 Hello, I'm Jehilam Pandit and I'm a product marketing intern in the OpenShift product marketing team at Red Hat. Today, in the next few minutes, I will be talking about why you should deploy database and data analytics workloads on Red Hat OpenShift. Here is the agenda for today. We will start by providing an overview on the potential of databases and data analytics workloads and why deploy them on containers and Kubernetes. Next, we will talk about the challenges with operationalizing databases and data analytics workloads on containers and Kubernetes. Finally, we will provide an overview of why Red Hat OpenShift and how Red Hat OpenShift can help you accelerate cloud-native app development by deploying databases and data analytics workloads on it. Databases and data analytics are crucial to cloud-native development. They are an integral part of modern cloud-native applications. These workloads are being deployed by organizations globally for mission-critical applications, such as developing mobile apps, e-commerce apps for processing online transactions for data and business intelligence, and AIML. Containers and Kubernetes are in the critical part to succeed with cloud-native development. For successful and efficient cloud-native app dev, containers and Kubernetes are used to deploy all varieties of workloads. They give you the ability to scale in and out based on business requirements. They give you the agility to deploy workloads fast and respond quickly. They also make the workloads ready to be ported. Red Hat OpenShift is the container and Kubernetes platform of choice for databases and data analytics workloads. It automates and simplifies operations. Red Hat OpenShift enables a broad spectrum of databases and data analytics softwares to be automatically deployed and lifecycle managed, empowering the system administrators to focus on more strategic tasks that are important to the business. It gives consistency and portability across hybrid cloud. Secure and consistent deployment of databases, data analytics, and all workloads is crucial to cloud-native app development. OpenShift gives you this consistency and offers flexibility by providing a platform across the hybrid cloud, including Edge, as well as providing customizable development and deployment options. Red Hat also has partnerships and integrations with key database and data analytics ISVs. So what exactly are databases and data analytics? Databases are used to store important information, such as analytical, streaming, and transactional data electronically. There are two types of databases, relational, for example, Microsoft SQL Server 2019, and non-relational, for example, MongoDB, Couchbase, et cetera. Data analytics includes tools such as Apache Kafka, Red Hat Data Grid, Red Hat AMQ streams, and they're used to gather and analyze data from multiple sources to achieve valuable insights. It facilitates data ingestion, data storage, preparation and transformation of the data, and helps in analyzing and visualizing the data for business intelligence. We are seeing an emerging trend of databases and data analytics being deployed on containers and Kubernetes-based hybrid cloud solutions. So let's talk about benefits of the same. Continents and Kubernetes help accelerate cloud native app dev by adjourn deployment of databases and data analytics workloads. Based on our experience of helping organizations globally, these are the top benefits that you can achieve by deploying databases and data analytics on containers and Kubernetes. Due to the agility provided by containers and Kubernetes to your databases and data analytics workloads, you will be able to deploy, test, and manage databases and data analytics with speed. You will be able to serve your customers faster and better with agile deployment and faster response times. You will be able to make use of dynamic scaling of compute resources to meet the changing needs of databases and data analytics, and scale in and out as per changing needs. You will also be able to port your databases and data analytics wherever needed, whenever needed. Datadoc, which is a leading monitoring service for cloud-scale applications, providing monitoring of servers, databases, tools, and services, in its 2019 report, analyzed more than 1.5 billion containers run by thousands of Datadoc customers. According to this survey, databases such as Postgres are running in more than 30% of containers in orchestrated environments, closely followed by data analytics, data analytics software such as Elasticsearch and the MongoDB database at more than 20%. This shows that a significant number of workloads running on Kubernetes are databases and data analytics workloads. And there is also a growing trend of deploying databases and data analytics in containers and Kubernetes. For example, there is a 50% increase in the deployment trend of Postgres in Kubernetes from 2018 to 2019. Another leading company, Cystec, that provides a powerful way to observe system behavior, troubleshoot application performance and secure container platforms carried out a survey in 2019 that incorporates details from both SAS and on-prem Cystec users to provide a snapshot of enterprise usage across well over 2 million deployed containers. Database such as Redis, Postgres SQL, MongoDB and data analytics such as Apache Elasticsearch make up for nearly 50% of the workloads being run in containers. This goes to show that databases and data analytics are extremely popular workloads in containers and are being adopted by more and more businesses in recent times for deploying cloud native apps. Here are examples of business outcomes achieved by organizations