Redefining ETL Pipelines with Apache Technologies to Accelerate Decision-Making and Execution for Clinical Trials
•Download as PPTX, PDF•
9 likes•1,095 views
Pharmaceutical and medical device makers spend over $130bn each year collecting and analyzing new data, mostly through clinical trials. It costs over $1.8bn to bring a new drug to market, and over $4bn when factoring in the cost of failures. By more efficiently understanding and analyzing this data, new drugs can reach patients quicker, safer, and at a lower cost. In this presentation, Eran will discuss how ETL pipelines can be built using the Apache and other open source projects to improve clinical trial development. We will examine how the system is built, the challenges we faced and how we are able to reduce cost, accelerate execution time, and improve results. We will also demonstrate how reliable resource allocation, scalable data ingestion adapters, on-demand and fault tolerant job deployments, and monitoring benefit clinical trial decision-making and execution.
Recommended
Recommended
More Related Content
What's hot
What's hot (18)
Viewers also liked
Viewers also liked (19)
Similar to Redefining ETL Pipelines with Apache Technologies to Accelerate Decision-Making and Execution for Clinical Trials
Similar to Redefining ETL Pipelines with Apache Technologies to Accelerate Decision-Making and Execution for Clinical Trials (20)
More from Eran Chinthaka Withana
More from Eran Chinthaka Withana (7)
Recently uploaded
Recently uploaded (20)
Redefining ETL Pipelines with Apache Technologies to Accelerate Decision-Making and Execution for Clinical Trials
- 1. Redefining ETL Pipelines with Apache Technologies to Accelerate Decision Making for Clinical Trials Eran Withana
- 2. www.comprehend.com Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 3. www.comprehend.com Open Source Member, PMC member and committer of ASF Apache Axis2, Web Services, Synapse, Airavata Education PhD in Computer Science from Indiana University Software engineer at Comprehend Systems About me …
- 4. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 5. www.comprehend.com Clinical Trials – Lay of the land Number of Drugs in Development Worldwide (Source: CenterWatch Drugs in Clinical Trial Database 2014) Source: http://www.phrma.org/innovation/clinical-trials
- 6. www.comprehend.com Clinical Trials – Lay of the Land Multiple Stakeholders • Study Managers • Program Managers • Monitors • Data Managers • Bio-statisticians • Executives • Medical Affairs • Regulatory • Vendors • CROs • CRAs Sites Labs Patients Safety EDC Reports ● Latent ● Fragmented Data PV Data Excel Sponsor Contract Research Organization (CRO) Sites and Investigators
- 7. www.comprehend.com For decades, clinical development was primarily paper-based.
- 8. www.comprehend.com Various Software and Practices Used in Each Layer medidata CROs and SIs Technologies
- 9. www.comprehend.com Clinical Trials with Centralized Monitoring Clinical Operations Sites Labs Patients ● Consolidated ● Real-time ● Self-Service ● Mobile Clinical Analytics & Collaboration Data Safet y EDC PV Data Excel
- 10. www.comprehend.com Providing up-to-date answers Executives Medical Review CRAs Data Management Clinical Operations EDC CTMS Safety ePro Other Web Ad-Hoc Mobile Collab
- 11. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 12. www.comprehend.com FDA, HIPAA Compliance Metadata/Database structure synchronization Less frequent (once a day) Data Synchronization More frequent (multiple times a day) Ability to plugin various data sources RAVE, MERGE, BioClinica, File Imports, DB-to-DB Synchs Real time event propagations Adverse events (AEs) - the need for early identification Business Requirements
- 13. www.comprehend.com Hardware agnostic for resiliency and better utilization Repeatable deployments Real time processing and real time events Fault Tolerance In flight and end state metrics for alerting and monitoring Flexible and pluggable adapter architecture Time travel Audit trails Report generations Technical Requirements
- 14. www.comprehend.com Events all the way Shared event bus for multiple consumers Use of language agnostic data representations (via protobuf) Automatic datacenter resources management (Mesos/Marathon/Docker) Core Design Principles
- 15. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 16. www.comprehend.com • Data processing Apache Storm and Trident, Apache Spark and Spark Streaming, Samza, Summingbird, Scalding, Apache Falcon, Azkaban • Coordination and Configuration Management Apache Zookeeper, Redis, Apache Curator • Event Queue Apache Kafka • Scheduling Chronos, Apache Mesos, Marathon, Apache Aurora • Database Synchronization Liquibase, Flyway DB • Data Representations Apache Thrift, protobuf, Avro • Deployments Ansible • File Management Apache HDFS • Monitoring and alerting Graphite, StatsD • Database PostgreSQL, Apache Spark • Resource Isolation LXC, Docker Technologies Evaluated
