Constant Contact: An Online Marketing Leader’s Data Lake Journey
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Constant Contact is an online marketing company that handles large amounts of customer data and wants to enable more advanced analytics. They implemented a data lake architecture to centralize data from various sources and make it accessible for different users like data scientists, analysts, and business leaders. The key aspects of their data lake include a flexible reference architecture, identifying different types of users and tools, documenting metadata through an asset inventory, implementing security controls, and establishing lightweight governance practices over the data domains. The data lake approach allows Constant Contact to more easily perform analytics on large, diverse datasets and gain insights.
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Constant Contact: An Online Marketing Leader’s Data Lake Journey
- 1. Constant Contact: An Online Marketing Leader’s Data Lake Journey Matt Laudato Director, Big Data Analytics Constant Contact Session DMT-3517
- 2. Agenda • Introduction – About Constant Contact • The Analytics Process • Enabling Analytics through Data Lakes • Summary • Q&A
- 3. About Constant Contact We Do Digital Marketing – BIG!
- 4. About Constant Contact 450,000+ customers Big data. 185,000,000+ emails sent per day BIG data. 300,000,000+ subscriber pool BIG DATA.
- 5. About Constant Contact We track and record everything. opens clicks opt-outs bounces
- 6. It’s not about counting… …it’s about insights. 100,000 44% from just 10% The Analytics Process Data Lakes?
- 7. Use Case: Architecting for AnalyticsThe Analytics Process Data Lakes?
- 8. Use Case: Architecting for Analytics Enabling Analytics through Data Lakes This... ... Not this
- 9. What not to do Data Lake Operational Analytics System Enabling Analytics through Data Lakes
- 10. The Keys to Data Lake Success Data Lake Architecture Access Model Asset Inventory Security Governance Enabling Analytics through Data Lakes Operational Analytics System
- 11. Reference Architecture for the CTCT Data Lake Enabling Analytics through Data Lakes
- 12. Architecture Keys Enabling Analytics through Data Lakes • Linear data ingestion time • Horizontal expansionScale • Data source patterns • New data sourcesFlexible • No SPOF • Vendor support • Disaster recovery plan Reliable
- 13. Access Model – different strokes Enabling Analytics through Data Lakes Can we predict key KPIs? Can I explore that data? How’s the business doing? We need a new data set
- 14. Access Model Keys Enabling Analytics through Data Lakes • Operational report writers • Data Scientists • Business leaders • Predictive Modelers Identify Stakeholders • Reporting • Aggregation • Modeling • Visualization Tools • Internal Knowledge Bases • Local experts • Vendor training Training
- 15. Asset Inventory – What’s in your lake? Enabling Analytics through Data Lakes ETL and model code Aggregate data sets Data Domains Reports
- 16. Asset Inventory Keys Enabling Analytics through Data Lakes • Data sources • Key terms • Key transformations Identify • Data Lineage • Change procedures • Usage models Document • Organizational shift • Data stewards lead the charge • Strict ‘no one-off’ policy Share
- 17. Security – Who’s in your lake? Enabling Analytics through Data Lakes Access rights PCI and PIIHackers! Auditing
- 18. Security Keys Enabling Analytics through Data Lakes • Identify PII, PCI Data • Encrypt in the lakeData • Access logging and auditing • Data set and field level security • Download monitoring System • Trust but verify • Transparent policies • Training People
- 19. Data Governance in the Lake Enabling Analytics through Data Lakes Who owns this data? What is data truth? These numbers don’t agree! It’s a swamp, not a lake!
- 20. Governance Keys Enabling Analytics through Data Lakes • Stress the value • Adopt the behaviorsMindshare • Minimal governance in lake • Increased governance in warehouse • Data domains Lightweight • Per-domain process • Data Glossary • Data stewards Repeatable
- 21. Summary • It’s a lake, not a swamp – inventory and manage • Lightweight governance is critical • Architecture needs to be flexible • Ignore security at your own risk • Involve accessstakeholders early and often