First Container Solution – Partha Seetala, CTO, Robin Systems | DataWorks Summit 2018 The CUBE Video

Robin Hyper-Converged Kubernetes Platform announced as the First and Only Container Solution certified to run Hortonworks Data Platform (HDP)

Robin Hyper-Converged Kubernetes Platform – the First Container Solution certified to run Hortonworks Data Platform (HDP)

Robin Hyper-Converged Kubernetes Platform – the First Container Solution certified to run Hortonworks Data Platform (HDP)

On day two at Dataworks Summit 2018, Rebecca Knight and Jame Kobielus spoke with Partha Seetala, Chief Technology Officer (CTO), Robin Systems at theCUBE to discuss the first container solution certified to run Hortonworks Data Platform (HDP).

Tell us about Robin Systems

Robin Systems, a venture-backed company is headquartered in San Jose in the Silicon Valley. The focus is in allowing applications, such as big data, databases, NoSQL, and AI ML, to run within the Kubernetes platform. What we have built (the first container solution certified to run HDP) is a product that converges storage, complex storage, networking, application workflow management, along with Kubernetes to create a one-click experience where users can get managed services kind of feel when they’re deploying these applications. They can also do one click lifecycle management on these apps. Our thesis has initially been to actually look at it from the applications down and then say, “Let the applications drive the underlying infrastructure to meet the user’s requirements.”, instead of looking at this problem from an infrastructure up into the application.

Is this the differentiating factor for Robin Systems?

Yes, it is because most of the folks out there today are looking at is as if it’s a component-based play, it’s like they want to bring storage to Kubernetes or networking to Kubernetes but the challenges are not really around storage and networking.

If you talk to the operations folk they say that “You know what? Those are underlying problems but my challenge is more along the lines of when my CIO says the initiative is to make my applications mobile. The management wants to go across to different Clouds. That’s my challenge.” The line of business user says “I want to get a managed source experience.” Yes, storage is the thing that you want to manage underneath, but I want to go and click and create my, let’s say, an Oracle database or distributions log.

In terms of the developer experience here, from the application down, give us a sense for how Robin Systems tooling your product in a certain way enables that degree of specification of the application logic that will then get containerized within?

Absolutely, like I said, we want applications to drive the infrastructure. What it means is that Robin is a software platform – the first container solution certified by Hortonworks to run HDP. We layer ourselves on top of the machines that we sit on – whether it is bare metal machines on premises, on VMs, or even an Azure, Google Cloud as well as AWs. Then we make the underlying compute, storage, network resources almost invisible. We treat it as a pool of resources. Now once you have this pool of resources, they can be attached to the applications that are being deployed as can (3:10) inside containers. I mean, it’s a software plane installed on machines. Once it’s installed, the experience now moves away from infrastructure into applications. You log in, you can see a portal, you have a lot of applications in that portal. We ship support for about 25 applications.

So are these templates that the developer can then customize to their specific requirements? Or no?

Yes. Absolutely, we ship reference templates for pretty much a wide variety of the most popular big data, NoSQL, database, AI ML applications today. But again, as I said, it’s a reference implementation. Typically, customers take the reference recommendation and they enhance it or they use that to onboard their custom apps, for example, or the apps that we don’t ship out of the box.

So it’s a very open, extensible platform – but the goal is that whatever the application might be, in fact, we keep saying that, if it runs somewhere else, it’s running on Robin. So the idea here is that you can bring any app or database, and with the flip of a switch, you can make it a 1-click deploy, 1-click manage, one-click mobile across Clouds.

You keep mentioning this one click and this idea of it being so easy, so convenient, so seamless. Is that what you say is the biggest concern of your customers? Are this ease and speed? Or what are some other things that are on their minds that you want to deliver?

