Big Data, Big Data, Big Data – everybody is talking about it, but what is it, why are people talking about it, and how is it being done? Come ready to talk about emerging healthcare big data use cases that are pleading for the help of practical and powerful technologies like Spark, Hive, and others. If applied appropriately, these technologies can rev up your data warehouse and help you to address evolving data-driven healthcare needs around unstructured data, real-time data feeds, and machine learning. Sean Stohl, SVP in Product Development at Health Catalyst, will give you a practical understanding of where to get started with these technologies. Sean will also give you a glimpse how he thinks these technologies will evolve over time in this technically focused webinar. Attendees will be able to explain: What Big Data and Hadoop are Why Big Data and Hadoop are needed in healthcare What the challenges to adoption are How to get started Attendees will also get to see Big Data in action. We look forward to you joining us.

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Exploring How to Use Hadoop in
your Healthcare Big Data Strategy
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Sean Stohl
Senior Vice President, Product Development
Health Catalyst

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Poll Question #1
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What brought you to this webinar? 115 Respondents
1. Everyone is talking about Big Data/Hadoop – What is it? – 31%
2. Searching for uses cases – What is the value proposition? – 42%
3. Need help implementing it – 7%
4. Want to hear others’ experiences – 16%
5. I am bored so why not try this webinar – 4%

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Learning Objectives
Be able to explain
• What is Big Data and Hadoop
• Why do we need Big Data and Hadoop in Healthcare
• What are the challenges to adoption
• How do I get started
• See it in action
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Scaling Up Limits

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What does it take to reach the Big Data threshold?
3 V’s of Big Data

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We Are Not “Big Data” in Healthcare Yet
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Volume, Velocity, and Variety aren’t the only reasons to move
Dear Data…

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• Created by Doug Cutting and Mike Cafarella at Yahoo in 2005.
• Hadoop named after Cutting’s son’s toy elephant.
• “The name my kid gave a stuffed yellow elephant. Short, relatively easy to spell and
pronounce, meaningless, and not used elsewhere: those are my naming criteria. Kids
are good at generating such. Googol is a kid’s term.” - Doug Cutting
• Open-sourced software framework that supports processing and storing of large data sets
distributed across clusters of commodity hardware.
• HDFS – Hadoop Distributed File System. File System that provides the capability to
distribute data across a cluster to take advantage of the parallel processing of Map
Reduce.
• Map Reduce - Parcels out work to various nodes within the cluster or map, and it
organizes and reduces the results from each node into a cohesive answer to a query.
History of Hadoop

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Poll Question #2
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How would you categorize your organization’s involvement with Hadoop? 126
Respondents
1) Piloting Hadoop in the Cloud or Plan to – 9%
2) Piloting Hadoop on Premise or Plan to – 18%
3) Heavily using Hadoop in the Cloud – 1%
4) Heavily using Hadoop on Premise – 5%
5) Unsure or not applicable – 68%

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• Data Growth
• Different Types of Workload
• Semi Structured
• Archiving
• Streaming
• Machine Learning
Why Big Data and Hadoop in Healthcare

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Just Beginning: Digitization of Health
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“EMR data represents ~8% of the data we need for population health and precision
medicine.” — Alberta Secondary Use Data Project
The Growing Ecosystem of Human Health Data
Healthcare
Encounter
Data
7x24
Biometric
Data
Consumer
Data
Genomic
&
Familial
Data
Social
Data
Outcomes
Data

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• Structured
• Data that can be stored relationally in RDBMS
• Semi Structured
• Data that has some organizational properties but isn’t in a relational database format
• CSV, XML, X12 (835/837) , HL7, JSON
• Doctor Notes - Template Generated Sections
• Unstructured
• E-mails, text messages, Word documents, videos, and pictures
• Doctor Notes – Free Form Sections
Types of Data

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Archiving

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Streaming

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Implementation
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Challenges to Adoption and
How to Overcome Them

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Poll Question #3
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Which challenge has been or would be the greatest barrier for your organization to
adopt Hadoop? 137 Respondents
1. People with the right skill sets – 33%
2. Funding hardware costs - 8%
3. Defining the business value – 37%
4. Security concerns – 6%
5. Unsure or not applicable – 16%

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Challenges to adoption
Organizational
Buying
Administering
Using
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Organizational
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Stuck in the Mud

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Buying
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Cloud
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Administering
 Fewer experienced people
 Lack of best practices
 Myriad of tools
 Open Source yes – but lots of assembly required
 Security?
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Administering
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Packaged Solutions
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Administering
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Invest in your people
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Using
• Which SQL on Hadoop
 Hive
 Impala
 Spark SQL
 Apache Drill
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Tools continue to Evolve
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http://www.infoworld.com/article/3131058/analytics/big-data-face-off-spark-vs-impala-vs-hive-vs-presto.html

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Don’t Rip and Replace
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Meeting in the middle
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RDBMS Vendors
• Oracle
• SQL Server
• Teradata
• …
Hadoop Solutions
• Hortonworks
• Cloudera
• Mapr
• Cloud
• …
Convergence

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Additive Approach

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Data Operating System

38. Demos

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Lessons Learned
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1. Let use cases help drive the need to implementing Hadoop. (Be Pragmatic.)
2. Think additive.
3. Invest in people now.
4. In general, the Cloud will give you the most flexibility in deploying Hadoop.

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Thank
You
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