Hadoop has forever changed the economics and dynamics of large-scale computing, and its use among enterprises looking to augment traditional data warehouses continues to grow. Join this workshop to explore the basics of Hadoop, including the Hadoop Distributed File System, MapReduce, and the budding ecosystem of Hadoop software projects. Learn best practices for installing and configuring Hadoop in your environment, managing its performance, and developing Big Data applications. Please Note: This workshop is hands-on. Attendees are responsible for bringing their own laptop.

Built for large-scale data processing, Spark is on the rise as one of the most active open source projects in the marketplace today. By enabling organizations to perform in-memory analytics on big datasets, Spark offers substantial performance benefits, as well as versatility as a multi-use platform for data scientists. This workshop provides an overview of its key features, ecosystem, and architecture, as well as how to run Spark, connect data sources, and start building applications. Please Note: This workshop is hands-on. Attendees are responsible for bringing their own laptop.

Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends, may be the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This workshop dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications. Please Note: This workshop is hands-on. Attendees are responsible for bringing their own laptop.

From recommender systems to disease diagnosis, machine learning is revolutionizing the process of complex decision-making by enabling the analysis of bigger, more complex datasets and the delivery of faster, more accurate results. This workshop examines the statistical and algorithmic principles for developing scalable, real-world machine learning pipelines and applications. Please Note: This workshop is hands-on. Attendees are responsible for bringing their own laptop.

For more than a decade “data” has been at or near the top of the enterprise agenda. A robust ecosystem system has emerged around all aspects of data—collection, management, storage, exploitation and disposition. And yet, more than 66% of Global 2000 senior executives are dissatisfied with their data investments/capabilities. This is not a technology problem. This is not a technique problem. This is a people problem. Futurist Thornton May, in a highly interactive session, shares research results of his multi-institution examination of the human side of the data revolution.

"Data" is the new differentiator and companies who can successfully adapt their businesses based on insights gleaned from data will have a significant advantage. In this session, Rob Thomas will provide a brief overview of Machine Learning and the use case patterns that clients are using to disrupt their industry.

IoT represents the next wave of data on which companies are focused for new business opportunities as well as insights into their existing customers, products, and operations. What are the implications for the enterprise in terms of issues, such as data management, governance, and security?

There is plenty of buzz today about the Internet of Things. But what’s real and what’s hypothetical or just the marketing machine behind the technology’s potential? Radiant Advisors, DBTA, and Unisphere Research recently partnered on an IoT Market Survey to assess the state of IoT adoption and maturity. This presentation shares results of the survey and key insights, including overall market maturity, key drivers and expectations, perceived technology gaps, primary challenges and success factors, and the importance of privacy and security considerations.

IoT changes the data game. Again. The ability to create, collect, and exchange data with connected devices, sensors, actuators, and essentially anything in which a computing system can be embedded offers a new level of insight and intelligence into every industry. It also introduces a new level of risk and liability which companies cannot ignore. Anne Buff looks at the governance challenges IoT presents and suggests actions for you to take now to protect your business. Learn the key success factors in providing a governed IoT environment that enables discovery and innovation while protecting the business and its most valuable assets.

Efficiently collecting and securely storing large quantities of data is a challenging enough, but using it for greater insights and competitive advantage, even more so. Organizations need to know the tools and strategies that can help put Big Data to work.

We’ve all been promised the “Shangri-La” that is data lakes: More data means more insights—synergy! But has it really panned out? Data lakes are actually more similar to the early days of the Internet than they are a panacea for Big Data woes, filled with pristine, useful information. The data may have been reliable when first published but may not be now. Yet, as with many stale webpages, there is no way to tell, and the business continues to rely on wrong data to make decisions. Zillow faced the same problem and decided to change it. This session covers the tools the company built and the key tenets to help you ensure your lake rejuvenates your organization.

