© 2012 IBM Corporation IBM Security Systems 1 © 2013 IBM Corporation 1 Ecommerce Antoine Harfouche

© 2012 IBM Corporation IBM Security Systems 2 © 2013 IBM Corporation 2 Big Data Analytics Lecture Series Adapted from Kalapriya Kannan IBM Research Labs July, 2013

© 2012 IBM Corporation IBM Security Systems 3 © 2013 IBM Corporation 3 What is the aim of the course Focus is on “Systems” and applications for cloud-based storage and processing of BIG DATA. +Big Data - Definition +Big Data - Analytics +Big Data - Storage (HDFS) +Big Data - Computing (Map/Reduce) +Big Data - Database (HBase) +Big Data – Graph DB (Titan) +Big Data - Streaming (Strom)

© 2012 IBM Corporation IBM Security Systems 4 © 2013 IBM Corporation 4 “Learning is not just restricted to listening, it is actively asking relevant questions”

© 2012 IBM Corporation IBM Security Systems 5 © 2013 IBM Corporation 5  Get Convinced about “Big Data”  Understand why we need a different paradigm.  Ascertain with confidence the need to look at data computing in a different way.  Realize the potential of big data – All of you are skilled enough to get into it.  What we will not do – Do research on why things have evolved into the current trends as it stands. – Try to be hands-on – But not guaranteed Aim

© 2012 IBM Corporation IBM Security Systems 6 © 2013 IBM Corporation 6 What are we going to understand  What is Big Data?  Why we landed up there?  To whom does it matter  Where is the money?  Are we ready to handle it?  What are the concerns?  Tools and Technologies – Is Big Data Hadoop

© 2012 IBM Corporation IBM Security Systems 7 © 2013 IBM Corporation 7 Simple to start  What is the maximum file size you have dealt so far? –Movies/Files/Streaming video that you have used? –What have you observed?  What is the maximum download speed you get?  Simple computation –How much time to just transfer.

© 2012 IBM Corporation IBM Security Systems 8 © 2013 IBM Corporation 8 What is big data?  “ Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is “ big data.”

© 2012 IBM Corporation IBM Security Systems 9 © 2013 IBM Corporation 9 Huge amount of data  There are huge volumes of data in the world: + From the beginning of recorded time until 2003, + We created 5 billion gigabytes (exabytes) of data. + In 2011, the same amount was created every two days + In 2013, the same amount of data is created every 10 minutes.

© 2012 IBM Corporation IBM Security Systems 10 © 2013 IBM Corporation 10 Big data spans three dimensions: Volume, Velocity and Variety  Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information. – Turn 12 terabytes of Tweets created each day into improved product sentiment analysis – Convert 350 billion annual meter readings to better predict power consumption  Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value. – Scrutinize 5 million trade events created each day to identify potential fraud – Analyze 500 million daily call detail records in real-time to predict customer churn faster – The latest I have heard is 10 nano seconds delay is too much.  Variety: Big data is any type of data - structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together. – Monitor 100’s of live video feeds from surveillance cameras to target points of interest – Exploit the 80% data growth in images, video and documents to improve customer satisfaction

© 2012 IBM Corporation IBM Security Systems 11 © 2013 IBM Corporation 11 Finally…. `Big- Data’ is similar to ‘Small-data’ but bigger.. But having data bigger it requires different approaches: Techniques, tools, architecture … with an aim to solve new problems Or old problems in a better way

© 2012 IBM Corporation IBM Security Systems 12 © 2013 IBM Corporation 12 Whom does it matter  Research Community  Business Community - New tools, new capabilities, new infrastructure, new business models etc.,  On sectors Financial Services..

© 2012 IBM Corporation IBM Security Systems 13 © 2013 IBM Corporation 13 How are revenues looking like….

© 2012 IBM Corporation IBM Security Systems 14 © 2013 IBM Corporation 14 The Social Layer in an Instrumented Interconnected World 2+ billion people on the Web by end billion RFID tags today (1.3B in 2005) 4.6 billion camera phones world wide 100s of millions of GPS enabled devices sold annually 76 million smart meters in 2009… 200M by TBs of tweet data every day 25+ TBs of log data every day ? TBs of data every day

© 2012 IBM Corporation IBM Security Systems 15 © 2013 IBM Corporation 15 What does Big Data trigger?  From “Big Data and the Web: Algorithms for Data Intensive Scalable Computing”, Ph.D Thesis, Gianmarco

© 2012 IBM Corporation IBM Security Systems 16 © 2013 IBM Corporation 16 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 Warehousing Structure 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

© 2012 IBM Corporation IBM Security Systems 17 © 2013 IBM Corporation 17 Types of tools typically used in Big Data Scenario  Where is the processing hosted? – Distributed server/cloud  Where data is stored? – Distributed Storage (eg: Amazon s3)  Where is the programming model? – Distributed processing (Map Reduce)  How data is stored and indexed? – High performance schema free database  What operations are performed on the data? – Analytic/Semantic Processing (Eg. RDF/OWL)

© 2012 IBM Corporation IBM Security Systems 18 © 2013 IBM Corporation 18 When dealing with Big Data is hard  When the operations on data are complex: – Eg. Simple counting is not a complex problem. – Modeling and reasoning with data of different kinds can get extremely complex  Good news with big-data: – Often, because of the vast amount of data, modeling techniques can get simpler (e.g., smart counting can replace complex model-based analytics)… – …as long as we deal with the scale.

© 2012 IBM Corporation IBM Security Systems 19 © 2013 IBM Corporation 19 Why Big-Data?  Key enablers for the appearance and growth of ‘Big-Data’ are: + Increase in storage capabilities + Increase in processing power + Availability of data

© 2013 IBM Corporation IBM Security Systems 20 IBM big data IBM big data IBM big data IBM big data THINK