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Fast Data Strategy Virtual Summit 2019

Cloud and Data Science Success with Agile Data Management

12+ sessions on accelerating your journey to cloud/ multi-cloud/ hybrid cloud and data science / AI / machine learning

AMERICAS
Wednesday, April 24, 2019
9 am PDT | 11 am CDT | 12 pm EDT
APAC
Wednesday, April 24, 2019
9 am SGT | 11 am AEST
EMEA
Thursday, April 25, 2019
8 am BST | 9 am CEST | 12:30 pm IST

Improve the success of your cloud and data science initiatives with insights from thought leaders and industry experts from leading companies like Autodesk, Radiant Advisors, SimplicityBI, and more. Learn the latest trends and best practices that will give you a leg up over your competition, and boost your career.


Architects, data scientists, and BI practitioners can't afford to miss this online conference on data virtualization!

Data abstraction icon

Using data abstraction to minimize business disruption when migrating to the cloud

Data model icon

Creating multiple virtual data models to discover data associations, lineage, and patterns

Data management icon

Learning how data virtualization propels you to next-generation data management


Why register?

  • Further enhance your career by enriching your cloud, big data, and analytics expertise through fast data strategy.
  • Learn about data virtualization and fast data strategy from leading industry experts in an interactive virtual environment.
  • Unlike in-person events, you have the flexibility to attend this event from virtually anywhere.

AGENDA


Data Warehouses and Data Lakes Move to the Cloud
Using data abstraction to minimize business disruption when migrating to the cloud

Today’s cloud migration strategies need to account for increased complexity of data governance and hybrid and multi-cloud architectures while reducing the inherent risks of disrupting users and applications during the migration. The core benefits of data virtualization technology provide the data abstraction required for decoupling users and applications from activities such as data migration and consolidation, while adding the semantics and governance necessary in modern data environments.

In this session, you will learn:

  • How data abstraction is critical to sustain users and applications during data migration
  • How data abstraction reduces complexity of hybrid cloud environments
  • How data abstraction accelerates cloud migrations to modern data platforms and data lakes
  • How data abstraction continuously optimizes data in the cloud

Autodesk is a global software corporation that develops software for the engineering, manufacturing, and media industries. The company decided to transform its revenue model from a perpetual licensing to subscription-based licensing model to propel growth. Autodesk designed a modern data architecture around logical data warehouse that heavily uses data virtualization to integrate new transaction sources, including big data systems like Spark, while retaining the old systems, which are actively used for reporting and complying with regulations.

In this session, you will learn:

  • How you can build a logical data warehouse using data virtualization
  • How to create a single, unified enterprise-wide access point for any data used within the company
  • How data virtualization enables easy migration of on-premises systems to the cloud

Most organizations are on a journey to becoming data driven companies, and business users have been adopting self-service analytics for a while now. The cloud offers many powerful data platforms that scale to the requirements of your business, but there is still a need to report on data stored on-premises, warranting the need for a powerful data integration/ virtualization platform on the cloud that has the flexibility to manage your hybrid data requirements.

In this session, you will learn:

  • How Tableau enables analytics for everyone
  • How the Tableau & Denodo platforms give you the flexibility to manage your cloud journey at your own pace
  • How data virtualization enables governed self-service analytics on all your data - on-premises or in the cloud

The experts field tough questions on the best approaches for companies to migrate their data warehouses and data lakes to the cloud.

  • What pitfalls to avoid while undertaking the cloud journey?
  • Should you go with a single cloud provider or multi-cloud? What criteria should you use?
  • How to set up data virtualization in a hybrid-cloud scenario?

Successful Data Science with Data Virtualization
Creating multiple virtual data models to discover data associations, lineage, and patterns

Efficiently integrating data from multiple data sources is the linchpin for successful data science projects. Data virtualization provides an environment to leverage business analysts’ domain knowledge and SQL skills and offload the data prep and integration work from data scientists. Further, the data virtualization environment also provides reusability of integrated data along with higher performance SQL data access.