How do you drive the most value from your data? There is growing attention on the need to process and analyze data in motion. As your business looks to improve value generated from various data sources, the finding is likely that processing historical data is not enough nor is relying solely on real-time streaming solutions. Learn how continuous data integration can drive value for your business. Gain insights from HVR on how leading global organizations have implemented this approach and the impact on their business.

Having access to data that can be trusted is essential for effective decision making and ultimately for business success

Just because a DBMS supports ACID does not guarantee that your data will be correct. There are many other parameters and programming decisions that will impact your data, and, all too often, nobody is paying any attention to them. This session looks at the difference between ACID and BASE transaction models and considers other issues that can impact data integrity and accuracy.

In many organizations, distributed data architectures like Hadoop and in-memory databases like Redis have become the backbone of an increasingly important data analytics engine that informs critical business applications. But an analytics application is only as good as the data it depends on, and many organizations don’t have a good grasp on the health of their Hadoop environment until there’s a problem. Are you relying on open-source tools to monitor an enterprise-class analytics cluster? You’re certainly not alone. But, you might sleep a little better if you took a tip or two from this presentation.

Databases continue to be a central part of solutions architectures, but different systems offer different attributes. In this fast-paced Big Data world, understanding the array of data management choices is essential.

Big Data and Hadoop, in particular, have heightened the importance of making prudent database decisions in days and weeks where months and years were previously allotted. Couple this compressed decision window with an order of magnitude more choices, and even a consummate architect or database professional has got a serious challenge. Targeted at architects, administrators, and DBAs, this presentation covers a taxonomy of databases, unique advantages and disadvantages of each, usage, tidbits, positioning, strategy, and sundry items such as a “cheat sheet” to help architects navigate the maze to help make both strategic and tactical decisions with ninja-like precision.

With the proliferation of data, databases, and data types, it may seem that you're drowning in a sea of data. When hybrid is not just an idea, but a reality within your organization, you must not only survive, but also thrive, because your business depends on data. During this session, senior solutions architect, Russ Tuttle gives you practical advice on how to maintain productivity and reduce risks while implementing and utilizing more database platforms. He shows how to make your hybrid environment work for you by sharing best practices for proactive database management, how to simplify administration, and ways to minimize the learning curve for new and additional database platforms.

BI and analytics are at the top of corporate agendas. Competition is intense, and, more than ever, organizations require fast access to insights about their customers, markets, and internal operations to make better decisions—often, in real time.

Enterprises face challenges powering real-time business analytics and systems of engagement (SOEs). Analytic applications and SOEs need to be fast and consistent, but traditional database approaches, including RDBMS and first-generation NoSQL solutions, can be complex, a challenge to maintain, and costly. Companies should aim to simplify traditional systems and architectures while also reducing vendors. One way to do this is by embracing an emerging hybrid memory architecture, which removes an entire caching layer from your front-end application. This talk discusses real-world examples of implementing this pattern to improve application agility and reduce operational database spend.

Marc Andreesen’s famous pronouncement that software is eating the world continues to ring true. Cutting-edge approaches are helping organizations define success in ways that couldn’t be imagined just a few years ago.

In today’s analytics-driven world, the challenge for business success is not so much who can mine actionable nuggets of information from a mountain of data but who can get to it faster. To achieve this edge, Uber created an intelligent data infrastructure layer that actively monitors data usage patterns across its Hadoop cluster and recommends strategies for faster data processing. This presentation provides insights into Uber’s intelligent data infrastructure layer and how to achieve knowledge discovery at speed using simple data organization strategies, as well as the pitfalls to avoid while implementing them.

Data, big and small, surrounds us at every point and from every perspective. It can open new, powerful insights on virtually everything. What can it inform you and your business opportunities? This interactive program details the five key pillars of Big Data analytics—context, data, methods, technologies, and resources, along with their orchestration in technical architectures and formal data science processes to successfully uncover actionable answers found in multiple large datasets and data streams.

For both government agencies and businesses, unforeseen opportunities for innovation and differentiation as well as fresh approaches to problem-solving can emerge from massive quantities of information—when combined with the right Big Data technologies.

