T E C H N O L O G Y T R A N S F E R P R E S E N T S

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1 T E C H N O L O G Y T R A N S F E R P R E S E N T S Rome, December Residenza di Ripetta Via di Ripetta, 231 INTERNATIONAL S U M M I T BIG DATA ANALYTICS

2 A B o U T T H E S U M M I T In 2014 almost every business in Europe has turned its focus on two things. The first is to improve customer insight and experience to retain and grow its customers. The second is to improve its processes by reducing cost, increasing efficiency and improving effectiveness. The desire to improve customer experience is driving business demand for new customer insight to add to what companies already know. Improving processes is resulting in companies deploying new technologies like sensors that provide new data to better understand what is happening in operations. All of this means that companies want to analyse new data sources, both internal and external, that can be very large in volume, may vary in data type, and that can be generated at very fast rates. In particular there is a requirement to understand customer behaviour and interaction across all channels. Clickstream data in particular is in very high demand as more and more people switch to on-line as the preferred way to transact business. Understanding on-line behaviour is now insight most companies most companies want to understand. There is also a need to analyse social interactions and sentiment. It is not surprising therefore that Hadoop is now at the centre of Big Data Analytics. In addition, the shift to on-line and the emergence of sensor data has also created a demand to speed up the capture and serving up of non-transactional data which in turn has seen NoSQL DBMSs grow in popularity. New types of analytical workloads are now running on platforms outside of a Data Warehouse. In addition, many new start-ups have emerged focused on everything from Hadoop Data Hubs, accelerating parts of the Hadoop stack, NoSQL databases, streaming analytics, in-memory file systems and in-memory processing, self-service data integration, exploratory analysis, search, machine learning, self-service BI and SQL on Hadoop. But how do you make sense of all of this? How do you align with business needs? What should be in a Big Data strategy? How does all this link together and fit into your existing architecture? How do you get started? How do you design NoSQL databases? Should you offload any existing workloads to Hadoop? How do you organize Hadoop? What are the best practices? This Summit is looks at how to use Big Data for competitive advantage. It looks at popular Big Data use cases, Hadoop deep dive, streaming analytics, NoSQL DBMSs and how to integrate Big Data into your existing analytical architecture. It also looks at the growing problem of data governance and data management in a Big Data environment and how to deal with this.

3 A B o U T T H E S U M M I T Examples of topics that will be covered include: The Business Value of Big Data Popular Use Cases for Big Data Technologies Integrating Big Data Analytics And Technologies Into Your Existing Analytical Environment Hadoop - Best Practices in Successful Implementation Using Big Data Analytics And Self-Service BI for New Customer Insight Data Modeling for NoSQL Databases Analysing Data in Motion using Apache Storm Analytical SQL Databases for Big Data Data Governance - Building A Managed Data Hub on Hadoop

4 SPEAKERS Mike Ferguson He is Managing Director of Intelligent Business Strategies Limited. As an analyst and consultant he specialises in Business Intelligence, Analytics, Big Data, and Data Management. With over 33 years of IT experience, Mr. Ferguson has consulted for dozens of companies, spoken at events all over the world and written numerous articles. Formerly he was a principal and co-founder of Codd and Date Europe Limited the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of DataBase Associates. Bernard Marr He is founder and CEO, Advanced Performance Institute. He is a best-selling business author, keynote speaker and consultant in strategy management, performance management, analytics, KPIs and Big Data. He helps companies to better manage, measure, report and analyse performance. Has worked work with major companies, organisations and governments across the globe. Mr. Marr has written a number of books, reports and articles on enterprise performance and analytics. This includes the bestsellers Key Performance Indicators, The Intelligent Company, More with Less, Managing and Delivering Performance and Strategic Performance Management. Bernard Marr Rick Van Der Lans REGISTRATION FORM Mike Ferguson Vladimir Bacvanski Once filled to be given to: Technology Transfer Piazza Cavour, Roma Tel Fax info@technologytransfer.it