So one click, of course, is a user experience part – but what is the real challenge? The real challenges are –  there are a wide variety of tools being used by enterprises today. Even the data analytic pipeline, there’s a lot across the data store, processor pipeline. Users don’t want to deal with setting it up and keeping it up and running. They don’t want the management but they want to get the job done. Now when you only get the job done, you really want to hide the underlying details of those platforms and the best way to convey that, the best way to give that experience is to make it a single click experience from the UI. So I keep calling it all one click because that is the experience that you get to hide the underlying complexity for these apps with the First Container Solution certified to run HDP.

Does your environment actually compile executable code based on that one click experience? Or where do the compilation and containerization actually happen in your distributed architecture?

Alright, so, I think the simplest to explain like this – You work on all the three big public clouds. Whether it is Azure, AWS or Google. Your entire application is containerized itself for deployment into these Clouds. So the idea here is let’s simplify it significantly. You have Kubernetes today, it can run anywhere, on premises, in the public Cloud and so on. Kubernetes is a great platform for orchestrating containers but it is largely inaccessible to a certain class of data-centric applications. Robin makes that possible.

But the Robin take is, just onboarding those applications on Kubernetes does not solve your CXO or your line of business user’s problems. You ought to manage the environment from an application point of view, not from a container management point of view. From an application point of view, management is a lot easier and that is where we create this one-click experience.

Give us a sense for how we’re here at DataWorks and it’s the Hortonworks show. Discuss with us your partnership with Hortonworks and you know, we’ve heard the announcement of HDP 3.0 and containerization support. Just give us a rough sense for how you align or partner with Hortonworks in this area.

Absolutely. It’s kind of interesting because Hortonworks is a data management platform, if you think about it from that point of view and when we engaged with them first – So some of our customers have been using the product, Hortonworks, on top of Robin, so orchestrating Hortonworks, making it a lot easier to use. One of the requirements was, “Are you certified with Hortonworks?” And the challenge that Hortonworks also had is they had never certified a container based deployment of Hortonworks before. They actually were very skeptical, you know, “You guys are saying all these things. Can you actually containerize and run Hortonworks?”

So we worked with Hortonworks and we are, I mean if you go to the Hortonworks website, you’ll see that we are the first in the entire industry who have been certified as a container based play that can actually deploy and manage Hortonworks. They have certified us by running a wide variety of tests, which they call the Q80 Test Suite, and when we got certified the only other players in the market that got that stamp of approval was Microsoft in Azure and EMC with Isilon.

So you’re in good company?

I think we are in great company.

Are you certified to work with HDP 3.0 or the prior version or both?

When we got certified we were still in the 2.X version of Hortonworks, HDP 3.0 is a more relatively newer version. But our plan is that we want to continue working with Hortonworks to get certified as they release the program and also help them because HDP 3.0 also has some container based orchestration and deployment. So we want to help them provide the underlying infrastructure so that it becomes easier for users to spin up more containers.

The higher level security and governance and all these things you’re describing, they have to be over the Kubernetes layer. Hortonworks supports it in their data plane services portfolio. Does Robin Systems solutions portfolio tap into any of that, or do you provide your own layer of sort of security and metadata management so forth?

We don’t want to take away the security model that the application itself provides because the user might have step it up so that they are doing governance, it’s not just logging in and auto control and things like this. Some governance is built into. We don’t want to change that. We want to keep the same experience and the same workflow hat customers have so we just integrate with whatever security that the application has. We, of course, provide security in terms of isolating these different apps that are running on the Robin platform where the security or the access into the application itself is left to the apps themselves. When I say apps, I’m talking about Hortonworks or any other databases.

Moving forward, as you think about ways you’re going to augment and enhance and alter the Robin platform, what are some of the biggest trends that are driving your decision making around that in the sense of, as we know that companies are living with this deluge of data, how are you helping them manage it better?

I think there are a few trends that we are closely watching. One is around Cloud mobility. CIOs want their applications along with their data to be available where their end users are. It’s almost like follow the sun model, where you might have generated the data in one Cloud and at a different time, different time zone, you’ll basically want to keep the app as well as the data moving. So we are following that very closely. How we can enable the mobility of data and apps a lot easier in that world.