In this session, you will learn:

  • How to leverage a business semantic layer for improved data science project
  • How data virtualization’s optimized SQL query engine is an advantage in data science
  • How data virtualization improves data reusability and freshness in data science

Data science requires vast amounts of data. And that too from multiple sources. Data virtualization integrates the data across these disparate sources and provides a unified view of the data to data science algorithms. With it, data scientists can deliver important answers to business questions that enable the business users to perform their functions efficiently.

In this session, you will learn:

  • The data science requirement at Prologis - cost optimization
  • How data virtualization can be used to support data science projects
  • Architectural setup with data virtualization feeding data science algorithms

A virtual layer can help the data scientist speed up some of the most tedious tasks, like data exploration and analysis. At the same time, it also integrates well with the data scientist ecosystem. There is no need to change tools and learn new languages. In this session we will see:

  • How the data catalog simplifies the search for useful data
  • How to use Denodo's SQL engine to combine, transform and analyze data from any source
  • How Denodo integrates with tools like Zeppelin and Spark to work with large data volumes

The experts debate the merits and demerits of using physical vs. virtual data models for data science.

In this session, you will learn:

  • When to use physical / virtual data models or a combination of both for data science?
  • Can automation of models help business users become citizen data scientists?
  • How will machine learning and AI influence data science?

Product and Solutions Sessions
Learning how data virtualization propels you to next-generation data management

According to a leading analyst firm, the total spend in data and analytics is expected to reach $104 billion in 2019! Companies are investing in data warehouse modernization and data lake projects for descriptive and advanced analytics; however, for the analysis to be holistic, today’s architects weave disparate data streams together, not only from these analytical sources, but also from operational, third party, and streaming data sources. Logical data warehouse is a modern architectural methodology that virtually combines all the data across the enterprise and makes it available to analytical and visualization tools that facilitate timely, insightful, and impactful decisions throughout the enterprise.

In this session, you will learn:

  • What is logical data warehouse and how to architect one
  • The benefits of logical data warehouse – speed with agility
  • The Insights Development Workbench – a framework in action

Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.

In this session, you will learn:

  • How data catalogs enable enterprise-wide data governance regimes
  • What key capability requirements should you expect in data catalogs
  • How data virtualization combines dynamic data catalogs with delivery

Hybrid cloud computing is slowing becoming the standard for businesses. The transition to hybrid can be challenging depending on the environment and the needs of the business. A successful move will involve using the right technology and seeking the right help. At the same time, Multi-cloud strategies are on the rise. More enterprise organizations than ever before are analyzing their current technology portfolio and defining a cloud strategy that encompasses multiple cloud platforms to suit specific app workloads, and move those workloads as they see fit.

In this session, you will learn:

  • Key challenges of migration to the cloud in a complex data landscape
  • How data virtualization can help build a data driven, multi-location cloud architecture for real time integration
  • How customers are taking advantage of data virtualization to save time and costs with limited resources

Featured Speakers

John O'Brien
Principal Advisor and CEO
Kurt Jackson
Platform Architect
Ryan Thompson
Director, Data Virtualization and Architecture
Nicolas Brisoux
Nicolas Brisoux
Director of Product Management
Nicolas Brisoux
Mano Vajpey
Founder and Data Virtualization Architect
SimplicityBI
Lorrin Ferdinand
Lorrin Ferdinand
Senior Consultant
Neudesic
Kevin Scheffel
Kevin Scheffel
Data Virtualization Consultant
Kadenza
Ravi Shankar
Ravi Shankar
SVP and CMO
Denodo
Pablo Alvarez
Pablo Alvarez
Director of Product Management
Denodo
Saptarshi Sengupta
Saptarshi Sengupta
Director of Product Marketing
Denodo
Ravi Shankar
Mitesh Shah
Senior Cloud Product Manager
Denodo

Sponsors

Birlasoft logo
Kadenza
Neudesic logo
Simplicity BI logo