2021/06/02 - Japan
9:30 am to 1:00 pm JST
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Presenter: Ravi Shankar, SVP & CMO, Denodo
The kickoff to 2021 Fast Data Strategy Virtual Summit. Hear the theme, exciting topics, and expert presenters, and how you can interact with the speakers, panelists, and fellow attendees.
Presenter: Mark Smith, CEO & Chief Research Officer, Ventana Research
Organizations worldwide are adopting a variety of the public cloud service providers (i.e. AWS, Google, Microsoft) and each have a portfolio of storage, compute, network, and security options. All of which create significant challenges in managing a hybrid and multi-cloud enterprise architecture. Even worse is the impact to the governance and integration of data from the clouds and physical infrastructure to support the broad array of analytics and operational requirements.
Can one public cloud provider meet all your needs today and in the future? How do you manage across multiple public and private clouds you have today and where your data exists? And, how would you manage and operate your multi-cloud and on-premises systems to gain value from your data in any of them? The Chief Research Officer at Ventana Research, Mark Smith, will expound the challenges and path ahead for virtualization and integration of your data and the clouds, setting an architectural path for best success.
Many companies have been modernizing their technology infrastructure from legacy on-premises to modern cloud systems. But such a transition has not been easy - many companies had to re-architect their IT landscape to fit the new technology model, disrupting business. Data virtualization provides a layer of abstraction for the IT to transform their systems, while enabling the business users to continue their operations without disruption. In this session, the customer will discuss their successful cloud journey using data virtualization as the data abstraction layer.
Presenter: Adnan Masood, PhD., Chief AI Officer, UST
AI and ML help automate many of the enterprise tasks. What role do they play in cloud technologies? And, different cloud service providers (CSP) claim AI and ML capabilities within their technologies. But which one has better support for data science? Does any one CSP provide better tools and automation for data scientists to perform their analysis with ease and speed? The Chief AI Architect from UST will elaborate on the differences between cloud technologies for supporting AI, ML, and data science. Do you have additional questions that you want answered on this subject? Then bring them on.
Panelists: Mark Smith, CEO & Chief Research Officer, Ventana Research, and Adnan Masood, PhD., Chief AI Officer, UST
Moderated by: Ravi Shankar, SVP & CMO, Denodo
Each of the cloud service providers (CSP) is pushing their technologies as a one-stop-shop. Can one CSP satisfy all of your enterprise requirements? What do you do when you need multiple CSPs? How do you negotiate with different providers? What can you do to avoid vendor lock in? How can you mitigate or even eliminate egress charges? Watch the keynote speakers actively debate the different options and provide essential answers that will immensely benefit your cloud strategy.
Presenter: Pablo Alvarez, Global Director of Product Management, Denodo
Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.
Various cloud service providers (CSP) have different mechanisms for enabling security across their systems. If you are a multi-cloud customer, using different CSPs for different use cases, then orchestrating the security among the different clouds is not an easy feat. This presentation will feature a multi-cloud expert who will provide the best way to harmonize data security across multiple cloud platforms. Listen in and ask your questions too.
Data warehouses have been the workhorse of analytics for almost three decades. With the movement to the cloud, the data warehouses are making their journey to the cloud as well. But how can companies migrate their on-premises data warehouse to the cloud? It’s not that easy to package up the 30-year legacy and move them to the cloud. This news reporting style segment will provide in-depth coverage on how best to make this transition, with interviews from several experts. Tune in and bring your questions too.
One of the prime principles of data governance has been to locate the governance of the data closer to the source. As the sources change from on-premises to the cloud, how should data governance change? In this fireside chat, the moderator will interview the expert on the best practices for modernizing the enterprise data governance for the cloud. This interviewer will take live questions from the audience and get them answered by the expert.
Cloud Service Providers provide incentives to their customers to move all of their enterprise data into their cloud, but penalize them with egress charges when the data is moved out. When customers use multi-cloud, is there a better option to reduce the data movement among them, and lower or eliminate the egress charges? This panel of partner experts will provide their best answers. So, bring on your questions.
Gone are the days when companies used to have a single reporting tool. Now, de facto, many of them have multiple reporting tools - one in finance, another in sales, and a different one in marketing. This creates a headache for IT to create and manage the different semantic layers for each of them. Is there a way to democratize the LOBs to use their reporting tool of choice while making it easy for the IT to maintain those different tools with a universal semantic layer? This panel will hotly debate this topic about how best to accomplish it. Chime in with your questions.
Data catalogs are becoming prime within organizations to document the business definitions of all enterprise data enabling enterprise data governance, self-service data discovery, and watertight security. The avant garde data catalogs are using machine learning to learn user behavior and adapt accordingly. How do these machine learning data catalogs enable these outcomes? Bring your questions and walk away with insightful answers.