The other one is around the general AI ML workflow. One of the challenges there, of course, you have great apps like TensorFlow or Theano or Caffe, these are very good AI ML toolkits but one of the challenges that people face, is they are buying this very expensive, let’s say NVIDIA DGX Box, this box costs about $150,000 each. How do you keep these boxes busy so that you’re getting a good return on investment? It will require you to better manage the resources offered with these boxes. We are also monitoring that space and we’re seeing that how can we take the Robin platform and how do you enable the better utilization of GPUs or the sharing of GPUs for running your AI ML kind of workload.

We’ll be discussing these trends at the next DataWorks Summit, I’m sure, at some other time in the future.

Learn more about Robin Hyper-Converged Kubernetes Platform – the First Container Solution Certified to run Hortonworks Data Platform (HDP) for big data, nosql databases and RDBMS applications.

Hortonworks Data Platform Optimized for Docker Containers – Get Started Today

Hortonworks Data Platform Optimized for Docker Containers – Get Started Today

Robin Hortonworks Webinar

HDP on Robin Hyper-Converged Kubernetes Platform

Although the Docker revolution has made containers mainstream, containerizing big data is challenging because many containerization platforms do not support stateful applications. With the first and only out-of-the-box container-based solution that is certified by Hortonworks to run HDP, Robin Systems helps to build an Application-Defined Infrastructure.

Containerizing big data brings to the table many benefits such as 1. Improved utilization and reduced licensing costs with shared hardware resources, 2. Decreased administration costs and reduced time-to-market for big data apps with simplified operations.

Join this Robin Hortonworks Webinar to learn about:

  1. App-store experience; 1-click deployment of HDP: Deploying HDP is now as easy as installing an app from the App Store. Robin enables self-service big data deployment with 1-click cluster provisioning to deploy complex distributed applications in minutes.
  2. Doesn’t get any simpler: Scaling up and scaling out is now as easy as adjusting the brightness on your phone – Use sliders to configure compute, network, and storage layers.
  3. Meet critical SLAs: See how Robin’s multi-tenant architecture enables IT teams to meet the most demanding SLAs and handle performance isolation between HDP services even in a shared infrastructure environment while letting development and scientific user teams enjoy the simplicity of an app-store experience.

Ali Bajwa, Principal Partner Solutions Engineer, Hortonworks

Ali Bajwa is a seasoned engineer with extensive experience architecting and developing complex Big Data, CRM, mobile software infrastructure projects. He has delivered customer success by leading architectural workshops and proof of concept engagements with key customers. He has broad development experience across Hadoop, Web, Mobile, and Desktop applications.

Ankur Desai, Director of Products, Robin Systems

Ankur Desai is a Director of Products at Robin Systems. He brings over 12 years of experience in software development, product management, and product marketing for enterprise software. Ankur holds an MBA from Dartmouth College, and a Bachelor of Engineering in Information Technology from University of Mumbai.

Robin Hortonworks Webinar – Hortonworks Data Platform Optimized for Docker Containers – Get Started Today

Infographic: Building Stateful Cloud Applications With Containers

Infographic: Building Stateful Cloud Applications With Containers

Tips From Top Thinkers

Building Stateful Cloud Applications With Containers

The continued expansion of the cloud, growing end-user application performance demands, and an explosion in database needs are all stacking up fast against enterprise IT teams. When it comes to building enterprise database and big data applications, many are finding that container technology solves for at least a few of these problems. Here are stats and tips from top thinkers on how to best use containers when building stateful cloud applications.

Persistent Storage is a Top Challenge

26% of IT professionals cited “persistent storage” as a top challenge, when it comes to leveraging containers.

