CASE STUDY: HOTEL TONIGHT

REINVENTING HOTELBOOKINGSWITH IRONWORKER POWERED ETL
HotelTonight has reinvented the task of finding and booking discounted hotel rooms
at travel destinations. Designed for last-minute travel planners and optimized for themobile era, HotelTonight connects adventure-seeking, impulse travelers with just-intime available hotel rooms wherever they land.
This model has the market-enhancing effect of reducing excess inventory of unused
hotel rooms, while delivering a seamless user experience and deep discount for budget travelers who enjoy impulse travel and adventure.
What most travelers may not realize, is that behind the scenes at HotelTonight lies amassive business intelligence system that uses a sophisticated cloud-based ETL platform that collects, converts, and stores data from multiple external services.

WHAT ISEVENT-DRIVENETL?

ETL & ASYNCTASKPROCESSING

BENEFITSOFCLOUD ETL

Extract, Transform, Load(ETL) has been around in
IT circles for a long time,
dating back to tape storage and the mainframeera. Event-driven ETL
makes use of cloud-based
services along with loosely-coupled and flexible
processing to move data
between systems in realtime.

The key component powering HotelTonight’s ETL
process, which also allows it to be scalable andevent-driven is IronWorker, an asynchronous taskprocessing service provided by Iron.io. IronWorker
provides concurrent
workload processing thatis faster and more flexible
than existing methods.

Using cloud-based ETL,
HotelTonight has createda fully aggregated dataset that is more accurate,
more up-to-date, andmore reliable. Turning raw
data into knowledge fasterthan their competition
gives their analysts theability to make quicker updates on hotel room prices
and availability.

HOW HOTELTONIGHT’S ETL PIPELINE WORKS
HotelTonight’s ETL pipeline gathers external data from multiple sources and brings it
together into Amazon Redshift, a managed petabyte-scale data warehouse solution fromwww.iron.io
Copyright © 2015, Iron.io, Inc.

“The team at Iron.io has
been a great partner for us
for building our ETL pipeline. Their worker platform
gives us a quick and easymechanism for deploying and managing all ourRuby workers. They haveexcellent dashboards forreporting worker status,
giving us great visibility
over the current state ofour pipeline.”
Harlow Ward, Leaddeveloper at HotelTonightCASE STUDY: HOTEL TONIGHT
Amazon Web Services. Amazon Redshift acts as a “Unified Datastore” and makes use ofthe SQL query language to connect a variety of platforms using a Postgres Adapter. Custom Ruby scripts power the HotelTonight ETL process, connecting their Business Intelligence team to an SQL Workbench interface which front-ends their Amazon Redshift clusters. The dashboard lets anyone in the organization query the data and extract information
for use in their initiatives.

DISTRIBUTED ETL WORKFLOW
HotelTonight deploys a custom worker for each external data source. This loosely coupledapproach means that aggregation of each data source is independent of other sources.
“IronWorker’s modularity allows for persistent points along the lifetime of the pipeline. It
also allows us to isolate failures and more easily recover should data integrity issues arise.”according to Harlow Ward, lead developer at HotelTonight. “Each worker is responsible forits own unit of work and can kick off the next task in the pipeline.”

MONITORING AND ORCHESTRATION
In addition to solving the challenge of quick and easy deployment of independent workers,
the Iron.io dashboard provides current status of tasks to HotelTonight developers, giving
them instant visibility and insight on the state of their ETL pipeline. Users can control settings for the workflow, including increasing or decreasing concurrency, retrying tasks that
may have failed in prior attempts,or modifying job schedules.

Turning raw data into
knowledge faster thancompetition gives
HotelTonight’s analysts
the ability to makequicker updates on
hotel room prices andavailability.

NET RESULTS
Now that HotelTonight’s business intelligence data is consolidated within Amazon Redshift,
HotelTonight can run SQL queries to combine and correlate data from multiple platforms
into a unified dataset. Prior to this solution, HotelTonight’s data analytics consisted ofmultiple exported CSVs from each data source, merged into a single pivot table with lots of“magic” applied to make sense of it.
IronWorker makes it possible for HotelTonight to streamline and automate their entire ETL
process and bring together all their disparate data sources into a single flexible datastore.
HotelTonight can rest easy with the assurance that in using IronWorker, their data pipeline
into Amazon Redshift is in excellent order and can be updated or amended at will.

Behind the scenes atHotelTonight lies a
massive business intelligence system that uses
a sophisticated cloudbased ETL platform thatcollects, converts, andstores data from multiple external services.

www.iron.io
Copyright © 2015, Iron.io, Inc.