Tuesday, May 12, 2015

WELCOME & KEYNOTE - Understanding the Data Value Chain

Creating value from data requires a new mind-set. It’s hard to escape silos, whether they are technical or conceptual. To exploit fully the opportunity of Big Data tools and architectures, we need a new way of think- ing that frames data as a raw material of business. The answer is to focus not on the functional components— what you do to data—but on business outcomes and how they can be achieved—what you do with data. This new approach can be cultivated through looking at the data value chain.

MODERATOR: Marydee Ojala, Editor, Online Searcher, Computers in Libraries Magazine, & Editor-in-Chief, KMWorld Magazine

Edd Dumbill, VP Strategy, Silicon Valley Data Science

COFFEE BREAK in the Data Solutions Showcase

A102: Big Data Methodology

Tony Shan thinks about Big Data holistically, with a methodical framework that comprises the scientific and engineering steps in accelerating Big Data solutions: Problem, Diagnosis, Facts, Analysis, Hypothesis, Solution, Prototype, and Implementation. He then drills down to the key techniques and methods commonly used in these steps, such as root cause examination, process mapping, force field investigation, benchmarking, interview, brainstorming, focus group, Pareto chart, SWOT, impact evaluation, gap analysis, POC, and cost-benefit study. Best practices and lessons learned from the real-world Big Data projects are revealed in this session.

Tony Shan, Chief Architect, Wipro

Data Governance and Data Modeling

Casey Gwozdz, Principle Consultant, CA ERwin Products, CA Technologies

A103: Succeeding With Big Data Analytics

Opening the door to Big Data analytics may seem like an impossible task, but it’s not if you hold the right keys. Learn how building millions of predictive models in minutes is now computationally possible, more accurate, and operationally deployable from 1010data’s Ashfin Goodarzi, and join SiSense’s Eldad Farkash to hear about two Big Data projects that resulted in increased profitability and reduced customer churn, providing nontech- nical employees with real-time analytics, and gaining a handle on massive, exponentially growing datasets.

Big Data Mandated Analytics and Predictive Modeling

Afshin Goodarzi, Vice President, Chief Analyst, 1010data

Analytics Without the Headaches

Eldad Farkash, Founder & CTO (Chief Technology Officer), Sisense

A104: Winning Customers Through Business Intelligence

Mark Kryska, business intelligence leader at Kelly Services, Inc., has spent the last year working in its Business Intelligence Competency Center. Bringing a mathematical mind-set to business intelligence projects led him to technological solutions to winning customers at Kelly Services. From a customer case study, xplenty explains how to handle data challenges such as security, performance, scalability, and agility.

How Kelly Services Wins Customers Through Business Intelligence

Mark Kryska, Business Intelligence Manager, Kelly Services

Using xplenty to Integrate and Process Structured and Semi-structured Data

Yaniv Mor, Founder & CEO, Xplenty

NETWORKING RECEPTION in the Data Solutions Showcase

B102: Creating a Data Architecture for Business Intelligence and Analytics

The Analytical Data Architecture (ADA) represents the evolution of pragmatic practices for BI, data integration, and data warehousing. This framework leverages current and emerging technologies in its implementation supporting structured, semi-structured, and unstructured data. It includes data architecture, integration, and governance as they apply to business intelligence and analytics. ADA avoids pitting Big Data against "old school" architectures and presents a portfolio of data, information and technology architectural components to help enterprises implement BI and analytics.

Richard Sherman, Managing Partner, Athena IT Solutions

Data Virtualization: Plug 10 Key Gaps in Your BI/Analytics Capability Today (in <6 Months)

Suresh Chandrasekaran, Denodo

B103: Unlocking the Power of Machine Data

Machine data is one of the fastest growing, most complex, and most valuable segments of data. The technology infrastructure running your enterprise—the web servers, applications, network devices, mobile devices, sensors—generates massive streams of data, in an array of unpredictable formats that are difficult to process and analyze by traditional methods or in a timely manner. But why is it so valuable? Machine data contains a trace—a categorical record—of user behavior, cybersecurity risks, application behavior, service levels, fraudulent activity, and customer experience. If leveraged correctly, a company can gain a significant competitive advantage with machine data.

Why Unlocking Machine Data Is the Secret Ingredient to Your Business Success

Dejan Deklich, VP, Platform and Cloud Engineering, Splunk

Crowdflower Helps Unlock Data

Lukas Biewald, CEO & Founder, CrowdFlower

B104: Taking Advantage of New Data Management Technologies

More than ever before, businesses today run on data. Apache Ignite is the in-memory data fabric that combines in-memory cluster and computing, in-memory data grid, and in-memory streaming under one umbrella. Join this session to find out more about in-memory computing (IMC) and what is driving its exponential adoption, as well as the benefits of moving from an RDBMS view of the data world to a NoSQL view in order to leverage modern data models in your applications.