Streamline Until It Hurts “Some of the best writers have said they refine their work by cutting till it hurts. Containers are the same way.” Eric Vanderburg Vice President, Cybersecurity | TCDI

Isolate Containers & Hosts “Maintaining isolation between the container and hosts system by separating the file systems is vital towards management of the stateful application.” Craig Brown, PhD Senior Big Data Architect & Data Science Consultant

Select an Intelligent Orchestrator “An intelligent orchestrator along with a softwaredefined storage with software-defined networking is very essential for running a cloud-based application.” Deba Chatterjee Senior Engineering Program Manager | Apple

A Majority of Enterprises are Investing in Containers

69% of IT pros reported their companies are investing in containers 69

Validate All States “What they all (containerized stateful apps) have in common is the requirement to reliably validate all possible states and state transitions when changes are made to the application.” Marc Hornbeek Principal Consultant – Dev Ops | Trace3

Ensure You Can Monitor All Containers “Containerised applications are addictive. They can be created, tested and deployed very quickly when compared to traditional VMs. The infrastructure to begin monitoring a potentially vast and varying number of new containers is essential.” Stephen Thair Co-Founder | DevOpsGuys

Ofset Workloads with Containers “Stateful applications often reside in 1 or 2 geographical locations and take heavy loads … and at diferent times during peak and of-peak periods. Understanding these variables will enable an operations team to determine how to best design the use of container applications.” Steve Brown Director, DevOps Solutions N.A. | Lenovo

Top Container Orchestrators Now More Popular Than DevOps Tools

When choosing a platform, 35% felt Docker was the best fit for them among all DevOps tools

Get Infrastructure Pros Excited “A lot of people focus too much on the fact that “those application guys” are coming to mess with our infrastructure, instead of thinking that maybe we can elevate our own jobs and start working more closely with applications.” Stephen Foskett Proprietor | Foskett Services

Follow Design Microservices Principles “One of the fundamental aspects of containers is moving to immutable application infrastructure, which means that you cannot store state and application in the same container.” JP Morgenthal CTO Application Ser

Don’t Use Containers for Data Storage “When dealing with stateful applications, precautions need to be taken to ensure that you are not compromising or losing data.” Sylvain Kalache Co-Founder | Holberton School

Looking for more advice on building your stateful cloud application with containers? Download our full eBook today for more exclusive advice from top cloud, DevOps, and container technology pioneers.

Forrester – Taking Enterprise Apps to the Cloud

Taking Enterprise Apps to the Cloud – Challenges and Benefits blended with containers from Robin Systems on Vimeo.

Robin for Big Data

Robin for Enterprise Applications Challenges

Robin Systems on Vimeo

Enterprise Applications Challenges

Taking Enterprise Applications to the Cloud – Challenges & Benefits Blended with Containers

Forrester - Robin Systems - Joint Webinar - Taking Enterprise Apps to the Cloud - Challenges & Benefits Blended with Containers

Join Forrester and Robin Systems

Enterprise Applications Challenges – While the Cloud seems great for saving on CAPEX (subscription vs hardware) and optimizing on OPEX (greater agility, flexibility), you – like many others — might find out it is not necessarily always as easy as it sounds. Users say that the ease of spinning resources on the cloud does not help with server/resource sprawl, but rather it makes it harder to track and manage.

Looking at numerous cloud use patterns, it is clear that stateless web-scale/facing apps are best suited for the cloud – when a VM goes down, simply bring a new one instead. When it comes to onboarding stateful distributed or clustered applications, cloud resources on demand is not really a sufficient solution and significant planning and architecture adaptations are required.

Modern Enterprise apps often characterized by a data heavy/centric nature, relying on Big-Data pipelines or NoSQL databases, have architecture implications that are not easily solvable on the cloud.  

Learn what others are attempting and some already doing to make cloud work given these challenges. The session will include a discussion of the latest trends and best practices as well as guiding points to consider.

Dave Bartoletti is a Principal Analyst at Forrester Research.   Dave has developed, delivered, supported, and marketed game-changing technologies for more than 25 years as a software executive at several high-profile technology and financial services leaders. He was at the forefront of the middleware, web, virtualization, automation, and cloud computing tech disruptions as both vendor and consumer.

Razi Sharir, VP of Products and Marketing at Robin Systems.  Razi is a veteran product management executive and joined Robin from CA Technologies, where he lead the SaaS Center of Excellence and Product Management for the team that developed a container-based Enterprise PaaS geared for modern application economy.

