Provision Oracle RAC Database as a Service with Robin Platform

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Oracle RAC Database as a Service – Provision with Robin Platform

See how easy it is for anybody to stand up an entirely new Oracle RAC environment including the grid infrastructure installation the ASM configuration and finally create the RAC database tool.

Log into the Robin Hyperconverged Kubernetes Platform console. Go straight to the application bundle screen. In this case, we just have a couple of simple bundles, one of which is our Oracle RAC bundle. So we’ll just simply click on that to provision Oracle RAC. On-click, we are immediately presented with the provisioning workflow associated with this application.

We will name our application. We’ll just call this Oracle RAC demo. Now we’ve got a couple of network interfaces we need to consider because for Oracle RAC both the public and private IP address ranges are available here. This is where we specify the public address because this is how the application will receive connection requests we’ve got the ability to specify the size of the cluster – both in terms of the number of nodes and the amount of compute and memory capacity.

This gives us the ability to shape the way in which the database will be laid out. In this case, we are going to change the default from flash to spinning disk because we, in this case, don’t have enough flash memory available for this particular deployment. We will then move down here to specify our private interconnect IP address and specify our single client address name for RAC. We’ll scroll on down to find a number of other environment variables which may be passed through robin for this deployment.

We’ve got the ability to define how we will declare ASM disk group redundancy – various credentials and then we have our placement rules where we can control how these resources will be deployed on the physical robin cluster. In this case, we need to be able to allow for multiple RAC instances on the same physical node in the cluster because we only have two nodes in our demo environment.

Simply click on provision application from that screen. This will kick off the deployment of our RAC environment. The provisioning process goes through a number of different phases beginning with the deployment of the V nodes or the actual virtual nodes or pods in the cluster running a variety of scripts to complete the configuration of the RAC environment itself from an Oracle perspective through the UI. After this, it is really just in a matter of minutes as we have our entirely fresh new RAC environment up and running.

View Provision Oracle RAC demo to learn more.

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Scale Out Oracle RAC Database as a Service with Robin Platform

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Oracle RAC Database as a Service – How to Scale

See how easy it is for anybody to stand up an entirely new Oracle RAC environment including the grid infrastructure installation the ASM configuration and finally create the RAC database tool.

You have seen how easy it is to deploy a fresh new Oracle RAC database environment. But what if we want to know how our workload might respond when adding a third node to the cluster? In other words, test the scalability of that particular workload when adding a third node.

So it’s really easy. We just click on “Scale Out” for the application and here we can define the number of nodes by which we want to extend this cluster. This is done simply by sliding across this bar but for this demonstration purpose, we need to add a single node. We can also explicitly
call out a hostname for the new node. We can go back and tweak some of the environment variables as input for this new operation but for this demo, we really don’t need to make any of these changes.

So let’s just close these out and just simply click on the “Scale Out” button to begin the process for extending our RAC cluster. Behind the scenes, Robin is making all the necessary calls to Oracle to affect the extension of the cluster – in very much the same way as you might through conventional means for any other installation ensuring that from an Oracle perspective things are all agreeable with the configuration. You can see the success of the operation in this window. We’ll close this window and now we are back on our application screen with the newly refreshed view to find that our third node has been added.

We can see the new IP addresses – the physical host on which the new container has been deployed. Let’s just jump back into the new container – rather do a similar verification to see that we have actually successfully reshaped RAC database environment with three nodes from two nodes. We now log into Oracle set our environment through SRB CTL. Let’s just do a status again of our Robin database so we can see that we’ve got our Robin three instance now, which has been added and it’s now running on our new V node.

In the new container in the Robin cluster, we can see the new vip is added and is up and running. The resources have been successfully configured across the new node and if we go back into SQL plus and log back into the database itself and do once again a query of gv$instance, we can see that we had the databases up and fully available across all three instances of the cluster. Okay, so we exit out of that. We’re back to the UI and so now what if we want to scale back in? So we need to shrink that cluster – testing is completed – so we need to shrink that cluster back to two nodes –

Watch the demo to understand how to scale back.

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Clone Oracle RAC Database as a Service with Robin Platform

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Clone Oracle RAC Database as a Service with Robin Platform

We have a database application that is up and running. Now let’s take a look at how easy it is to take snapshots of that application and then subsequently perform cloning operations.

Create Snapshot

Creating a snapshot is quite easy with Robin. We have the option to provide a name for the snapshot or just use the default – which is what we’ll do here. We can look at some of the operations behind the scenes that are going to occur with respect to freezing IO and quiescing the application to maintain consistency. We will then see the newly created snapshot.