across various industries because of databases and data analytics workloads that they have deployed in containers and Kubernetes. We can see how what a wide range of use cases and what a wide range of industries this is covering. We have healthcare which is using databases and data analytics workloads and containers and Kubernetes for patient record management and we have financial services which is using it for critical applications such as fraud detection, risk analysis and mobile banking. We are seeing an increasing trend in government and energy sectors wherein they are using this and deploying databases and data analytics workloads and containers in Kubernetes to develop mobile and web apps and forecast customer needs and do a lot of real-time analytics. We're also seeing the fact that there are a lot of companies in the manufacturing and the logistics domain that are using databases and data analytics workloads on containers and Kubernetes for asset tracking, inventory management, et cetera. This is a desired conceptual architecture for databases and data analytics on containers and Kubernetes. The first step in the databases and data analytics lifecycle is to ingest and aggregate the data from various sources. For example, sources like sensors and smart devices, social media streams, banking transactions and so on. ETL operations are carried out in various forms of the data. For example, the data could be stream data, batch data, et cetera, and then the data is ingested. This data can be stored in an operational database to serve as a transactional database for online transaction processing or it could be stored in a data lake for data analytics. The database could be a SQL or a no SQL database and the data lake could be an S3 object storage. The next step is to prepare, process and transform the data using tools such as Presto, Apache Spark and store the processed and organized data into the analytical database which could be again a SQL or a no SQL database or this clean and structured data could be stored in a data warehouse as well. The final step is to perform analytics on the data for business insights and data visualizations and tools like Apache Spark could be used for this. Organizations may also choose to do AIML as a next step after data analytics. For a cloud native architecture, a containers and Kubernetes powered hybrid cloud platform is required to run all of these software tools in a consistent way for all of these stages in the data lifecycle. The platform should have set service capabilities so that data engineers, software developers can consume the resources in a self-service way as needed with an integrated compute acceleration to ensure good performance and seamless experience. Now let's shift gears for a bit and talk about the implementation challenges that organizations face when they try to containerize databases and data analytics. There are a few implementation challenges that come with deploying databases and data analytics workloads on containers and Kubernetes. Data loss, failures, and downtime is a major concern with these, with containerizing databases and data analytics. So is the operational complexity and performance trade-off that might be involved? There could also be a concern of a lack of ice, we support endorsement or documentation. Lack of support, caps and skills and processes is also a potential concern. Now let's talk about why Red Hat OpenShift and how the broader portfolio can help accelerate your cloud-enabled environment by helping you deploy databases and data analytics as a part of your overall architecture. Before we talk about OpenShift specifically, here are the value propositions of Red Hat for operationalizing your databases and data analytics workloads with us. Red Hat has a proven track record of helping organizations globally operationalize these solutions on Red Hat OpenCloud based on OpenShift. We have a comprehensive portfolio that helps complete the requirements of the cloud-native app architecture. We have powerful partnerships and integrations with key ISVs in the ecosystem that will help you accelerate and simplify the deployment and lifecycle management of databases and data analytics and all workloads. Finally, Red Hat is the leader and the trusted enterprise developer and provider of open-source software-based hybrid cloud solution. Let's explain how we built OpenShift. At Red Hat, we use a 100% open-source development model to deliver enterprise products. A secret source is our ability to own dozens and hundreds of open-source projects in a production-ready, stable and secure enterprise products that we support over many years. We have been contributing to Kubernetes and many other projects that OpenShift is based on since day one. Red Hat is the leading enterprise developer of Kubernetes besides this project sponsor, which is Google. OpenShift is robust and ensures that there will be no data loss or downtime when operationalizing databases and data analytics workloads on it. This is why you should be deploying databases and data analytics workloads on OpenShift. With OpenShift, you gain access to automated operations with Kubernetes operators, consistency and portability across different clouds and across all parts of the app development lifecycle. And we also have deep partnerships and strategic integrations with database and data analytics ISVs to ensure support, access and the ease of analyzing and visualizing the data easily. Your data is valuable if it can be stored and it can be used with agility and analyzed for business insights. Power your data with Red Hat OpenShift. With OpenShift, you get automated operations because of