Big Data and analytics exist at the core of 21st-century discovery and decision making in an increasingly data-driven world. Learn how leaders in this space are leveraging Big Data assets to enable collaborative problem-solving, transform interactions across industry and government, and accelerate innovation and how Rhode Island is exploring new models for driving Big Data partnerships forward.

Attunity, the #1 independent provider of real-time data streaming, will share its experiences in enabling universal data ingest for Fortune 100 Companies’ large-scale data lakes in support of real-time analytics and IoT initiatives. Join us to examine case studies from some of the world’s largest automotive, manufacturing, food processing and retail corporations.

Big Data has major implications for areas such as marketing and CRM. Forward-thinking companies in a range of fields are using Big Data to engage more effectively with their customer base.

The majority of business content is locked in disparate silos of unstructured data across many types of systems, including Collaboration, ECM, CRM, ERP, PLM, and archiving systems. This talk by BA Insight covers the techniques we've used—applying NLP, MapReduce, and search—to extract content from these systems, add structure to it, correlate it, use it to derive deep insight, and publish the resulting insights and explorable data via enterprise-wide portals. We cover four specific real-world examples and leave attendees with some specific steps they can take as well as inspiration about what they can achieve.

Having the capacity to move data seamlessly between on-premise to the cloud often requires significant internal effort and resources. However, the ability to move and integrate data seamlessly across heterogeneous systems in real-time is becoming mission critical in today’s ever growing competitive environments. We invite you to see how you can mitigate this challenge. In this HVR quick how-to, we will show you how easy it can be to implement a download that will capture real-time changes from relational databases and efficiently stream them in real-time to multiple destinations in the cloud.

With the advent of Big Data, new approaches to data architecture are needed to ingest data faster, store it more effectively, and put it to use for better decision making.

Math and science play an important role in the various phases of data analysis, exploration, and mining. With the explosion of Big Data technologies and data science methodologies, data analytics impact both our business and daily lives. We have new forms of business intelligence. We use descriptive analytics, which are reactive, along with standard and ad hoc reporting capabilities. However, predictive and prescriptive analytics, being proactive, give us the ability to predict, forecast, simulate, and optimize what will be coming next. Technology makes data more consumable. Fast access to insight is priority number one. Insights, in turn, spark new and rapidly evolving analytic requests. The result is convergence to new tools and scientific methodologies to meet the needs of data engineers and data scientists.

Explore the challenges and benefits of leveraging the cloud for data analytics. Jeschonek discusses why on-demand cloud computing infrastructure and its near-infinite processing capabilities make cloud a viable option for data modeling and analytics. Learn the criteria for choosing the right cloud strategy for your projects, the key benefits anr use cases for cloud analytics, and how to deal with the data gravity issue when considering cloud analytics.

Using robust technologies to track and analyze a buyer’s journey, organizations are using Big Data to supercharge their marketing efforts.

Those in a marketing role trying to understand and reach customers may feel like the industry and technology environments are changing faster than your organization can adapt. Everything that was considered a best practice a decade ago is now obsolete, buried under an increasingly colossal pile of data. With data coming from your website, dozens of social media sites, mobile apps, CRM systems, and numerous other systems, it’s challenging to create a 360 degree view of your customers in context with their interactions with other parts of your business. The benefits of harnessing the power of this treasure trove of information are mouth-watering—proving the ROI of different campaigns, focusing marketing dollars on the segments having the highest lifetime values, and understanding what kind of an experience your customers have had with your brand before engaging with them, and executing an effective marketing analytics strategy—and can make a massive impact on your key business objectives. Learn how groundbreaking innovation in business intelligence can help you transform your highly complex data landscape into a deep, unified, 360-degree understanding of your customers.

Hadoop is here to stay, but so are a host of other approaches. To be effective, they must all work together in the enterprise.

