As modern application development and deployment has evolved to meet demands for flexibility and scalability, development teams are increasingly relying on containers and platforms based on Kubernetes such as OpenShift to help them ensure that these run reliably no matter the location or environment. When applications require an enhanced level of security, developers need the ability to incorporate proven cryptographic operations while rigorously protecting the underlying cryptographic keys.

Listen to this podcast and learn how to add robust cryptography to containerized applications that process sensitive data. This episode's speakers will describe how certified cryptographic capabilities including key generation, digital signing, and data encryption are enabled through the use of hardware security modules (HSMs) to enhance security and facilitate auditing and regulatory compliance. The podcast will introduce you to the industries and use cases where robust security is needed for containerized applications and explain how HSM technology helps developers take advantage of this in modern containerized architectures.

Iain Beveridge, Product Marketing & Solutions Manager
Oli Wade, Senior Sales Engineer EMEA

The average organization has data sprawled across multiple databases, making it nearly impossible to unlock the value of their data for analytics purposes. Between on-premises and cloud database deployments, moving all data into a single source of the truth is often a costly, insurmountable task. Luckily an open source project called Presto was built to solve this problem, and is now used by hundreds of companies ranging from LinkedIn, to Netflix, to Lyft.

Join Martin Traverso, co-CTO of Starburst and co-founder of the Presto open source community as he discusses how a “single point of access” can solve data sprawl challenges for organizations. Presto can query data within any database, and quickly return results that are valuable for business analytics projects. He’ll discuss the Presto open source community, the technology’s use cases, his open source leadership style, and more.

Whether they’re on-premises, in the cloud, open source, or proprietary, there are more databases to choose from than ever before. While choices are great, many enterprises face the challenge of data living across multiple database sources. According to Gartner, 23 percent of a data analyst’s time is spent finding and moving data -- which takes time away from the actual (and critical) analysis part.

Starburst was built to solve the problem of data sprawl by providing a single point of access to data within any database, leveraging the open source technology Presto. With Starburst, organizations can query data across multiple sources, and get fast answers that are applicable to a variety of business scenarios. Join Starburst’s CEO Justin Borgman as he talks about Presto’s applications in the modern enterprise – from everyday business analytics to large-scale AI projects.

Click here to learn more about Starburst on OpenShift.

Join Baruch Sadogursky, head of DevOps advocacy at JFrog as he analyzes real-world software update fails and how multiple DevOps patterns, that fit a variety of scenarios, could have saved the developers. Manually making sure that everything works before sending an update and expecting the user to do acceptance tests before they update is most definitely not on the list of such patterns.

Join us for some awesome and scary continuous update horror stories and some obvious (and some not so obvious) proven ideas for improvement and best practices you can start following tomorrow. 

Part 2: The world is moving to the cloud and distributed services, and there needs to be a relational database architected and built for Kubernetes that still delivers a SQL interface. The world needs Distributed SQL.  As a partner with a Red Hat Software Partner with a Certified Operator, Jim Walker, VP of Product Marketing at Cockroach Labs, will look into the use cases and real world issues solved by running a cloud native distributed database on OpenShift.

MLOps is a practice for collaboration and communication between data scientists and
operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. 
In this podcast, Red Hat will host expert data scientist and CEO of Technology Partner cnvrg.io, Yochay Ettun, to discuss core practices in MLOps that will help data science teams scale on OpenShift. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline.

PART 1: The world is moving to the cloud and distributed services, and there needs to be a relational database architected and built for Kubernetes that still delivers a SQL interface. The world needs Distributed SQL.  As a partner with a Red Hat Software Partner with a Certified Operator, Jim Walker, VP of Product Marketing at Cockroach Labs, will look into the use cases and real world issues solved by running a cloud native distributed database on OpenShift.

Many experts believe that the banking industry will change more in the next five years than it has in the last twenty-five years as a result of the rising influence of FinTechs, Open Banking requirements, and Real-Time Payment initiatives. In order to deal with these changes, banks will need to open their core banking systems with a scope and scale that have never before been contemplated. But one of the biggest roadblocks in doing so is the dependence of banks on legacy mainframe systems. Believe it or not, mainframe systems still run over 30 billion transactions per day globally and handle over 90% of all credit card transactions! In order to succeed going forward, banks will need a better way to integrate their core banking systems to the rest of the world than traditional methods. In this podcast we take a look at GT Software’s Ivory Service Architect to understand how it can help banks rise to both the challenges and the opportunities of the change taking place in their industry.

Lightbend's Karl Wehden will look at the typical decisions you will face as you mature your machine learning enhanced applications and start delivering at scale, on never ending streaming data. What trade-offs are you making when selecting a scoring strategy? What model metadata should you capture? How do you ensure model management and training is scalable and reliable? What should your serving and scoring infrastructure look like? These topics and more in a comprehensive view of how Lightbend and Red Hat are bringing repeatable, reliable, open source machine learning infrastructure to the market.

In the wake of the COVID-19 pandemic, our way of life is increasingly become digital, even faster and in entirely new ways than before.  As virtual and online become the new normal for professional and personal interactions and entire industries like healthcare, banking, and retail re-imagine their supply chains, what are the key technologies required to accelerate digital transformation and what are the risks that come with this?