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by Ben Sharma, Ashish Thusoo
Architecting Data Lakes
1. Overview
What Is a Data Lake?
Drawbacks of the Traditional EDW
Key Attributes of a Data Lake
The Business Case for Data Lakes
Data Management and Governance in the Data Lake
Address the Challenge Later
Adapt Existing Legacy Tools
Write Custom Scripts
Deploy a Data Lake Management Platform
How to Deploy a Data Lake Management Platform
2. How Data Lakes Work
Four Basic Functions of a Data Lake
Data Ingestion
Data Storage and Retention
Data Processing
Data Access
Management and Monitoring
A Combined Approach
Metadata
3. Challenges and Complications
Challenges of Building a Data Lake
Rate of Change
Acquiring Skilled Personnel
Technological Complexity
Challenges of Managing the Data Lake
Ingestion
Lack of Visibility
Privacy and Compliance
Deriving Value from the Data Lake
Reusability
4. Curating the Data Lake
Data Governance
Integrating a Data Lake Management Solution
Data Acquisition
Data Organization
Data Catalog
Capturing Metadata
Data Preparation
Data Provisioning
The Executive
The Data Scientist
The Business Analyst
A Downstream System
Benefits of an Automated Approach
5. Deriving Value from the Data Lake
Self-Service
Controlling and Allowing Access
Using a Bottom-Up Approach to Data Governance to Rank Data Sets
Data Lakes in Different Industries
Healthcare
Financial Services
Retail
6. Looking Ahead
Ground-to-Cloud Deployment Options
Looking Beyond Hadoop: Logical Data Lakes
Federated Queries
Data Discovery Portals
In Conclusion
A Checklist for Success
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