Iron.io for ETL

Use Iron.io for ETL to extract, transform, and load data. Then, leverage this data for anything from analyzing trends or directing workloads.

Key Features

Iron.io is a best-in-breed solutions, providing a comprehensive feature set across the entire workflow lifecycle

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Flexible Job Scheduling

Schedule future jobs to run once, a set number of time, on a recurring, or many options in between. Scheduling API replaces CRON with an HA service that withstands node failures.

Job Priorities

Solutions include a built-in priority manager, allowing users to set the importance of specific jobs to be run.

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Logging

STDOUT is captured for every job and exposed via API and dashboard, and can stream to syslog or 3rd party.

Webhooks

Make the most of the evented web by hooking your workers up to an HTTP Post endpoint, another worker, or an IronMQ queue. The use of webhooks allows you to create a really powerful workflow across several distinct applications without the need of official integrations.

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Pull Queues, Push Queues & Long Polling

Once your code is uploaded, you can queue up jobs and Iron.io handles the provisioning and execution. Iron.io solutions support a variety of methods of getting messages. Pull queues periodically ask a client to check for messages, push queues inform subscribers when a message is added, and long polling allows you to keep a connection open with a queue until a message has been added.

Failure Handling

Job state change provides error and timeout handling, with alerting and auto-retry capabilities. Place alerts on your queues that notify your systems when they reach a certain number of messages for incorporating actions such as auto-scaling, failure detection, load-monitoring, and system health.

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Designed for last-minute travel planners and optimized for the mobile era, HotelTonight connects adventure-seeking, impulse travelers with just-in- time available hotel rooms wherever they land. See how HotelTonight leverages Iron.io for ETL to streamline and automate their entire ETL process and bring together all their disparate data sources into a single flexible datastore.

Download Case Study