Robin for Big Data

Robin for Enterprise Applications Challenges

Robin Systems on Vimeo

How I Stopped Worrying & Learned to Love Data – Hortonworks (HDP)

How I stopped worrying and started to love the data – Meeting seasonal data peaks

Hortonworks (HDP) and Robin Systems Webinar

Deploying, right-sizing, ability to meet seasonal peaks without disrupting availability or supporting multiple clusters on a shared platform are often seen as the most difficult challenges in a Hadoop deployment.

In this webinar, we discuss these operational complexities that are often associated with Hadoop deployments and how they adversely impact the business. We will then look at the Robin Hyper-Converged Kubernetes Platform and see how it can help address these challenges.

Eric Thorsen is VP, Industry Solution at Hortonworks, with a specialty in Retail and Consumer Products

Eric holds over 25 years of technology expertise. Prior to joining Hortonworks, Eric was a VP with SAP, managing strategic customers in Retail and CP industries. Focusing on business value and impact of technology on business imperatives, Eric has counseled grocers, e-commerce, durables and hardline manufacturers, as well as fashion and specialty retailers.

Eric’s focus on open source big data provides strategic direction for revenue and margin gain, greater consumer loyalty, and cost-takeout opportunities.

Deba Chatterjee, Director of Products at Robin Systems, has worked on data intensive applications a for more than 15 years. In his previous position as Senior Principle Product Manager, Oracle Multi-Tenant, Deba worked on delivering mission critical solutions for one of the biggest enterprise databases.

At Robin Systems, Deba has contributed his significant experience to building the Robin Hyper-Converged Kubernetes Platform that delivers Bare Metal performance and application level Quality of Service across all applications to help companies meet peak workloads while maximizing off-peak utilization.

Meeting seasonal data peaks

Today, organizations are struggling to cobble together different open source software to manage Big Data environments such as Hadoop or build an effective data pipeline that can withstand the volume as well as the speed of data ingestion and analysis.

The applications used within the big data pipeline differ from case to case and almost always present multiple challenges. Organizations are looking to do the following:

  • Harness data from multiple sources to ingest, clean, & transform Big Data
  • Achieve agile provisioning of applications & clusters
  • Scale elastically for seasonal spikes and growth
  • Simplify Application & Big Data lifecycle management
  • Manage all processes with lower OPEX costs
  • Share data among Dev, Test, Prod environments easily

Robin Solution – Simple Big Data application & Pipeline Management

Robin Hyper-Converged Kubernetes Platform provides a complete out-of-the-box solution for hosting Big Data environments such as Hadoop in your big data pipeline on a shared platform, created out of your existing hardware – proprietary/commodity, or cloud components.

Robin container-based Robin Hyper-Converged Kubernetes Platform helps you manage Big Data and build an elastic, agile, & high-performance Big Data pipeline rapidly.

  • Deploy on bare metal or on virtual machines
  • Rapidly deploy multiple instances of data-driven applications
  • No need to make additional copies of data

Robin for Big Data

Robin Sytems on Vimeo

On-demand Webinar Build an Agile and Elastic Big Data Pipeline

Build an Agile and Elastic Big Data Pipeline

In this live webinar, we will show you how to:

  • Build an agile and elastic data pipeline
  • Deploy, scale, and manage the most complex big data applications with just a single click of a button
  • Deal with variety, velocity, and volume of enterprise Big Data

Build an Agile and Elastic Big Data Pipeline with Robin Hyper-Converged Kubernetes Platform

Big Data Pipeline – To stay competitive in today’s global economy, organizations need to harness data from multiple sources, extract information and then make real-time decisions. Depending on the industry and the organization, Big Data encompasses information from multiple internal and external sources. Capturing, integrating and preparing this data for downstream analysis is often time-consuming and presents a big challenge.

Today, organizations are struggling to cobble together different open source software to build an effective data pipeline that can withstand the volume as well as the speed of data ingestion and analysis. Robin relieves customers from the pains of building and maintaining a data pipeline and helps enterprises to make the most of Big Data.