From here we have the option for restoring back to that point in time or in this case we were going to perform a thin clone operation based on that snapshot. Here we want to name the clone. It’s essentially an entirely new application stack that will be stood up as part of this operation. So we need to give it a name just as we would give the original application when it was provisioned.

Therefore, we also need to specify both the public and the private IP addresses, because again, this is a RAC database application. We could tweak the capacity for this app and we’ll just leave that the same specify the private IP address and just simply launch the operation by clicking on the clone. This takes a few minutes.

We can again take a look at some of the operations that are occurring behind the scenes with respect to deploying the application. It’s relatively quick and at this point, we can close out this window.

View Oracle RAC Clone and the original application

Now we will be presented with the new application screen as it relates to this new clone cloned app with all the related information in terms of the new nodes that have been provisioned – IP addresses etc. So then if we go back and just click on the general application screen then we can get a summary. you can see the original application and the newly cloned deployment and the snapshot on which it was based.

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Robin Storage Video – Advanced Data Management for Kubernetes

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Protect app+data with replication, snapshots, backup & recovery, and enterprise-grade security and get Hybrid & Multi-cloud portability with Robin Storage today!

As part of digital transformation initiatives, organizations across the globe are increasingly adopting containers and Kubernetes has emerged as the leading orchestration platform.

However, running mission-critical enterprise workloads that are Stateful on Kubernetes is still complex and challenging. Stateful applications such as PostgreSQL, MySQL, MongoDB, Elastic Stack, Kafka, and MariaDB require advanced data management capabilities in order to Recover from system failures, collaborate effectively across DevOps teams, and deliver hybrid and multi-cloud flexibility.

Introducing Robin Storage, the cloud-native storage with advanced data management that enables Stateful workloads on Kubernetes.

Born out of the partnership between Google and that entails

  • Engineering to engineering collaboration to design standardized APIs for running data-centric workloads in Google Kubernetes Engine.
  • And Robin Storage, as the preferred storage for enterprise workloads in GKE.

Robin Storage is a CSI-compliant block storage solution with bare-metal performance and powerful data management capabilities which are exposed through standard APIs that seamlessly integrates with Kubernetes-native toolings such as Kubectl, Helm Charts and Operator framework.

It provides automated provisioning, point-in-time snapshots, backup and recovery, Enterprise-grade data security, application cloning, QoS guarantee, and multi-cloud migration for stateful applications on Kubernetes.

Robin Storage enables powerful hybrid cloud use cases such as cloning a snapshot and rehydrating in multiple Google Cloud Platform availability zones. Robin Storage also offers flexibility to leverage existing investments in storage infrastructure like DAS/NAS/SAN from leading vendors and also offers a single plane for advanced data management capabilities across hybrid cloud implementations.

Protect app+data with replication, snapshots, backup & recovery, and enterprise-grade security and get Hybrid & Multi-cloud portability with Robin Storage today!

Consolidate ELK clusters with Robin Hyperconverged Kubernetes Platform

Deliver Elastic as-a-Service with Kubernetes

Improve hardware utilization

Robin provides performance isolation and RBAC to consolidate multiple ELK workloads without compromising SLAs and QoS.

Get more out of your hardware

Consolidate multiple ELK workloads and ensuring data locality for Data Nodes for better performance reduces hardware footprint. Also, reduce your hardware cost by sharing the compute resources between clusters. If an ELK cluster runs the majority of its batch jobs during the night-time, it can borrow a resource from an adjacent ELK cluster with day-time peaks, and vice versa.

Elastic – Dynamic Scaling with Robin Hyperconverged Kubernetes Platform |Video Demo

Deliver Elastic as-a-Service with Kubernetes

Scale on-demand

No need to create IT tickets wait for days to scale-up Data Nodes by adding more memory, CPU, or Storage, or to scale-out by adding more Data Nodes.

Dynamic scaling to meet sudden demands

If a Data Node runs out of resources, end users can simply scale up by adding more CPU/RAM, no need for IT tickets. Adding more Data Nodes to existing ELK cluster is also a simple 1-click operation.

Elastic – Deploy ELK Clusters with Robin Hyperconverged Kubernetes Platform

Elastic – Deploy ELK Clusters with Robin Platform

Deliver ELK (Elastic, Kibana, Logstash) Stack-as-a-service

Turbocharge your DevOps productivity with Elastic Stack on Kubernetes. Improve the agility and efficiency of your Developers, Operation teams, and Data Scientists.