Kubernetes operators. Operators automate a lot of day one to operations such as provisioning, backup, scaling, et cetera, which will help system administrators, database administrators, software developers to focus on more projects and strategic tasks. Operators make databases more accessible and supported. A variety of database and data analytics ISVs have made custom operators available on Red Hat OpenShift. Red Hat is actively working with database and data analytics ISVs to ensure that the operators are certified on OpenShift, are up to date, secure and have regular scans. OpenShift secure deployment, operations and portability in a consistent way across the hybrid cloud. It offers a unified consistent manner of deploying databases, data analytics and all workloads on the same platform across different phases of your app development life cycle, be it development, test or deployment. These deployment and all of these operations are secure and can be done in a consistent manner across the hybrid cloud because Red Hat OpenShift offers several options. The fully managed option, you have Red Hat OpenShift on Azure. OpenShift is supported on AWS, on IBM cloud, on Google Cloud and we have a self-managed option. All of these options are to accelerate deployments by based on whatever you choose based on your need. We have strategic partnerships and deep integrations with key ISVs and we also have Red Hat products in the data analytics ecosystem space. These integrations and partnerships ensure that customers can choose to report portfolio of databases in their Kubernetes operators to deploy their databases and data analytics workloads easily. We're also working with the ISVs to get them certified across various certification standards, which ensures availability of operators, security and support from both Red Hat and the ISVs. We have partnerships with key database ISVs such as Microsoft, MongoDB, CrunchyData, CouchPace, NuoDB and data analytics ISVs such as Microsoft, SAP, SAS, Cloudera, Starburst and all of their operators are available to help make our mutual customers successful. You can integrate your databases with data analytics products like Red Hat EMQ streams, data grid on OpenShift to not only store your databases but also fire up agile analytics. Red Hat Marketplace simplifies the process of acquiring and deploying the databases and data analytics softwares on OpenShift. Red Hat portfolio helps provide complementary products to support you throughout the databases and data analytics lifecycle in OpenShift. Red Hat Storage portfolio includes Red Hat OpenShift Container Storage and Red Hat Chef Storage that provides software defined storage capability for containers and helps address beta byte scale storage requirements. We also have the Red Hat Middleware portfolio which provides frameworks, programming languages and runtimes. All of this is definitely built on the very robust and very secure foundation for running database and data analytics workloads on Red Hat OpenShift which is Red Hat Enterprise Linux. Red Hat OpenShift is equipped to support you in your journey with databases and data analytics workloads. We have a broad set of ISV ecosystem and strategic integrations that will help you simplify and manage databases and data analytics. All of these tools can be run and are enabled by Red Hat OpenShift. It also supports all kinds of footprints such as AWS, Microsoft Azure, IBM Cloud, Google Cloud and Red Hat OpenStack platform. OpenShift Container Storage can also span across various clouds and footprints and all of this is built on the very strong foundation of Red Hat Enterprise Linux. So to summarize, databases and data analytics are critical to cloud native development. They are an integral part of modern cloud native applications and they're being deployed by organizations globally for a variety of use cases such as developing mobile apps, e-commerce apps, IoT apps, AIML, business intelligence, et cetera. And containers and Kubernetes are in the critical part to succeed with cloud native development. They give you agility to scale in and out based on business needs, agility to deploy workloads fast and respond quickly and they also make the workloads ready to be ported. Red Hat OpenShift is a container and Kubernetes platform of choice for databases and data analytics workloads. It automates and simplifies the operations by providing a broad spectrum of databases and data analytics operators to be automatically deployed and lifecycle managed which will empower the system administrators to focus on more strategic tasks. It gives consistency and portability across hybrid cloud. It offers flexibility by providing a platform across the hybrid cloud including Edge and provides consistency across public cloud, private cloud, on-prem and consistency throughout your app development lifecycle be it say development, test or deployment. Red Hat also has partnerships and integrations with key database and data analytics ISVs to help make our mutual customers successful. As next steps, we recommend giving us an opportunity to come back and do detailed requirements, discovery sessions to help us develop a solution strategy and execution roadmap for your specific goals. You can also learn more about our database and data analytics capabilities and see success stories from existing customers on our website. You can also watch webinars with our partners to discover a wide range of videos answering all your database and data analytics related questions. Please reach out to our sales team if you want to learn more or have any questions. Thank you so much for listening. I hope you have a good rest of the day.