During the last 10 years, Apache Hadoop has proven to be a popular platform among developers who require a technology that can power large, complex applications. For customers, partners, and application ISVs who write on top of Hadoop, there is still one huge issue that remains—interoperability. Steve Jones and John Mertic take a closer look at how Apache Hadoop can become more interoperable to accelerate Big Data implementations.

SQL has been with us for more than 40 years and Hadoop, about 10. Even though when Hadoop was born there was no SQL interface to it, it has become imperative that SQL on Hadoop solutions are brought to the market. This talk provides an overview of SQL on Hadoop, including low latency SQL on Hadoop for analytic workloads, and how SQL engines are innovating

Open source platforms and frameworks such as Apache Spark have paved the way for commodity-priced processing on a massive scale.

One of the most exciting use cases of Apache Spark is the development of Self Service and interactive Predictive Analytic platforms. We can now integrate model generation and prediction of machine learning with data visualization capabilities that are powered by distributed processing capabilities of Apache Spark. In this presentation we would explore this capability to see how you can 'see' your data in full color.

Real-time utilization of streaming data requires a modern architecture that can scale. Learn about the technologies that can help.

This presentation covers how to build a multiple location, event-driven architecture that uses streaming data to interconnect Docker-hosted microservices that allow implementation of scalable, redundant, and highly available services across multiple data centers. Using Docker containers and single-purpose microservices, this presentation demonstrates how these services are interconnected with event-driven streams and how this architecture can be deployed

Streaming Analytics has been around since before even big data was around. Proprietary streaming engines like Software AG Apama (2000) and IBM Streams (2003), and open source streaming like Yahoo S4 (2010) and Apache Storm (2011). For awhile, Big Data seemed to refer only to Hadoop (Paper in 2003 and development in 2006). But now, streaming is all the rage - whether you call it stream computing, streaming pipelines, streaming analytics or fast data, it means people care about analyzing data as it's created, not after it's been indexed and stored in some persistent repository. With so many choices - on prem, cloud, resource managers, virtualized machines, containers, and nearly 40 streaming offerings, it's hard to know where to begin. Come learn about the current landscape and some thoughts on where's it going to be in the future.

The concept of an enterprise data lake is enticing. Find what’s needed and the technologies available to help build a data lake for the enterprise.

An enterprise data lake typically requires substantial effort to ingest, process store, secure, and manage data from a variety of sources. Cask Data Application Platform (CDAP) is an open source solution, which offers a self-service user interface for creating data lakes and simplifies the building and managing of production data pipelines on Spark, Spark Streaming, MapReduce and Tigon. This talk discusses how to achieve broad, self-service access to Hadoop while maintaining the controls and monitors necessary within the enterprise.

A recent Unisphere Research survey on data management found that Apache Hadoop is gaining significant traction. About 40% of respondents now have a Hadoop installation.

Think Hadoop is not in your future? According to a recent survey, 97% of organizations working with Hadoop anticipate onboarding analytics and BI workloads to Hadoop. When this happens, the companies which have disregarded the Big Data opportunity may be left behind. The good news is that onboarding your business intelligence workloads to Hadoop is not as complicated as it used to be. If you understand some key concepts, the transition can be simpler and more successful—allowing you to recycle current skill sets while avoiding either a rip-and-replace of your technical stack or elimination of business analysts to hire data scientists.

Data breaches can have wide-reaching implications for a range of organizations spanning healthcare, financial services, government, and manufacturing— and more. Find out how the threat landscape has changed and what you need to do to defend your organization.

The relentless pace of IT innovation is unfortunately matched by the speed of innovation in the ranks of hackers, cyber thieves, and other bad actors in the digital world. This talk looks at some of the emerging cyber threats likely facing enterprises in the near future.

As hackers become more sophisticated, they are better able to capitalize on the gaps between enterprise SIEM systems to orchestrate an attack. Join this session to learn how companies are turning to streaming data integration, analytics, and pattern matching to provide an aggregated overview across siloed SIEMs and a deeper understanding into the flood of alerts to prevent cyberattacks. Several real-world case studies will be shared.