In this webinar, we will focus on how Robin’s containerization platform can be used to:

  • Build an agile and elastic data pipeline
  • Deploy, scale, and manage the most complex big data applications with just a single click of a button
  • Deal with variety, velocity, and volume of enterprise Big Data

Build an Agile and Elastic Big Data Pipleline – pdf

Robin for Big Data

Robin Videos

Agile Provisioning

  • Simplify cluster deployment using application-aware fabric controller—provision an entire operational data pipeline within minutes
  • Deploy container-based “virtual clusters” running across commodity servers
  • Automate tasks – create, schedule and operate virtual application clusters
  • Scale-up or scale-out instantaneously to meet application performance demands

Robin’s application-aware manager simplifies deployment and lifecycle management using container-based “virtual clusters.” Each cluster node is deployed within a container. The collection of containers running across servers makes the “virtual cluster.” This allows Robin to automate all tasks pertaining to the creation, scheduling, and operation of these virtual application clusters, to the extent that an entire data pipeline can be provisioned or cloned with a single click and minimal upfront planning or configuration.

It is necessary to scale up or out as demand for resources spikes and then comes back to normal. Robin enables you to scale up with a single click by allocating more resources to the application. Robin enables you to scale out easily when you need to add nodes and helps you clone parts of your data when you need give data to developers and analysts for analytics, test upgrades, testing changes or for integration testing.

Emerging approaches to building stateful applications using containers

Self-service deployment of a Cloudera cluster on the Robin platform demo video

Self-service deployment of a Cloudera cluster on the Robin platform

Robin Systems Videos

In this demo video, we demonstrate how you can setup a Cloudera cluster with a click of a button on the Robin Platform.

Robin’s application-aware manager simplifies deployment and lifecycle management using container-based “virtual clusters.” Each cluster node is deployed within a container. The collection of containers running across servers makes the “virtual cluster.” This allows Robin to automate all tasks pertaining to the creation, scheduling, and operation of these virtual application clusters, to the extent that an entire data pipeline can be provisioned or cloned with a single click and minimal upfront planning or configuration.

Robin Platform has three components

Robin Application-aware compute

Robin platform aggregates the existing compute – proprietary or commodity servers – and creates a single layer of all compute resources that are available to each application that the enterprise uses.

Container Technology

Robin leverages container technology to consolidate applications with complete runtime isolation. Container is a lightweight, OS-based virtualization technology that allows creation of compartmentalized and isolated application environment on top of the standard OS.

Performance-Sensitive Workloads

Robin is the first and only product in the industry that brings application lifecycle management benefits to all types of enterprise applications – including highly performance-sensitive workloads such as NoSQL databases RDBMS and Big Data.

Appropriate Container Configuration

Robin’s Adaptive container technology picks the appropriate container configuration depending on the application types. Traditional applications are deployed within “system containers” to provide VM-like semantics and Robin supports the deployment of stateless microservices applications such as Docker containers.

Zero Performance Impact

When used with bare-metal servers, Robin enables “zero-performance-impact” consolidation of data-havy databases, and other distributed applications such as Elasticsearch, with Application lifecycle management features, resulting in significant operational efficiency gains and cost reduction.

Robin Application-aware storage

Robin Application-aware manager

Agile Provisioning

  • Simplify cluster deployment using application-aware fabric controller—provision an entire operational data pipeline within minutes
  • Deploy container-based “virtual clusters” running across commodity servers
  • Automate tasks – create, schedule and operate virtual application clusters
  • Scale-up or scale-out instantaneously to meet application performance demands

Self-service deployment of a Cloudera cluster on the Robin platform

Robin Systems Videos

Provisioning, scaling, cloning and time travel an ELK cluster demo video

Provisioning, scaling, cloning and time travel an ELK cluster

Robin Systems Videos

In this demo video we show 3 very important lifecycle management operations with Robin Hyper-Converged Kubernetes Platform on a ELK (Elasticsearch, Logstash, Kibana) cluster. Provisioning followed by scaling and then cloning and time travel.

