assigns Map and Reduce tasks to other nodes on the cluster These nodes each run a software daemon known as the TaskTracker The TaskTracker is responsible for actually instantiating the Map or Reduce task, and reporting progress back to the JobTracker Terminology – A job is a ‘full program’ – a complete execution of Mappers and Reducers over a dataset A task is the execution of a single Mapper or Reducer over a slice of data A task attempt is a particular instance of an attempt to execute a task There will be at least as many task attempts as there are tasks If a task attempt fails, another will be started by the JobTracker Speculative execution can also result in more task attempts than completed tasks 15
Pig Latin Data-flow oriented language High-level language for routing data, allows easy integration of Java for complex tasks Data-types include sets, associative arrays, tuples 16 Client-side utility Pig interpreter converts the pig- script to Java map-reduce jobs and submits it to JobTracker No additional installs needed on Hadoop Cluster Pig performance ~ 1.4x Java MapReduce jobs, but lines of code needed ~ 1/10 th Developed at Yahoo!
Hive SQL-based data warehousing app Feature set is similar to Pig Language is more strictly SQL-esque Supports SELECT, JOIN, GROUP BY, etc. Uses “Schema on Read” philosophy Features for analyzing very large data sets Partition columns Sampling Buckets Requires install of metastore on Hadoop cluster Developed at Facebook 17
HBase Distributed, versioned, column-oriented store on top of HDFS Goal - To store tables with billion rows and million columns Provides an option of “low-latency” (OLTP) reads/writes along with support for batch-processing model of map-reduce HBase cluster consists of a single “HBase Master” and multiple “RegionServers” Facebook uses HBase to drive its messaging infrastructure Stats - Chat service supports over 300 million users who send over 120 billion messages per month Nulls are not stored by design and typical table storage looks like – 18 Row-keyColumn-familyColumnTimestampValue 1CFNameTs1Vijay 1CFAddressTs1Mumbai 1CFAddressTs2Goa
Sqoop RDBMS to Hadoop Command-line tool to import any JDBC supported database into Hadoop And also export data from Hadoop to any database Generates map-only jobs to connect to database and read/write records DB specific connectors contributed by vendors – Oraoop for Oracle by Quest software Teradata connector from Teradata Netezza connector from IBM Developed at Cloudera Oracle has come up with “Oracle Loader for Hadoop” and claim that it is optimized for “Oracle Database 11g” 19
Informatica HParser Graphical interface to design data transformation jobs Converts designed DT jobs to Hadoop Map-reduce jobs Out-of-the-box Hadoop parsing support for industry-standard formats, including Bloomberg, SWIFT, NACHA, HIPAA, HL7, ACORD, EDI X12, and EDIFACT etc. 20
Flume Flume is a distributed, reliable, available service for efficiently moving large amounts of data as it is produced Developed at Cloudera 21
Machine Learning Apache Mahout Scalable machine learning library most of the algorithms implemented on top Apache Hadoop using map/reduce paradigm Supported Algorithms – Recommendation mining - takes users’ behavior and find items said specified user might like. Clustering - takes e.g. text documents and groups them based on related document topics. Classification - learns from existing categorized documents what specific category documents look like and is able to assign unlabeled documents to the appropriate category. Frequent item set mining - takes a set of item groups (e.g. terms in a query session, shopping cart content) and identifies, which individual items typically appear together. RHadoop (from Revolution Analytics) and RHIPE (from Purdue University) allows executing R programs over Apache Hadoop 22
Graph Implementations Graph implementations follow the bulk-synchronous parallel model, popularized by Google’s Pregel – 1) Giraph (submitted to Apache Incubator) 2) GoldernOrb 3) Apache Hama 4) More – http://www.quora.com/What-are-some-good-MapReduce- implementations-for-graphshttp://www.quora.com/What-are-some-good-MapReduce- implementations-for-graphs 23
Hadoop Variants / Flavors / Distributions Apache Hadoop – Completely open and up-to-date version of Hadoop Cloudera’s distribution including Hadoop (CDH) Open source Hadoop tools packaged with “closed” management suite (SCM) Profits by providing support (Cost-model is per node in Cluster) & Trainings Hortonworks Data Platform Spun-off in 2011 from Yahoo!’s core Hadoop team Open source Hadoop tools packaged with “open” management suite (Apache Ambari) Profits by providing support (Cost-model is per node in Cluster) &Trainings Signed a deal with Microsoft to develop Hadoop for Windows MapR Claims to have developed faster version of HDFS MapR’s distribution powers EMC’s Greenplum products Oracle Big Data Appliance & IBM BigInsights Powered by CDH More may exist…….. 25
References Hadoop: The Definitive Guide by Tom White (Cloudera Inc.) Hadoop in Action by Chuck Lam () HBase: The Definitive Guide by Lars George (Cloudera Inc.) Mahout in Action by Sean Owen, Robin Anil, Ted Dunning, and Ellen Friedman Programming Pig by Alan Gates (Hortonworks)