Managing data vulnerabilities both internally and externally to prevent data breaches from happening is a key concern in the enterprise today.

Exposure of private and sensitive data is making headlines. Affected organizations suffer severe costs and damage to their reputations. This presentation reviews ways to facilitate forensic analysis when data has been compromised and methods for prevention to ensure that vulnerability and loss do not happen (or happen again). Learn how to identify the “who, when, and where” of your private and sensitive data. It’s not just about ensuring data security; it’s about trust and traceability and the appropriate level of data governance. All data assets do not need to be managed to the same degree. Learn how you can focus on your critical data elements.

We all want devices to be smarter. The benefits of IoT across industries, such as healthcare, manufacturing, oil and gas, and others, are gaining greater appreciation, but the security implications of all these connected things are also becoming better understood.

How long can we continue to place trust in the everyday devices we rely on? In an age of growing connectedness for everything from manufacturing robots to toothbrushes, the Internet of Things has the potential to morph from a helpful productivity enhancer into a cover for malicious infiltration of your home and office. Learn how makers can build secure “things” as well as the security controls that operators can implement. Milan Patel presents a simple model for assessing threats to the IoT ecosystem relevant to your industry and products. Security practitioners can learn how to be effective early adopters without being victims of “things.”

With data increasingly seen as an important asset whose value is enhanced when it is made more widely accessible to internal users and outside partners, the risk of cyberattacks is increasing at the hands of bad actors as well as from unintentional lapses by trusted employees

This session presents the threat mechanisms that commonly result in data breaches and lays out the core strategies for improving the security of your databases, including encrypting data at rest and in transit, key management, user and service authentication, secure data sharing, protecting against injection attacks, and configuration changes. The specific challenges and tactics for implementing these strategies vary between relational databases and NoSQL. It looks at the tools, processes, and practices you can put in place regardless of where you are storing your data, including tools that check for vulnerabilities in the code that accesses your data and blocks attacks. It also surveys leading database products and identifies product-specific tools provided by leading database vendors.

The challenges to data security keep coming. Find out how to build security into your data architecture.

Big Data presents challenges and opportunities for a wide range of activities, enterprises, and individuals. From complex data architectures, cloud storage, data analysis, and overall database management to the legal implications, information governance underpins Big Data analytics. The innovative uses of Big Data are exciting, but questions about data retention policies, securing your data (both big and small), ransomware, and compliance with legal systems in the U.S. and other countries shouldn’t be ignored.

In this session, we review all the AWS database service offerings and share some of the benefits our customers are experiencing by allowing AWS to manage their databases.

Only a small fraction of global firms increase productivity year after year, according to the Organization of Economic Cooperation and Development (OECD). Creating and using unique stocks of data capital is one of the key tactics these firms use to widen their lead. Come learn how two new ideas – data trade and data liquidity – can help all companies, not just superstars, take advantage of the data revolution, and hear examples of firms already putting these ideas into practice.

The enterprise data warehouse is evolving. Cloud services, virtualization, the data lake, and other emerging technologies have a role to play. Find out how.

In addition to the ongoing blizzard of technological changes and related opportunities facing large organizations, including Big Data, NoSQL, IoT, and cloud, many leading financial services organizations are grappling with additional issues that are relevant to the financial services arena. This presentation discusses whether the logical data warehouse is an appropriate approach to capitalize on the recent advances and looks at additional data management strategies, such as blockchain and FIBO (Financial Industry Business Ontology).

Data Warehouse is no longer the single source of the truth. The traditional data warehouse has reached its limit. With the advent of big data, it is no longer feasible to store all of the data in the data warehouse because of cost, and in some cases like the unstructured data, it is no longer possible to store such data in the data warehouse. Data warehouse has evolved into bi-modal logical data warehouse architecture where it is now necessary to combine the derived, structured data from the data warehouse with unstructured data from Hadoop, Spark, NoSQL, and other enterprise systems. This session will showcase case studies of award-winning customers, whose logical data warehouse architectures demonstrate the future of the data warehouse.

