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TRANSFORMING AND SCALING LARGE SCALE DATA
ANALYTICS: MOVING TO A CLOUD-BASED ENTERPRISE DATA
LAKE
Terry Padgett - Senior Solutions Architect, Cloudera
Nitin Naik - Chief Technology Officer, U.S. Census Bureau

2. 2
Census Background
• United States leading provider of quality
data about its people and economy
• Decennial, Economic, Demographic,
and a multitude of other surveys
• Serves other federal agencies
• Processes large volumes of data
• Preparing for future analytic needs of the
enterprise

3. Enterprise Data Lake
Make changes to culture, processes, and technologies to practice
and accelerate the efforts required to remain a leader in data and
technology innovation.
• Optimize Survey Operations
• Reduce Respondent Burden
• Improve Data Products
• Consolidate Data and Code
• Manage Large Datasets
• Centralize Security

4. EDL Guiding Principles
• Scalability
• Availability
• Automation
• Security & Privacy
• Data Diversity
• Data Stewardship
• Identifiable, Locatable and Linkable Data
• Reproducibility
• Governance

5. Cloud First
• Establish the EDL in GovCloud
• On-demand Server Instances
• Cloud Object Stores
• Leverage Serverless Computing

6. Data Availability
• Short-term and shared data available through cloud object stores
• Long-term data available through archival stores
• Built-in resiliency of storage to prevent data loss
• EDL applications deployed as highly available

7. Deployment Automation
• Repeatable deployment across tiers
• Platform Infrastructure - Teraforms
• AMI Creation - Ansible, Puppet, scripting
• HDP Clusters - Cloudbreak

8. Census Business Process and EDL
Mapping of the Survey Lifecycle to the Data Lifecycle and the identification of the data flow, allows EDL to incrementally
build upon the key areas highlighted in green. The EDL will focus on the Enterprise Data Lifecycle Stages (Process,
Derive, ect.) and leverage technology advances (e.g. Data mashups, Machine Learning, Distributed computing).
Consolidation of Data
Collection Systems
Consolidation of Data Management / Store Systems
Consolidation of Data
Dissemination Systems
CEDCaP Enterprise Data Lake (EDL) CEDSCI
Survey
Design
Frame
Development
Sample
Design
Response Data
Collection
Instrument
Development
Data Editing
& Imputation
Disclosure
Avoidance
Research/
Analytics
Data Product
Dissemination
Estimation, Data
Review, & Analysis
DEFINE COLLECT
Survey
Design
Frame
Development
Sample
Design
Instrument
Development
Response Data
Collection
CAPTURE
3rd Party Data
Capture
PROCESS DERIVE PUBLISH RESEARCH
Data Editing &
Imputation
Estimation, Data
Review, & Analysis
Disclosure
Avoidance
Data Product
Dissemination
Research/
Analytics
DISSEMINATE
Areas currently in the scope of CEDCaP,
CEDSCI or other programs
Areas currently in the scope of EDL
LEGEND
Survey Lifecycle
Data Lifecycle
EDL Supported Areas (not in scope)
Data Lifecycle

9. Enterprise Data Lake Features
• Data Control
• Data Lineage
• Authorization Model
• Storage Management
• Data Sharing
• Dynamic Platform Provisioning
• Cloud-based
• Cost Control
9

10. Data Control
• Data Registration
• Datasets onboarded with mandatory metadata
• Registered in the existing Data Management System
• Project access controls generated
• Code Repositories
• Data Lineage: Atlas
• Authorization: Ranger
• Controls for projects
• Column protection
• Row filtering

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Data Platform
Common Shared Services

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Data Platform
Compute On Demand

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Data Platform
Transient Clusters

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Data Movement
DataPlane Service

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Data Sharing
• S3 as first-class storage -permanent data
• Local HDFS - working data
• Hive: Data Warehouse

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Data Science
• Spark
• R
• SAS

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Data Lineage Tracability

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Data Protection
• EBS volume encryption
• S3 server side encryption
• SSL/TLS
• Hadoop TDE

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THANK YOU