Tuesday, May 22

Track A: Moving to a Modern Data Architecture


A101. Modern Data Architecture Design

Tuesday, May 22: 10:45 a.m. - 11:45 a.m.

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.

Designing a Data Architecture for Modern Business Intelligence & Analytics

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.


, Managing Partner, Athena IT Solutions

Polyglot Persistence Versus Multi-Model Databases

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?


, Director of Product Management, Intersystems


A102. Making the Shift From Relational to NoSQL

Tuesday, May 22: 12:00 p.m. - 12:45 p.m.

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.

NoSQL Concepts for the Relational DBA

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.


, Senior Systems Consultant, Quest Software


A103. IoT Data Strategies

Tuesday, May 22: 2:00 p.m. - 2:45 p.m.

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.

IoT Data: From the Edge to the Cloud and Back

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.


, Senior Director, Product Management, MapR Technologies


A104. Overcoming Big Data Integration Challenges

Tuesday, May 22: 3:15 p.m. - 4:00 p.m.

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.

Data Acquisition to Support Trading Data Analytics

This technical presentation shows how a global investment management firm, Wellington Management Co., 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.



, Principal, VP, Wellington Management Co.


A105. The Rise of Graphs & Enterprise Data Fabric

Tuesday, May 22: 4:15 p.m. - 5:00 p.m.

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 Rise of Graph Databases: From Thomson Reuters to Amazon Neptune

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.


, CTO, Cambridge Semantics

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