Session 30 Powerful Ways to Use Hadoop in your Healthcare Big Data Strategy
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1 Session 30 Powerful Ways to Use Hadoop in your Healthcare Big Data Strategy Bryan Hinton Senior Vice President, Platform Engineering Health Catalyst Sean Stohl Senior Vice President, Product Development Health Catalyst
2 2
3 Poll Question #1 What brought you here? a) Everyone is talking about Big Data/Hadoop What is it? b) Searching for uses cases What is the value proposition? c) Need help implementing it d) Want to hear others experiences e) I got lost 3
4 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 4
5 Scaling Up Limits 5
6 3 V s of Big Data What does it take to reach the Big Data threshold? 6
7 We Are Not Big Data in Healthcare Yet 7
8 Dear Data Volume, Velocity, and Variety aren t the only reasons to move 8
9 History of Hadoop Created by Doug Cutting and Mike Cafarella at Yahoo in 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. 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. 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.
10 Map Reduce Example 10
11 Poll Question #2 How would you categorize your organization s involvement with Hadoop? 1) Not interested 2) Interested but no plans to implement 3) Planning implementation 4) Piloting Hadoop 5) Heavily using Hadoop 6) Unsure or not applicable 11
12 Why Big Data and Hadoop in Healthcare Data Growth Different Types of Workload Semi Structured Archiving Streaming Machine Learning 12
13 Just Beginning: Digitization of Health EMR data represents ~8% of the data we need for population health and precision medicine. Alberta Secondary Use Data Project Healthcare Encounter Data Genomic & Familial Data Outcomes Data 7x24 Biometric Data Consumer Data Social Data The Growing Ecosystem of Human Health Data 13
14 Types of Data 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 s, text messages, Word documents, videos, and pictures Doctor Notes Free Form Sections 14
15 Archiving 15
16 Streaming 16
17 17
18 18
19 Implementation 19
20 Challenges to Adoption and How to Overcome Them
21 Poll Question #3 Which challenge has been or would be the greatest barrier for your organization to adopt Hadoop? a) People with the right skill sets b) Funding hardware costs c) Defining the business value d) Security concerns e) Unsure or not applicable 21
22 22
23 Challenges to adoption Organizational Buying Administering Using 23
24 Organizational Stuck in the Mud 24
25 Buying 25
26 Cloud 26
27 Administering Fewer experienced people Lack of best practices Myriad of tools Open Source yes but lots of assembly required Security? 27
28 Packaged Solutions 28
29 Administering 29
30 Invest in your people 30
31 Using 31
32 Using Which SQL on Hadoop Hive Impala Spark SQL Apache Drill 32
33 Meeting in the middle RDBMS Vendors Oracle SQL Server Teradata Convergence Hadoop Solutions Hortonworks Cloudera Mapr Cloud 33
34 Don t Rip and Replace 34
35 Additive Approach 35
36 Data Operating System 36
37 Demos
38 Lessons Learned 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. 38
39 Analytic Insights Questions & AnswersA 39
40 What You Learned Write down the key things you ve learned related to each of the learning objectives after attending this session 40
41 Thank You 41
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