With the growth of Big Data, there is renewed appreciation for data governance principles. Understanding data lineage and provenance and having policies and processes in place are key requirements for ensuring that data is trustworthy and protected.

People talk a lot about building enterprise “data lakes” or data hubs to knock down data silos and democratize data access to different types of users. These are abstract topics. Maybe it’s time to stop talking and see how this can be done in practice. Search Technologies is currently processing and searching data from a data lake for a large life sciences customer. Come see, step by step, how this is accomplished. Paul Nelson takes disparate data sources such as document files and data tables, indexes and searches them and shows attendees morph lines for content processing and indexing, as well as search and visualizations to provide business insight into this data lake. He does so using Cloudera CDH, including Cloudera Search and Hue.

As organizations face greater pressure to compete on analytics, the need for data quality becomes paramount. In order to realize the promise of Big Data for improved decision making, data stores must be trustworthy.

As an enterprise moves from historical business intelligence reporting to predictive analysis founded on Big Data, data quality becomes increasingly important for successful outcomes. By observation, using conventional BI tools, the consumer of the information can drill down through the report to the detail when the aggregate number does not “feel” right, but this will not be possible with Big Data due to the sheer volume and complexity of the data. This presentation looks at the elements of data quality required to make effective use of algorithm-based data analysis and predictive analytics. Using the Data Management Maturity Model from the CMMI Institute, the presentation reviews the capabilities of an effectual data quality program and considers the competencies required to support data analysis and predictive analytics with Big Data.

A recent survey by Unisphere Research of data executives and professionals found that a majority of respondents, 57%, had strong demand for real-time information within their organizations. With the ability to bring faster insights to users and applications, in-memory technologies hold the potential to bring analytics to new levels

You're an architect or a developer with years of experience creating amazing applications. And now you've just been assigned to a low-latency project! Because you're an expert, you're expected to master a new in-memory data technology and be productive from Day 1. This session covers what you need to know to be successful on your low-latency in-memory project. Attendees learn about the modern in-memory technology landscape; distributed data topologies; reliable, scalable, and durable in-memory data—the techniques for really big in-memory data. And, most important, no prior knowledge of in-memory data grids is required!

High-quality analytics requires high-quality data. To achieve business goals from traditional data management stacks, emerging technology environments, or a combination thereof, data quality is still a foundational requirement.

The Tab Group created a lifecycle curve for data quality. Many of its nodes have come to fruition. However, many of these nodes have coincided with similar points on an inverse curve that represents the lifecycle of data analytics. The simultaneous tracing of these symbiotic nodes on both curves provides opportunities and challenges toward accelerating maturation for data quality and analytics.

If you are a believer in the data-driven organization (or even just curious) and have ever wondered what could happen if you cleverly combined the power of data collection, indexing, text mining, search, and machine learning into a unified platform and applied it within the enterprise, this talk is for you! Come learn about the state of cognitive search and analytics technology and how it is enabling great companies across a wide swath of industries to amplify mission-critical expertise within their business in a surprisingly short amount of time.

With the growing dependence on data analytics, experts agree the realtime delivery of data insights can be invaluable to business agility.

In a world awash with data, stale spreadsheets and lagging queries cannot provide the rapid insights required to succeed in today’s data-driven marketplace. There is an entire layer of insights buried underneath these rudimentary graphs that could prove invaluable if users had access to them. Enter a new generation of analytics: Interactive. This presentation covers the value of interactive analytics and dives into the issues that analytics pose for today’s data-driven leaders.

We live in an on-demand world of Twitter, DVRs, Snapchat, and more. Consumers get what they want, when they want it. Why should analytics for organizations or customers be any different? Yet, “Big Data” and “Fast Data” are at odds with traditional data architectures, requiring a new approach to providing real-time insights and interactive analytics. Apache Hadoop, Spark, Kafka, Kudu, and others are modernizing the data platform. The final step is to provide real-time, self-service analytics and interactive visualization for end users to get the real-time insights they need to make more informed decisions and test new hypotheses.