Provisioning, scaling, cloning and time travel an ELK cluster

Robin Systems Videos

The Open Source Elastic Stack

Reliably and securely take data from any source, in any format, and
search, analyze, and visualize it in real time.

ELK Cluster – Elasticsearch, Kibana, Logstash

Elasticsearch

Elasticsearch is a distributed, JSON-based search and analytics engine designed for horizontal scalability, maximum reliability, and easy management.

The Heart of the Elastic Stack

Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected.

SPEED

Elasticsearch Is Fast.
Really, Really Fast.

When you get answers instantly, your relationship with your data changes. You can afford to iterate and cover more ground.

Being this fast isn’t easy. We’ve implemented inverted indices with finite state transducers for full-text querying, BKD trees for storing numeric and geo data, and a column store for analytics.

And since everything is indexed, you’re never left with index envy. You can leverage and access all of your data at ludicrously awesome speeds.

Kibana

Kibana gives shape to your data and is the extensible user interface for configuring and managing all aspects of the Elastic Stack.

Your Window into
the Elastic Stack

Kibana lets you visualize your Elasticsearch data and navigate the Elastic Stack, so you can do anything from learning why you’re getting paged at 2:00 a.m. to understanding the impact rain might have on your quarterly numbers.

Logstash

Visualize your data. Navigate the Elastic Stack.

Logstash –

Ingest any data, from any source, in any format.

Logstash is a dynamic data collection pipeline with an extensible plugin ecosystem and strong Elasticsearch synergy.

Centralize, Transform & Stash Your Data

Logstash is an open source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously, transforms it, and then sends it to your favorite “stash.”

QUERY

Be Curious. Ask Your Data Questions of All Kinds.

Elasticsearch lets you perform and combine many types of searches — structured, unstructured, geo, metric — any way you want. Start simple with one question and see where it takes you.

ANALYZE

Step Back and Understand the Bigger Picture.

It’s one thing to find the 10 best documents to match your query. But how do you make sense of, say, a billion log lines? Elasticsearch aggregations let you zoom out to explore trends and patterns in your data.

Share data across two Cloudera clusters

Share data across two cloudera clusters

Robin Systems Videos

In this demo, we will demonstrate how we can share data across two Cloudera clusters with Robin Hyper-Converged Kubernetes Platform

Agile Provisioning

  • Simplify cluster deployment using application-aware manger—provision an entire operational data pipeline within minutes
  • Deploy container-based “virtual clusters” running across commodity servers
  • Automate tasks – create, schedule and operate virtual application clusters
  • Scale-up or scale-out instantaneously to meet application performance demands

Share data – Robin eliminates cluster sprawl by deploying a data pipeline on shared hardware. This also results in better hardware utilization. The key to successful multi-tenancy is the ability to provide performance isolation and dynamic performance controls. The Robin application-aware manager equips each virtual cluster with dynamic QoS controls for every resource that it depends on – CPU, memory, network, and storage. This creates a truly elastic infrastructure that delivers CPU, memory, network and storage resources – both capacity and performance – to an application exactly at the instant it is needed.

Cluster Consolidation and QoS

  • Eliminate cluster sprawl with data pipeline components on the same shared hardware
  • Enable multi-tenancy with performance isolation and dynamic performance controls
  • Leverage dynamic QoS controls for every resource – CPU, memory, network and storage

Robin provides out of the box support for application time travel. Cluster level distributed snapshots at pre-defined intervals can be really useful to restore the entire pipeline or parts of it if anything goes wrong. Robin recommends admins to take snapshots before making any major changes. Whether you are upgrading the software version or making a configuration change make sure to have a snapshot. If anything goes wrong the entire cluster can be restored to the last known snapshot in matter of minutes.

Application Time Travel

  • Take unlimited cluster snapshots
  • Restore or refresh a cluster to any point-in-time using snapshots

Robin for Big Data

Setting up Hadoop cluster in the cloud

Robin Videos