Philips Healthcare goes Saturday Night Live!


Q:  How is healthcare changing in the US?  A:  Just ask Philips Healthcare. Read this article to learn how two weekend warriors leveraged Attunity CloudBeam to load 37 million records in < 90 minutes.

Weekend Warriors Leverage Attunity CloudBeam and the AWS Marketplace to Load Data to Amazon Redshift with Just a Few Clicks

Q:  How is healthcare changing in the US?

A:  Just ask Philips Healthcare.

To increase the quality of care in the US market, Philips Healthcare relies heavily on data to understand patterns and drive decisions.  Understanding data related to reimbursement claims, operations, cost side, and HR can result in a Competitive Advantage.

Data Scientists Douglas Ranahan and Andy Allaway like to run fast and hard.  Under deadline to build new models and deliver on-going intelligence to the executive team, Doug and Andy turned right to the Cloud and Amazon Web Services.

Data Management and Analytics made perfect sense in an agile cloud setting.  The obvious solution on paper:  a SQL Server / Amazon Redshift / BI stack.

But wait!  How does one get data from SQL Server to Amazon Redshift?  Working within a “prove it” budget, Doug and Andy quickly learned that native tools were somewhat clunky and low performance.  In fact, using bespoke methods, Andy estimated it would take 56 days to load 37 million records! This kind of timetable was not acceptable so the team continued to hunt for a more effective loading solution.

Under tremendous pressure, working over the weekend, Doug and Andy set out to overcome the data loading bottleneck.  Philips Healthcare turned to the AWS Marketplace and located Attunity CloudBeam optimized for Amazon Redshift.  A few clicks later, Andy was able to load those 37 million records in less than 90 minutes. 

By 11:30 pm, Philips Healthcare went SATURDAY NIGHT LIVE!  Doug and Andy implemented an end-to-end, enterprise-grade solution with just a few clicks – heroes by Monday morning! Using Attunity CloudBeam was a game changer for the team – they went from nearly two months of work to just under two hours.


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