- 17. www.comprehend.com Data Processing Technology Evaluation Criteria Storm + Trident Spark + Streaming Samza Summingbird Scalding Falcon Chronos Aurora Azkaban DAG Support Y DAGScheduler Y Y Y Y Y N Y DAG Nodes Resiliency Y Y Y Y Y Y Y N Y Event Driven Y Y Y Y N N N N N Timed Execution Y Y Y Y Y Y Y Y DAG Extension Y Y Y Y Y Y Y Y Y Inflight and end state metrics Y Y Y Y Y Y Y Y Y Hardware Agnostic Y Y Y Y Y Y Y Y Y
- 18. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 20. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 21. www.comprehend.com Bare Metal Boxes Partitioned using LXC containers Use of Mesos to do the resource allocations as needed for jobs Managing Hardware
- 22. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 23. www.comprehend.com Ansible Repeatable deployments Password management Inventory management (nodes, dev/staging/production) Deployments
- 24. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 25. www.comprehend.com Adapters – High Level
- 26. • Syncher is for DB structural changes Syncher creates a database schema from the source information Runs a generic database diff and applies those to the target database • Seeder is for data synchronization Uses the database schema created by Syncher • Seeders gets jobs from Syncher or Timed scheduler Data Adapters
- 27. • Coordination and Configuration through Zookeeper Job configuration Connection information Distributed locking and counters Metric Maintenance Last successful run Data Adapters – Coordination and Configuration
- 28. www.comprehend.com Data Adapters - Implementation
- 29. www.comprehend.com Syncher Connectivity to source/sink systems fail • Retry job N times and alert, if needed Schema changes to the database fails in the middle • Transaction rollback Seeder Connectivity to source/sink systems fail • Retry job N times and alert, if needed If seeding fails midway • Storm retries tuples • Failing tuples are moved to an error queue Table and row level failues • Option to skip the tables/rows but send a report at the end Effect on “live” tables during data synchronizations • Option to use transactions or • Use temporary tables and swap with original upon completion Failure Modes
- 30. www.comprehend.com Can bring in data from more data sources and more studies effectively Run real time reports on studies and configure alerts (future) Can configure refreshes as needed by each use case Can throttle input and output sources at study/customer level Ability to onboard new customers and deploy new studies with minimal human intervention What Have We Gained
- 31. www.comprehend.com A generic framework which eases integration with new data sources • For each new source, implement a method to create a virtual schema and to get data for a given table can scale and fault tolerant has generic monitoring and alerting eases maintenance since its mostly generic code notification of important events through messages runs on any hardware What Have We Gained
- 32. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 33. www.comprehend.com Accessibility Customers must be able to drop files securely (SFTP like functionality) Ability to access resources through URLs Data storage Scalability and Redundancy Scale-out by adding nodes Resilience against loss of nodes, data centers and replication Miscellaneous Access control over read/write Performance/usage/resource utilization monitoring Distributed File System - Requirements
- 34. www.comprehend.com Two name nodes running in HA mode, co-located with two journal nodes Third journal node on a separate node Data nodes on all bare metal nodes Mounting HDFS with FUSE and enabling SFTP through OS level features Automatic failover through DNS and HA Proxy HDFS with High Availability Mode
- 35. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 36. www.comprehend.com Regulatory requirements Data encryption requirements for clinical data Audit trails Data quality Source system constraints Coordination between Synchers and Seeders Distributed locks and counters Automatic fail over when a name node fails in HDFS HDFS HA mode stores active name node in ZK as a java serialized object, yikes !! Challenges
- 37. Clinical Trials – Lay of the land Business and Technical Requirements Technology Evaluation High Level Architecture Implementation Managing Hardware Deployments Data Adapters: Implementation and Failure Modes Distributed File System Challenges Future Work Overview
- 38. www.comprehend.com Time travel Ability to go back in time and run reports at any given point of time Trail of data Containerization In-memory query execution with Apache Spark Future Work
Editor's Notes
- Dose 20-100 Efficacy and safety 100-300 > 1000