Self-service experience

Robin provides self-service provisioning and management capabilities to developers, operations teams, and data scientists, significantly improving their productivity.

Provision custom Elastic stacks in minutes

Robin has automated the end-to-end cluster provisioning process for the Elastic Stack, including custom stacks with different versions and combinations of Elasticsearch, Logstash, Kibana, Beats, and Kafka. The entire provisioning process takes only a few minutes.

Deliver Database-as-a-Service

Robin Platform in 2 Minutes

Robin Systems Unveils Hyper-converged Kubernetes Platform for Big Data, Databases and AI/ML Applications 

Using the unique hyper-converged Kubernetes technology, with built-in enterprise-grade container storage and flexible overlay networking, Robin eliminates these challenges and extends Kubernetes multi-cloud portability to big data, databases, and AI/ML.

Robin Explainer Video – Platform 2 Min Video

Robin Explainer Video – Hyper-Converged Kubernetes Platform

Robin solves the fundamental challenges of running big data and databases in Kubernetes and enables the deployment of an agile, and flexible infrastructure for your Enterprise Applications.

As the only purpose-built Kubernetes-based solution, Robin offers the entire application lifecycle management embedded natively into the compute, storage, and network infrastructure stack for any application anywhere on premises and on the public cloud.

Robin is the first implementation of hyper-converged Kubernetes in the market. Using Robin users can do self-service deployment of big dataNoSQL databasesRDBMS,  and AI/ML, share entire experiments among team members, quickly do what-if trials, scale resources including GPU and IOPs, and migrate as well as recreate entire application environments across data centers and clouds.

Robin offers a self-service app-store experience that simplifies deployment and lifecycle management with 1-click functions that shorten DevOps and IT tasks from hours and weeks to minutes. It makes applications truly agnostic of infrastructure choices and enables them to share resources and data with predictable performance, leading to significant cost savings.

Big Data, Artificial Intelligence & Machine Learning EcoCast – Partha Seetala, CTO

Robin is a Software Platform for Automating Deployment, Scaling and Life Cycle Management of Enterprise Applications on Kubernetes

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Big Data Ecocast – Partha Seetala, CTO

Big data and artificial intelligence/machine learning are technology trends for which we’re just scratching the surface of the long-term potential.  In such environments, storage isn’t just about capacity, but about how to use that data in the most expedient way possible. Today, as organizations consider the potential of these technologies, they’re struggling with determining how to store, manage, and protect this data. Moreover, they’re identifying key use cases for their burgeoning datasets. Increasingly. Organizations are collecting data to train artificial intelligence and machine learning models in order to bring these powerful capabilities into their operations to get ahead of the competition and to make the world a better place.

For example, PAIGE.AI, a spinout of Memorial Sloan Kettering Cancer Center (MSKCC) is using advanced technology to accelerate and optimize cancer research. The goal of PAIGE.AI is to develop and deliver a series of AI/ML modules that allow pathologists to improve the scalability of their work, enabling them to provide better care at lower cost. By analyzing petabytes of data from tens of thousands of digital slides of anonymized patient data, PAIGE.AI has developed deep learning algorithms based on convolutional and recurrent neural networks and generative models that are able to learn efficiently and help improve the accuracy and speed of cancer diagnosis.

This entire world brings with it new challenges and whole new terminology that has to be learned. You need to figure out the ups, the downs, the ins, and the outs of designing a big data architecture as well as help to identify and deploy the tools that will manage and consume this data.

In this Big Data, Artificial Intelligence & Machine Learning EcoCast you will learn about how big data, AI, and ML all come together and will be exposed to solutions that can help you rein in the madness while also harnessing their potential power.

On This Big Data EcoCast Event, You’ll Discover

  • Learn about the critical challenges imposed by big data needs
  • Identify the use cases that drive decisions around when to choose which architecture
  • Discover how AI & ML critically intersect with big data and what you need to do to keep that intersection from becoming the scene of an accident
  • Understand how you can leverage next-generation infrastructure to accelerate AI/ML model development

On-premise and Multi-Cloud support for AWS, Microsoft Azure, SAP HANA, MS-SQL, IBM DB2 & Packaged Enterprise Applications

Application-aware compute, network and storage layers decouple applications and infrastructure so that the applications can be easily moved, scaled, cloned and managed with 1-click lifecycle operations regardless of the infrastructure model (on-premise, cloud, hybrid-cloud, multi-cloud), which can technically be anywhere.

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