How McAfee Built High-Quality Pipelines with Azure Databricks to Power Customer Insights on 250TB+ of Data: Lessons Learned in Data Governance and Lineage

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"How do you make over 250TB of data useful to data scientists? Bring a lot of CPUs? If only it were so simple! Understanding customer behavior requires having high quality, reliable data. Any data quality challenges are magnified with high volumes of data, limiting data scientists’ ability to understand, clean, and use data. If you have to clean up 600 million events per day, it’s like cleaning Moscone West’s floors: as soon as you’re done, you have to start all over again. Come learn how McAfee built a data-driven pipeline using Azure Databricks to maintain high data quality and comprehensive lineage to enable data scientists to be more productive and make sound statistical inferences."

Données & analyses

WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

David Newell & Geoff Oitment, McAfee
Customer Insights from 250TB+ of Data:
Lessons Learned in Data Governance and Lineage
#UnifiedAnalytics #SparkAISummit

“ 3#UnifiedAnalytics #SparkAISummit
At the heart of [AI] technology is data.
Henry Schuck, Forbes–
https://www.forbes.com/sites/forbestechcouncil/2018/05/02/why-data-accuracy-is-critical-to-the-evolution-of-artificial-intelligence-in-b2b-sales/#57bebedf466d

What’s so hard about Big Data?
4#UnifiedAnalytics #SparkAISummit
Technology Moves Fast
Siloed Data Sources
Finding Signal in the Noise
Limited Big Data Skills

McAfee started no better
5#UnifiedAnalytics #SparkAISummit
Current data systems are 15-20 years old
Multiple data silos
What activity does an event track?
Limited data science talent across
business functions
Typical McAfee
Data Analyst
I don’t know how to start!

Goal: enable self-service analytics
6#UnifiedAnalytics #SparkAISummit
Comprehensive Customer 360
All the data in one place
Accurate Calculations
Standardized, certified metrics
Easy to Access
Self-service in tool of choice
Scalable & Responsive
Get data fast, even in real-time

The data lake fallacy: it’s magic!
7#UnifiedAnalytics #SparkAISummit
These photos by Unknown Authors are licensed under CC BY-NC-SA
vs
A data lake is not a magic wand:
• Veracity still dependent on data quality
• Volume still dependent on inputs
• Value still dependent on skill set

How we kept the data lake clean:
portal for systematic data governance
• Data validation rules
• Source data collection driven
by documentation
Comprehensive documentation
at all ingestion points
• Automatic documentation and
governance enforcement
• Generated tracking manifests
• Data cleaned at the source
Centralized governance &
documentation system
8#UnifiedAnalytics #SparkAISummit

What we built:
documentation-driven pipeline
• All ETL jobs defined in
configuration
• Data lineage derived from
same configuration
Configuration-driven design
• Enables end-to-end data flow
visualization
• Configuration generated by
governance system
Integrates into the centralized
portal
9#UnifiedAnalytics #SparkAISummit

Architecture
10#UnifiedAnalytics #SparkAISummit
Anonymized Source Data
Data
Governance
Data Portal
Event
Processing
Client SDK
Collection &
Queuing
Event Hubs
(Kafka-compatible)
Storage
Data Lake
Data Lake Store
(HDFS-compatible)
Self-service
Analysis
or tool of choice!
Data Science
Pipeline
Processing
Azure Databricks
Analytics
Platform
Azure Databricks
Data Catalog
Proprietary
Marketing
Systems
Proprietary &
Third-party
Profiles
• Account
• Device
Orchestration
• Databricks Jobs
• Azure Data Factory

Lessons Learned
11#UnifiedAnalytics #SparkAISummit
Don’t compromise on data quality;
you’ll regret it later
Create small, manageable features that
enable quick iteration
Think ahead; don’t do something that will
handicap future development

Don’t compromise on data quality;
you’ll regret it later
12#UnifiedAnalytics #SparkAISummit

DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

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Plus de Databricks (20)

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

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Raven: End-to-end Optimization of ML Prediction Queries

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Massive Data Processing in Adobe Using Delta Lake

Machine Learning CI/CD for Email Attack Detection

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Infrastructure Agnostic Machine Learning Workload Deployment

Improving Apache Spark for Dynamic Allocation and Spot Instances

Importance of ML Reproducibility & Applications with MLfLow

Hyperspace for Delta Lake

How We Optimize Spark SQL Jobs With parallel and sync IO

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1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2. David Newell & Geoff Oitment, McAfee Customer Insights from 250TB+ of Data: Lessons Learned in Data Governance and Lineage #UnifiedAnalytics #SparkAISummit

3. “ 3#UnifiedAnalytics #SparkAISummit At the heart of [AI] technology is data. Henry Schuck, Forbes– https://www.forbes.com/sites/forbestechcouncil/2018/05/02/why-data-accuracy-is-critical-to-the-evolution-of-artificial-intelligence-in-b2b-sales/#57bebedf466d

4. What’s so hard about Big Data? 4#UnifiedAnalytics #SparkAISummit Technology Moves Fast Siloed Data Sources Finding Signal in the Noise Limited Big Data Skills

5. McAfee started no better 5#UnifiedAnalytics #SparkAISummit Current data systems are 15-20 years old Multiple data silos What activity does an event track? Limited data science talent across business functions Typical McAfee Data Analyst I don’t know how to start!

6. Goal: enable self-service analytics 6#UnifiedAnalytics #SparkAISummit Comprehensive Customer 360 All the data in one place Accurate Calculations Standardized, certified metrics Easy to Access Self-service in tool of choice Scalable & Responsive Get data fast, even in real-time

7. The data lake fallacy: it’s magic! 7#UnifiedAnalytics #SparkAISummit These photos by Unknown Authors are licensed under CC BY-NC-SA vs A data lake is not a magic wand: • Veracity still dependent on data quality • Volume still dependent on inputs • Value still dependent on skill set

8. How we kept the data lake clean: portal for systematic data governance • Data validation rules • Source data collection driven by documentation Comprehensive documentation at all ingestion points • Automatic documentation and governance enforcement • Generated tracking manifests • Data cleaned at the source Centralized governance & documentation system 8#UnifiedAnalytics #SparkAISummit

9. What we built: documentation-driven pipeline • All ETL jobs defined in configuration • Data lineage derived from same configuration Configuration-driven design • Enables end-to-end data flow visualization • Configuration generated by governance system Integrates into the centralized portal 9#UnifiedAnalytics #SparkAISummit

10. Architecture 10#UnifiedAnalytics #SparkAISummit Anonymized Source Data Data Governance Data Portal Event Processing Client SDK Collection & Queuing Event Hubs (Kafka-compatible) Storage Data Lake Data Lake Store (HDFS-compatible) Self-service Analysis or tool of choice! Data Science Pipeline Processing Azure Databricks Analytics Platform Azure Databricks Data Catalog Proprietary Marketing Systems Proprietary & Third-party Profiles • Account • Device Orchestration • Databricks Jobs • Azure Data Factory

11. Lessons Learned 11#UnifiedAnalytics #SparkAISummit Don’t compromise on data quality; you’ll regret it later Create small, manageable features that enable quick iteration Think ahead; don’t do something that will handicap future development

12. Don’t compromise on data quality; you’ll regret it later 12#UnifiedAnalytics #SparkAISummit

13. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

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