Famously tagged as the sexiest job of the 21st century in the Harvard Business Review, the reality at many organizations is that data scientists, as well as analysts and other users, are struggling to find the information they need when they need it. New approaches are available to unlock the power of data science.

Savvy businesses are recognizing the value of taking an “Amazon-ified” approach to data. Mimicking the sort the one-stop-shop approach that users enjoy and are accustomed to with Amazon, a sophisticated data catalog that is tied to data governance offers businesses the ability to deliver new levels of efficiency for users to easily “shop” for the data they need, all from one central location. This can be done instantaneously for quicker business operations and decisions, without needing to go through a technical intermediary. This presentation covers how a data catalog can enable data citizens to uncover insights and drive innovation that solves critical business challenges.

Market leaders realize that to deliver the best customer experience and to have meaningful engagement they need to understand the client better. Deeper customer understanding requires blending data from all internal, external and third-party sources and leverage it to make informed decisions. This session will show how a modern data management platform brings data together from multiple sources and uses advanced analytics to deliver a complete Customer 360 Degree view with relevant insights and intelligent recommended actions. We will discuss how to customer-centric organizations are learning more about their customers, their relationships, and preferences to deliver personalization at scale.

As organizations seek to use data for competitive advantage, providing information when it is needed in the right context is a challenge. Creating a data-driven culture means embracing new approaches to deliver value for decision makers.

Self-service analytics has many benefits, but it’s also caused the data landscape within many companies to become like the Wild West. Data and analytics outcomes aren’t being shared and reused for the greater good; rather, users are starting every analytics project from scratch without the benefit of repeatable data modeling. This presentation explains how data socialization is revolutionizing self-service data preparation and the analytics experience. This transformative new capability integrates traditional self-service data preparation benefits with key attributes common to social media platforms. The powerful combination enables data scientists, business analysts, and even novice business users to search for, share, and reuse prepared, managed data to achieve true enterprise collaboration and agility.

Many IT departments are seeking ways to automate routine tasks and free up resources. Cloud represents a new way of approaching database management, potentially relieving enterprise data shops of many administrative burdens.

Cloud is everywhere, and the database administrator is caught in the middle. How do you know where to start and what to migrate first? How do you perform consolidation planning and ensure data security? This session builds on a full demonstration, along with tips and tricks to show how DBAs can secure their environments to allow a self-service option so they no longer are the constraints to projects.

BI and analytics are at the top of corporate agendas. Competition is intense, and, more than ever, organizations require fast access to insights about their customers, markets, and internal operations to make better decisions—often, in real time. Enterprises face challenges powering real-time business analytics and systems of engagement (SOEs). Analytic applications and SOEs need to be fast and consistent, but traditional database approaches, including RDBMS and first-generation NoSQL solutions, can be complex, a challenge to maintain, and costly. Companies should aim to simplify traditional systems and architectures while also reducing vendors. One way to do this is by embracing an emerging hybrid memory architecture, which removes an entire caching layer from your front-end application. This talk discusses real-world examples of implementing this pattern to improve application agility and reduce operational database spend.

Cloud solutions and services continue to be embraced for competitive advantage due to a variety of reasons. It is important to understand how the environment works today.

After 10 years of existence, the cloud industry would have you believe it is a mature, stable industry that is safe and secure to take your business to. Yet, the secret the insiders in the know is caveat emptor: Let the buyer beware. This presentation covers the state of the cloud today, lessons learned from the early adopters, the new role of cloud keeper, and what to know in advance to create a successful cloud deployment. It ends with a checklist you can use ensure you choose a cloud provider that can meet your needs.

Data virtualization is an enabling technology that can help an organization address the need for a comprehensive view of its data, avoid the need to replace physical systems, and allow the business and IT sides to work closer together in a more agile fashion.