5 Who should Attend Who should Attend PARTICIPATION FEE Euro 1400 IT Directors, CIO s, IT Managers, IT Architects, BI Managers, Data Warehousing Professionals, Data Scientists, Enterprise Architects, Data Architects The fee includes all seminar documentation, luncheon and coffee breaks. HOW TO REGISTER You must send the registration form with the receipt of the payment to: TECHNOLOGY TRANSFER S.r.l. Piazza Cavour, Rome (Italy) Fax PAYMENT Wire transfer to: Technology Transfer S.r.l. Banca: Cariparma Agenzia 1 di Roma Iban Code: IT 03 W within November 19, 2014 GROUP DISCOUNT If a company registers 5 participants to the same seminar, it will pay only for 4. Those who benefit of this discount are not entitled to other discounts for the same seminar. EARLY REGISTRATION The participants who will register 30 days before the seminar are entitled to a 5% discount. CANCELLATION POLICY A full refund is given for any cancella tion re ceived more than 15 days before the seminar starts. Cancellations less than 15 days prior the event are liable for 50% of the fee. Cancellations less than one week prior to the event date will be liable for the full fee. CANCELLATION LIABILITY In the case of cancellation of an event for any reason, Technology Transfer s liability is limited to the return of the registration fee only. Rick Van Der Lans He is Managing Director of R20/Consultancy based in The Netherlands. He is an independent analyst, consultant, author and lecturer specializing in Data Warehousing, Business Intelligence, Big Data, Data Virtualization, and Database Technology. Mr. van der Lans has advised many large companies worldwide on defining their Data Warehouse, Business Intelligence, and SOA architectures. He has lectured all over the world for over twenty five years and has written a number popular books, including Introduction to SQL and SQL for MySQL Developers, that have been translated into many languages and have sold over 100,000 copies. Recently, he has published a very successful book Data Virtualisation for Business Intelligence. ROME December Residenza di Ripetta Via di Ripetta, 231 Registration fee Euro 1400 first name surname job title organisation address postcode city country telephone fax Stamp and signature SEMINAR TIMETABLE 2 days: 9.30 am pm 2.00 pm pm International Summit 2014 Vladimir Bacvanski He is founder of SciSpike. He is a consultant and mentor on advanced software and data technologies and specialises in NoSQL and Big Data technologies. He has over twenty years of experience with software technologies in areas such as architecture and design of mission critical and distributed enterprise systems, rule-based systems and languages, modeling tools, real-time systems, agent systems, and database technologies. Mr. Bacvanski also has extensive experience in software architecture and requirements analysis, and has helped many companies to select, transition, and apply new software technologies. He is published worldwide and is a frequent speaker, session chair, and workshop organizer at leading industry events. He is also the recipient of a number of prestigious academic scholarships and grants. Mr. bacvanski received a Doctoral degree in Computer Science from Aachen University of Technology (RWTH Aachen) in Germany.

6 O U T L I N E First Day Session 1 The Business Value of Big Data Popular Use Cases for Big Data Technologies Bernard Marr While the hype around Big Data appears to be never ending, many companies are now getting down to planning and implementation Big Data projects. To do this, they need to first determine how Big Data and Big Data Analytics technologies can be used for competitive advantage and then put together a Big Data strategy. This session addresses how companies can get value out of Big Data to get business buy in and key things to consider in your Big Data strategy. Understanding the business case for Big Data The 10 most popular ways Big Data can benefit your company Simple ways of finding and leveraging new and exciting data sets Many real world examples of how companies across all sectors are using Big Data today Five simple steps of creating a Big Data strategy for your business Session 2 Integrating Big Data Analytics and Technologies into Your Existing Analytical Environment Mike Ferguson Most organizations today have a significant investment in traditional data warehouse and BI technology. However, the arrival of new Big Data sources, and new technology platforms has resulted in many organizations trying to understand how all this should fit together in a modern analytical architecture. This session looks at traditional and new Big Data workloads, and shows how traditional DW/BI and new Big Data analytics technologies can be brought together to create an integrated analytical ecosystem. Component technologies in a modern analytical ecosystem Deploying analytics across Big Data and traditional environments Accessing Big Data using existing BI tools and SQL on Hadoop Data Management across traditional and Big Data environments Dealing with high volume and high velocity data ingest The organisational operating model for the modern analytical ecosystem Session 3 Hadoop - Best Practices in Successful Implementation Vladimir Bacvanski Big Data and Hadoop are the buzzwords of modern IT. A few years ago, the ability to process large volumes of data was sufficient. Many beginners mistakes have been made. Today, as Hadoop is more mature, we expect a more sophisticated usage. In this talk, we address the application cases for Hadoop and its key elements. We will compare the Classic Hadoop with Hadoop 2.0 and YARN, the new architecture for Hadoop, and present the migration path. Hadoop can be programmed in several ways using Java, Pig, Hive, Scala (Scalding). We will explore the best application cases for each of them. Integration with existing systems and governance are a critical part of succeeding with Hadoop, and we discuss the best practices and quick wins. At the end, we will address the growing Hadoop ecosystem and exciting systems like Storm and Spark. Application case for Hadoop A quick overview of data processing with Hadoop Hadoop 2.0, YARN and migration paths Choosing the right programming model: Java, Pig, Hive, Scala and Scalding Integration with conventional systems; Hadoop for ETL Governance Achieving quick wins Expanding the horizon with Apache Storm and Spark Traditional Data Warehouse architectures New data, new Big Data analytical workloads Why a new architecture is needed for multi-platform analytics

