Modern architecture has evolved beyond the traditional data warehouse to include logical data warehouses, data lakes, distribution hubs, data catalogs, analytical sandboxes, and data science hubs, along with both self-service data preparation and BI.

This session looks at the data architecture for modern business intelligence and analytics which must support structured, unstructured, and semi-structured sources and hybrid integration and data engineering as well as analytical uses by casual information consumers, power users, and data scientists. Technologies include databases (relational, columnar, in-memory, and NoSQL); hybrid data, application, and cloud integration; data preparation; data virtualization; descriptive, diagnostic, predictive, and prescriptive analytics; and on-premise and on-cloud deployments.

Relational, columnar, object, XML, and graph databases are flourishing, but many applications need all of these for different capabilities. Those who tout "polyglot persistence" insist that one size cannot fit all and focus on integrating multiple data stores. At the same time, the multi-model database is on the rise, and most leading operational DBMSs offer multiple data models. Fried considers the pros and cons, what is possible, what is best for performance, and what is practical. Should you use a multi-model database in your next project?

Enterprises today are competing on analytics, and this requires the right combination of technologies. Increasingly, that means a combination of data management systems spanning NoSQL and relational, cloud, and on-premise.

More and more, DBAs who have traditionally managed relational database systems such as Oracle and SQL Server are being tasked with managing companies’ "non-relational" databases. Platforms such as MongoDB and Cassandra are coming under the management of enterprise IT, requiring a new set of skills for their existing teams. Hall explores concepts familiar to relational DBAs, such as data modeling, high availability, and scalability, and discusses how those concepts translate into NoSQL platforms.

IoT environments often pose a data management problem because of the huge volumes of data that are created and the latencies inherent in having global distribution.

The challenges of aggregating data from consumer-oriented devices, such as wearable technologies and smart thermostats, are fairly well-understood. However, there are a new set of challenges for IoT devices that generate megabytes or gigabytes of data per second. Certainly, the infrastructure will have to change, as those volumes of data will likely overwhelm the available bandwidth for aggregating the data into a central repository. Ochandarena discusses a whole new way to think about your next-gen applications and how to address the challenges of building applications that harness all data types and sources.

Does the thought of capturing massive volumes of streaming sensor data from your hundreds or thousands of connected assets give you heartburn? Perhaps you are considering how to implement analytics at the edge to relieve your data warehouse. Maybe you are storing time-series data in your Hadoop or S3 data lake as you build a solid and strategic IoT business case. No matter where you are on the IoT analytics curve, the Vertica analytical database provides leading intelligent device manufacturers with the highest levels of query performance and massively scalable in-database Machine Learning and analytics to derive bottom-line business value.

With the rise of Big Data, there is the need to leverage a wider variety of data sources as quickly as possible for real-time decision making in mission-critical environments.

This technical presentation shows how a global investment management firm architected continuous data feeds using data integration technology so that it could enable real-time data analytics for best execution. It covers the cloud-based trading data analytics platform that leverages HVR as a key, real-time data ingestion tool.

Traditionally, the selection and user of a data integration platform presupposes the existence of a trusted, proven, and accepted data architecture, However, these platforms can be just as valuable developing data architectures as they are in operating them.

With the rapidly increasing amount of data being generated, organizations spanning a range of industries are undergoing a digital transformation to analyze and query large amounts of data at high speeds.

The recent announcements of Thomson Reuters and Amazon Neptune providing knowledge graphs to their customers validate the effectiveness of using graph-based information as opposed to traditional databases to derive insights and business value. Martin analyzes why Thomson Reuters and Amazon Neptune have turned to graph databases as disruptive and necessary technologies and explains how companies in data-intensive industries, such as financial services, healthcare, pharmaceutical, and oil and gas, can use graph-based technologies as a new marketing strategy to maintain existing customers and attract new ones.

We live in a data-driven world, where the data flowing through systems is extremely complex, constantly changing, large in volume, and highly connected. Many use cases such as fraud detection and prevention, customer 360, and IoT network management involve capturing and analyzing massive amounts of highly connected data to identify hidden relationships and patterns. By unlocking the values in those relationships, you gain contextual insights of your data.

Traditional relational database and other NoSQL systems are not suited for those types of use cases because the technologies are primarily focused on the entities as opposed to the relationships. This is where graph databases come in so handy. They make it easy to discover, explore, and make sense of complex relationships. By leveraging the insights in data relationships you can deliver more relevant, real-time experiences for your customers, proactively fight fraud, and ensure the health and seamless operations of your network.

Heath explores how DataStax Enterprise (DSE) Graph, coupled with Expero’s expertise in graph and analytics, empowers users to explore and visualize complex graph data in innovative and meaningful ways.