Apache Ignite: In-Memory Data Fabric

Dmitriy Setrakyan, EVP of Engineering, GridGain Systems, Inc.

Moving From a Traditional Database to a NoSQL Database

David Gorbet, VP of Engineering, MarkLogic

NETWORKING RECEPTION in the Data Solutions Showcase

C102: An Introduction to Oracle Enterprise Metadata Manager

As technology stacks expand and transform over time, one constant shines bright—the need for metadata. Metadata manager tools have come and gone over the years, but they always felt incomplete. The newly released Oracle Enterprise Metadata Manager is the world's first complete metadata tool. This session shows how the tool deals with the standard features of data lineage, impact analysis, and version management applied across a myriad of Oracle and non-Oracle technologies and combines them into a consistent metadata whole, including Oracle Database, Oracle Data Integrator, Oracle Business Intelligence, and Hadoop.

Stewart Bryson, Red Pill Analytics

C103: Integration of a SQL Database Oracle and a NoSQL Database Cassandra for a High Data Volume Big Data Application

Oracle is a strong relational database management system that well serves the use cases for which it was originally designed, such as OLTP applications. But for applications with big/fast data processing requirements, it makes both business and technical sense to have an integrated database system. Cassandra is a non-ACID, NoSQL key-value pair database that uses a sharding approach for horizontal scalability and is built on guiding principles that are opposite for those guiding the Oracle database. This session discusses how Apex Supply Chain Technologies built an integrated POC database system for a transformative supply chain cloud-based

Rao S Kasinadhuni, Database Technologist, Vice President, JPMorgan

C104: Data Warehousing with Hadoop and Integration with OBIEE 11g Leveraging Oracle Big Data Discovery (BDD) and Big Data SQL

Increasing interest in using Hadoop for data management, transformation, and analysis had led to significant development efforts by companies to enhance and extend Hadoop framework and offer a range of different Hadoop solutions. Many of these solutions can be used to enhance and extend the current BI and data warehousing environment. However, architecting such a system requires understanding of not just Hadoop but a broad range of related projects in the Hadoop ecosystem such as Flume, Hive, Impala, Oozie, Sqoop, etc. It also requires an understanding of the existing Enterprise Data Warehousing systems but more importantly how they integrate with various tools in the Hadoop ecosystem. The newest release of Oracle Business Intelligence 11.1.1.7 shows Oracle's continued efforts in trying to integrate its Oracle Business Intelligence Platform with big data technologies such as Hadoop and Hive.

Pravin Patil, Practice Director, BI/EPM, Apps Associates LLC

NETWORKING RECEPTION in the Data Solutions Showcase

H102: Hadoop and Your Data Warehouse

Elliott Cordo shares real-world insights across a range of topics, including the evolving best practices for building a data warehouse on Hadoop that also coexists with multiple processing frameworks and additional non-Hadoop storage platforms, the place for massively parallel-processing and relational databases in analytic architectures, and the ways in which the cloud offers the ability to quickly and cost-effectively establish a scalable platform for your Big Data warehouse.

Building a Real-World Data Warehouse

Elliott Cordo, CEO/Founder/Builder, Data Futures, LLC

Snowflake and Data Warehouses

Greg Rahn, Director of Product Management, Snowflake Computing

H103: Hadoop in the Cloud

To get your Big Data job done right, you need to use the right Big Data tools. How can you make sure you are leveraging the right tools? Learn from Ben Sgro about how Simulmedia, a pioneer in audience-based advertising on TV, is using a custom Python framework to programmatically create EMR clusters, move data to and from Amazon Simple Storage Service, and load data into its Redshift data warehouse. Xplenty’s Yaniv Mor talks about how using Hadoop in a coding-free, cloud-based environment ensures that businesses can benefit from Big Data without having to invest in hardware, software, or related personnel.

Python and EMR for MapReduce ETLs in the Cloud

Ben Sgro, Director of Data Engineering, Simulmedia

Offloading Data Integration/ETL to the Cloud (Using Hadoop)

Yaniv Mor, Founder & CEO, Xplenty

H104: Harnessing the Hadoop Ecosystem

Big Data is transforming how companies analyze information and enabling them to connect with customers in ways never possible before. Radius, which provides companies with a real-time marketing intelligence platform, is moving its core infrastructure from Hadoop to Spark. Hear Spotright’s Nathan Halko talk about his experiences moving from Hadoop to Spark. Qubole’s Jason Huang provides an overview of Apache Hive, the key differences between Hive and traditional data warehouses built on top of RDBMSs, and key techniques to increase performance and simplify Hive.

Moving From Hadoop to Spark: The Business Case

Nathan P Halko, Data Scientist, Spotright

Deep Dive Into Apache Hive

Jason Huang, Senior Solutions Architect, Qubole

NETWORKING RECEPTION in the Data Solutions Showcase