7 Session 4 Using Big Data Analytics and Self-Service BI for New Customer Insight Bernard Marr For many organizations, the focus around customer is now top priority in the boardroom. The demand for customer insight is needed to retain existing customers, grow existing customer value, acquire new customers and uncover new markets and opportunities. In particular, the switch to the Web to transact business is resulting in deeper insight being needed to understand customer on-line behaviour and improve customer experience management and improve customer satisfaction. This session looks at how companies can leverage Big Data Analytics and self-service BI to improve overall customer knowledge for competitive advantage. Understanding how Big Data can deliver fascinating customer insights Getting a completely new 360-degree view of your customers Using Big Data Analytics to really target your customers Improving decision-making at every customer touch point Many real word examples of how companies are using Big Data Analytics and self-service BI to better understand and target customers Secondo Day Session 5 Data Modeling for NoSQL Databases Rick van der Lans Many Big Data systems are developed using NoSQL data storage products, such as MongoDB, HBase, MapR, and Cassandra. These products have been designed to store and/or process massive amounts of data. For some organizations they make it possible to develop systems that were unthinkable a few years ago. However, as the name already indicates, these are not our well-known SQL or relational systems. They don t use relational tables with columns and rows to store data. As building blocks they support hierarchical structures, repeating groups, column families, sets, lists, and so on. And that, as can be imagined, has a direct impact on data modeling. This session discusses how data modeling changes when the data storage technology is a NoSQL system. Overview of key-value, document, column-family, and graph data stores Explanation of non-relational concepts, such as column families, hierarchies, sets, and lists Is storing unstructured and semi-structured data really more flexible? The differences between schema-on-read and schema-on-write Rules for transforming classic data models to NoSQL concepts Session 6 Analysing Data in Motion using Apache Storm Vladimir Bacvanski Velocity is one of the key aspects of Big Data, and it is not handled by traditional Hadoop systems. Apache Storm, originally developed at Twitter to analyze tweets in real time, is a dominant stream processing and analytics system for the new Hadoop YARN architecture. In this talk we explore stream analytics, the Storm architecture and programming model, and ways to achieve scalable, fault tolerant solutions. We conclude with an overview of Lambda Architecture, which combines Big Data with real-time stream processing.

8 Data in motion and real-time processing: use cases Contrasting Storm and Hadoop data processing Storm Architecture Processing topologies, streams, spouts, and bolts Achieving high performance, parallelism, and scalability Achieving fault tolerance - cluster setup Writing Storm applications Lambda architecture Best practices of Storm applications Session 7 Panel: Is Big Data Just Hype or Does It Offer Real Business Advantage? Speakers + Vendors Session 8 Analytical SQL Databases for Big Data Rick van der Lans It s hard to be innovative when your BI team is deluged with fixes, fighting Hadoop and other NoSQL platforms are being proposed by many to be the data storage technology for big data systems. But it s not the only alternative. Organizations can also opt for specialized analytical SQL database servers. Products that, on the outside, resemble classic SQL database servers, but have been optimized internally for complex analytics and reporting on large data sets. The advantages of this technology are productivity, familiarity, and accessible by countless tools for reporting and analytics. This session presents an overview of the market of analytical SQL database servers. Examples of questions addressed are: Which techniques do they use internally to speed up query performance? How do they integrate with Hadoop? Why is the European market slow in adopting this technology? How do they compare with Hadoop and NoSQL? Market overview including Actian Matrix and Vector, Exasol, HP Vertica, IBM PureData System for Analytics, Kognitio, Microsoft Analytics Platform System, Pivotal Greenplum, SAP HANA and SAP IQ, Teradata Appliance and Teradata Aster Database Should SQL-on-Hadoop engines be qualified as analytical SQL database servers? Storing and accessing SQL data in Hadoop The importance of productivity and agility Session 9 Data Governance - Building A Managed Data Hub on Hadoop Mike Ferguson, Intelligent Business Strategies Most organizations that adopted Hadoop early found themselves developing analytic applications on a highly scalable, low cost platform that offered high availability and good performance but with the bar set very high on the entry skills needed to work in this environment. Data scientists not only needed strong skills in statistics and mathematics, but also skills in Java programming coding to low level HDFS and MapReduce APIs. However the last 18 months has seen major improvements to the Hadoop platform and the arrival of much needed new tools that have made Hadoop a friendlier place. The result has been a faster uptake of Hadoop by many organizations attracted by the possibility of offloading some workloads from their Data Warehouses to a lower cost Hadoop platform as well as using Hadoop to process and analyse new types of data. In particular many companies are considering offloading ETL processing. This session addresses these use cases and looks at how to set up Hadoop as a managed data hub. What is a Data Hub and why have one? What should a Managed Data Hub do? What are the components in a Data Hub? Raw data collection and the Data Refinery What is involved in the data refining process? How should you organize your data in a Hadoop data refinery? Tools and techniques for collecting and capturing big data in a Hadoop data hub Tools and techniques for ETL processing on Hadoop Introducing analytics into Hadoop ETL workflows for automation Creating and managing analytical sandboxes in the Hadoop data refinery Data governance in a managed Data Hub auditing, securing and protecting data in a Hadoop environment Mapping new insights into your shared business vocabulary Integrating a managed Hadoop Data Hub into you analytical environment The virtual Data Hub multiple Hadoop deployments on a high performance grid

9 TECHNOLOGY TRANSFER Piazza Cavour, Roma Tel Fax info@technologytransfer.it