The data as a service (DaaS) approach enables provisioning of both internal and external assets that can readily be used in the decision-making process. DaaS enables transformation and delivery of the raw data into meaningful data assets for multiple business initiatives in an organization. Attend this presentation to learn how Guardian Life has successfully created a data marketplace using data virtualization to deliver real-time trusted data assets. Learn how this approach reduces risks from data silos that contribute to wrong decisions and serves the right data with the right context at the right time, enabling monetization of data assets by serving necessary data to both internal and external stakeholders.

Virtualization and cloud can provide an approach to abstract data from its sources of origin so that all data environments are opened up to access, including RDBMSs, NoSQL database systems, storage systems, and newer Big Data systems such as Hadoop and Spark.

Cloud adoption is the No. 1 challenge enterprise customers face today. What will your journey look like? What path should you take? How do you get started? This session provides three practical journeys to the cloud that can solve real technical challenges organizations face today. Whether your challenge is delivering self-service BI, a new data lake, or data science analytics, these easy-to-learn tutorials are designed to provide you with hands-on experience with Oracle Cloud services.

Awareness of data security has increased at many organizations due to the number of recent high-profile breaches. Understanding how to ramp up security is critical in this era of Hadoop and Big Data.

No industry is immune to a security breach. A recent report claimed that the most common cyber incidents are actually data breaches, “dwarfing rates of all other cyber events.” For enterprises the world over, data security, both internal and external, is of paramount importance. However, IT departments are faced with a difficult challenge. They need to ensure that internal business users have access to the data they need to make decisions and do their jobs and they also have to enforce appropriate controls to prevent unauthorized users from viewing or modifying sensitive data.

With the rise of Big Data and NoSQL technologies, what are the implications for data protection and security?

The terms “SQL injection” and “cross-site scripting attack” are well known to the application security community. With NoSQL databases, pure SQL injection attacks are no longer possible, but different types of injection attacks, occurring at all levels of the application stack, more than make up for them. This session looks at several strategies an attacker might use to perform parameter and even command injection in applications running against a NoSQL database such as MongoDB. It then looks at various strategies for eliminating these vulnerabilities, which mainly involve input validation and sanitization. In this context, the Open Web Application Security Project (OWASP) is discussed, along with some of the work being done under the OWASP banner in this area. Finally, methodologies are discussed for testing your applications to verify that they are (relatively) hardened against these types of attacks.

The opportunities for cyberattacks, mistakes by trusted insiders, and fraud are broad—and constantly growing. Today, organizations must adopt a data-driven security stance.

When we think of fraud today, our minds go first to stolen credit card credentials, but fraudsters have developed new and innovative approaches to monetizing data about companies and individuals. Among these efforts has been synthetic small business fraud, in which a small business may appear on any given day with no records of incorporation, no recorded credit history among bureaus, and no employees to vouch for its legitimacy. XOR Data Exchange has formulated its Small Business Risk Exchange to help service providers determine both legitimacy and ability to pay for services, even with little or no recorded credit history or employees. And it’s all driven by data.

From private, to public, to hybrid, the adoption of all cloud types is growing, driven by the need for organizations to increase the efficiency and availability of their databases and applications, while reducing costs and complexity. However, security remains a contentious issue, especially in discussions around the public cloud. While some IT leaders do not trust moving any sensitive data off-premises, others believe that public cloud providers can provide better security than they can achieve on their own. The fact is it all depends on your requirements and how the public cloud provider can meet them. Join us for a special, no-holds-barred panel that explores the public cloud landscape, pitfalls to avoid and successful strategies for ensuring the protection of your data.

The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by a prominent consultant who has helped many global enterprises adopt Big Data, provides the inside scoop you need to supplement your data warehousing environment with data intelligence—the amalgamation of Big Data and business intelligence.

Everyone attending the closing keynote is entered into a drawing to win a pair of Beats headphones. One winner will be announced at the conclusion of the event and must be present to win.