First Class Support for Docker: Better Service, More Legroom

First Class support for Docker

Thanks to Richard Moross for the base image CC BY 2.0

“Docker, please visit the front desk to receive your complimentary upgrade to first class seating.“

That’s right, Docker just received a first class upgrade on Iron.io. A ways back, Travis (our digital frontiers-man of a CTO) announced beta support for Docker. Today, we’re ripping off the beta tag. Docker is our preferred way to package code.

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Message Queues & Workers: the Heart of Modern Infrastructure

Message Queues + Workers

Thanks to Sonny Abesamis for the base image! CC BY 2.0

Increasingly, message queues and workers are intertwined with the language of modern infrastructure. You might rely on explicit solutions like IronMQ or IronWorker. You might not. Whether you do or don’t is irrelevant: MQs and workers are in everything these days.

MQs and workers are hidden heroes, quietly powering a lot of the technology that many of us rely on. They’re core components in programming languages, MVC frameworks, and even web servers.

As a result, when making infrastructure decisions a good understanding of both MQs and workers is essential. The white paper below will take you from a fuzzy understanding to a well-versed conceptualization for both MQs and workers.

Download the latest white paper from Iron.io: a Refresher on Message Queues & Workers.
Give it a read and let us know what you think!

E is for Event: A Fresh Take on ETL

ETL

As a follow up to my previous post, The Workloads of the Internet of Things, I wanted to walk through a real world example that fully captures the principles of event-driven computing put forth.

Let’s set the stage first… imagine we operate a windmill farm and want to continually track optimal weather conditions to maximize energy output. What basic steps need to be taken?

  1. Sensors capture surrounding weather conditions
  2. Captured data is delivered to a backend service
  3. The service calculates the expected power generation
  4. Calculated data is delivered to an analytics system
  5. Data is presented in a variety of charts and maps

This process flow sounds similar to the common Extract, Transform, Load (ETL) pattern, however the distinction to make here is that data is pushed from the source to the backend service instead of pulled. This means we need to update our pipeline to be more reactive.

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Iron.io Now Available in Microsoft Azure Marketplace

azure-icons-drawing
Iron.io continues to grow its ecosystem of value-added partners. To this point, today you can now find Iron.io solutions in the Application Services within the Microsoft Azure Marketplace. Azure users can now directly leverage Iron.io within their applications to respond to application events, decouple components as independent services, offload individual workloads, and schedule regular occurring jobs.

IronWorker and IronMQ can be added by visiting the Azure Marketplace. Developers can then write and package task code for deployment to IronWorker’s processing environment within Azure. The Iron.io dashboard built into Azure provides detailed insight into the state of tasks for monitoring complete application activity and performance.

By using Azure and Iron.io, developers and operators can move individual components to the cloud, while maintaining safe application environments through improved security. Iron.io can also act as a key processing gateway to Azure component services including storage, queues, mobile services, and more, making it easy to create hybrid solutions of existing client-server applications and cloud-based microservices.

To quote Iron.io CEO Chad Arimura: “The combination of Azure and Iron.io brings flexibility, scalability, control and security – all the things Enterprises are seeking for their applications.

IronWorker and IronMQ are currently available in the West US region of Azure, and support multiple languages with native SDKs including Go, Java, Ruby, PHP, Python, Node.js, and .NET.

Using IronWorker to Power Custom Service Integrations

The rise of the API economy in recent years has also given the integration economy a much needed breath of new life. What was once a painful process of dealing with proprietary formats and clunky middleware, has now become a streamlined process via openly consumable cloud-native REST APIs. As such, a new breed of services such as Zapier and IFTTT have come along to make API integrations as simple as a few clicks, while other products such as Slack have made integrations a first class citizen feature of the product itself.

The technology that powers many of these integrations behind the scenes is webhooks, essentially an event-driven callback – when this happens, notify that via HTTP POST. Webhooks are an incredibly useful feature with many services, however as developers, we’ll always find a scenario that’s beyond what’s provided out of the box. With service integrations, this often means performing custom data translations such as field mappings, or additional business logic such as filtering and tagging.

We see a lot of our customers bridge services together using IronWorker because of its direct webhook support, flexibility to run any custom logic in any language, ability to scale concurrently behind the scenes, and “serverless” environment. We say “serverless” in quotes because to a developer, there’s never a need to think or worry about provisioning resources to run and manage tasks at scale. When one of our users shared his custom integration using IronWorker on Twitter, I got in touch to hear more and share his story.

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What is a Worker???

what is worker drawingToday, Iron.io sales engineer/teacher Yaron Sadka continues our FAQ video series by answering IronWorker related questions. In the following two videos he guides us through: What is a Worker? and What you use a Worker for?

Stay posted for more of these FAQ video to come.  Click here if you have any questions or want to take a deeper dive into what Iron.io does. Continue reading “What is a Worker???”

How to Deliver Massive Volumes of Sports Scores and News to over 12 Million Mobile Users at Bleacher Report

Bleacher Report (B/R) is one of the leading sports news sources in the country. Second only to ESPN, Bleacher Report is a growing force for team-specific sports content, commentary, and real-time event coverage. B/R’s goal is to deliver a comprehensive experience for sports fans and foster engagement with favorite teams and topics across all major sports. The content that B/R delivers includes curated content originating from featured columnists and external sources, all pushed to fans in real-time. They provide timely and relevant news to sports fans keeping them informed and ahead of the game.

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Project Thor Will Deliver First True Hybrid IronWorker Solution

Today, Iron.io announced Project Thor, which is developing the world’s first hybrid job processing system. This is unlike anything we’ve done to date. Unlike previous IronWorker technology, Project Thor is architected to deliver the power of Iron.io to any server in the world in minutes.

With Project Thor, the Iron.io IronWorker Docker container is deployed on a company’s own servers, which then communicates with the Iron.io API. This brings the power of Iron.io’s event-driven computing service to the Enterprise in just a few easy steps. Project Thor seamlessly integrates with existing solutions for container deployment, such as OpenShift by Red Hat, Cloud Foundry, OpenStack and Kubernetes.

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No lock in, thanks to Docker

Flexibility and the freedom to choose are among the core tenets at Iron.io. For those who have been following Iron.io posts for some time, we’re sure you’ve read a number of the Docker-related developments we released over the past year, including the previous blog post that IronWorker supports custom Docker images. This is awesome not only for many reasons from a workflow perspective, it also means there’s no lock in.

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Treasure Data and IronWorker (repost)

Our friends at Treasure Data wrote a blog post about data collection in Ruby and how to run multiple data collection tasks in parallel (or scheduled) using IronWorker. The example from Treasure Data demonstrates what it takes to build a simple logging application in Ruby with IronWorker to manage and log the output to Treasure Data, which can then perform queries.

As noted in the blog, this example is not a complete solution but an illustration to show users what’s possible when combining Iron.io and Treasure Data. Big thanks to John Hammink and the Treasure Data team for their work to educate the community.

Here’s an excerpt from the original post:

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