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: Are Containers Ready to Run NoSQL Databases?

Are Containers Ready to Run NoSQL Databases?

The database is the quintessential data dependency for any application. Databases in production environments tend to be performance sensitive and expect consistent and predictable performance from their underlying infrastructure. On the other hand, databases in dev/test environments need to be fast, agile and portable.

Due to this paradox, production databases are typically deployed on bare metal servers for maximum performance and predictability. This often leads to underutilization of hardware, idle capacity, and poor isolation. On the other hand, dev/test databases are deployed on VMs which are fast to deploy, improve hardware utilization and consolidation, are fully isolated, and are easy to move across data centers and clouds but suffer from poor performance, hypervisor overhead, and unpredictability. It is a challenge to run nosql databases with great performance, no hypervisor overhead and move data from dev, test to prod seamlessly without any downtime.

In this webinar, learn about:

  • How NoSQL databases like Cassandra can benefit from container technology
  • If the current storage systems can support containerized databases
  • How to alleviate data management challenges for large databases
  • How to run NoSQL databases on RCP
  • How Robin Hyper-Converged Kubernetes Platform can deliver bare-metal-like performance while retaining all virtualization benefits

Robin for NoSQL Databases

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

On-demand Webinar: Containerizing Oracle: Not Thinking About It Yet? You Should Be!

Containerizing Oracle

Robin Hyper-Converged Kubernetes Platform for Oracle

Still running your databases on underutilized bare metal servers? Looking to consolidate your databases and reduce license costs, but not create an OS sprawl? If the answers are YES, then watch this on-demand webinar to see how Linux container technologies like docker and LXC can help.

We explore how to use containers to consolidate databases without compromising performance, while guaranteeing isolation and no manageability changes. We examine and contrast other prevalent consolidation approaches, along with the roadblocks they present and how containerization helps you overcome those problems.

We show you how to:

  • Configure an Oracle database on LXC
  • Apply IO resource management on your Oracle database running in a container
  • Simplify database lifecycle management tasks with single click clone, time travel

Core Elements to Running an Oracle database using Docker

The core elements are:

  • Storage
  • QoS
  • Data Sharing
  • Availability

Storage: How do I configure storage for the Oracle database running in a Docker container?

In my opinion, this is probably the most important question when considering running an Oracle database using Docker. Running a database in a container actually provides the unique opportunity to decouple the compute from the storage.

QoS: How do I consolidate without worrying about noisy neighbors?

One of the key virtues of virtualization is the ability to share the system resources across many applications. While traditional VM based virtualization has seen some adoption in Dev and Test systems, the inability to ensure predictable performance has forced enterprises to use bare metal servers for production databases.

Data Sharing: How do I share data across environments?

I am often asked this or a slight variation of this question – “Will I be able to share data across Dev and Test environments if I run my application database using Docker?” The answer is Yes, if you plan and configure your storage correctly.

Availability: Can I ensure application availability across machine failures?

As I mentioned above, handling failover or relocation (read as managed failover) of the database instance by moving the container from one host to another is a very important requirement for running databases in general. Today a similar capability exists in RAC One but it requires a dedicated and often an idle machine for instance failover. Not to mention the extra license cost.

Oracle full-stack deployment on Robin Hyper-Converged Kubernetes Platform

On-demand Webinar: Containerizing Oracle: Not Thinking About It Yet? You Should Be!

On-demand Webinar: Containerizing your Existing Enterprise Applications

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Containerizing your Existing Enterprise Applications

Robin Hyper-Converged Kubernetes Platform for containerizing your existing enterprise Applications

Traditional enterprise applications are the lifeblood of any business. However, most companies are still struggling to deploy and operate these applications using the existing data center infrastructure and tooling. This puts enormous burden on application developers and IT administrators to manually deploy applications and its dependencies, manage data lifecycle, and deliver the desired quality of service, all while meeting business SLAs. Containers are lightweight, fast, and agile. They solve all dependency issues related to applications. Unfortunately, container technology such as Docker has seen adoption mostly amongst modern applications – stateless, cloud-native, mobile, etc. Why should your existing enterprise applications continue to suffer their current fate and not benefit from the advantages of containers?

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Robin Hyper-Converged Kubernetes Platform Resources