Taming the Compliance Beast: Lessons
learnt at LinkedIn Sept 28, 2017 Shirshanka Das, Principal Staff Engineer, LinkedIn Tushar Shanbhag, Head of Data Products, LinkedIn @shirshanka, @tusharis ever-evolving ^

Data Protection in a Digital
World PLAYING CATCH-UP WITH INNOVATION GDPR

metric scripts production code Business
facing decision making OUR VISION Create economic opportunity for every member of the global workforce LinkedIn’s Vision 29K schools 10M companies 11B endorsements 500M Members 10M jobs

The LinkedIn Privacy Paradox “On
one hand, the company has 500+ million members trusting the company to protect highly sensitive data. On the other hand, one only joins the largest professional network on the Internet because they want to be found !" Kalinda Raina, Head of Global Privacy, LinkedIn MEMBER PRIVACY <> MEMBER DISCOVERY

metric scripts Members First is
a Core Value for LinkedIn MEMBER PRIVACY WHILE DELIVERING MEMBER VALUE production code Well-connected. Get relevance right. Few connections. Give them inventory. Example Member value is proportional to knowledge Member privacy is paramount for LinkedIn We strive to maintain this fine balance

Data Is the Lifeblood of
LinkedIn MEMBER EXPERIENCES + BUSINESS DECISIONS production code Member Data System of Intelligence Member Experiences Business Decisions

We needed data democracy to
deliver member value LinkedIn Data Science I want to analyze as much data as possible so my models are accurate Data Democracy ALL THE DATA, ALL THE TIME I want to discover data that’s needed for my analysis as fast as possible I want to access that data as quickly as possible for my analysis

I want my personal data
to be stored only where needed and not propagated unnecessarily Data Protection Need to Ensure Member Privacy LinkedIn Members STORE, PROCESS, DELETE,.. I want my personal data to be deleted when I close my account or request deletion I want my personal data to only be processed if essential and only if I consent

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox

LinkedIn’s Data Ecosystem

LinkedIn’s Data Ecosystem

LinkedIn’s Data Ecosystem

LinkedIn’s Data Ecosystem

LinkedIn’s Data Ecosystem

LinkedIn’s Data Ecosystem

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox

Data Hubs at LinkedIn In
Motion At Rest Scale O(10) clusters ~2.3 Trillion messages ~450 TB Scale O(10) clusters ~10K machines ~100 PB

In Motion At Rest Data
Integration SFTP JDBC REST Azure Blob, Data Lake Storage

SFTP JDBC REST Apache Gobblin:
Simplifying Data Integration @LinkedIn Hundreds of TB per day Thousands of datasets ~30 different source systems 80%+ of data ingest Open source @ https://gobblin.apache.org/ Stream + Batch Adopted by LinkedIn, Intel, PayPal, Apple, IBM, Swisscom, Prezi, AppLift, NerdWallet and many more… SFTP Azure Blob, Data Lake Storage

REQUIREMENTS Less Data Legal: Right
to Erasure or Right to be Forgotten “Delete all my personal data without undue delay when it is no longer necessary / when consent has been withdrawn” Engineering: Need the ability to delete some specific subset or all data associated with a specific LinkedIn member from all our data systems

A lot of data, different
formats Challenges Understand HDFS data: organization, formats, … Cycle asynchronously, within an SLA, deleting records, without affecting running jobs Quarantine exceptional records for manual triage Can scale to processing hundreds of PB of data Data Deletion IMPLICATIONS FOR HADOOP

Gobblin: The Logical Pipeline Source
Work Unit Work Unit Work Unit Extract Convert Quality Write Data Publish WriteQualityConvertExtract Extract Convert Quality Write Task Task Task

Gobblin: Extending for Purge HDFS
Work Unit Data Publish Extract Convert Quality Write Task Task HDFS If needs purge then drop else continue Member’s Delete Requests

STATUS AND CHALLENGES Gobblin: Data
Lifecycle Management at Scale Status Number of datasets: many thousands Amount of data scanned for purge: XXX TB/day Challenges Immutable Storage Formats + Right to Erasure = Unhappy Disks “Widespread implementation will surely lead to innovation in these formats!”

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox DATA LIFECYCLE MANAGEMENT

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox DATA LIFECYCLE MANAGEMENT

LinkedIn’s Data Ecosystem

Metadata based Search Experience for
Data Scientists Data Discovery Where is dataset X? How did it get created? Usage : In production since 2014 Users : Data Scientists, Product Engineers Use Cases: Discovery, Impact Analysis WhereHows FIND DATA, NAVIGATE RELATIONSHIPS Open source @ github.com/linkedin/wherehows

SEARCH SCREENSHOTS
WhereHows

LINEAGE SCREENSHOTS
WhereHows

More than just Discovery Use
Cases Which datasets at LinkedIn contain PII or highly confidential data? How many contain member-member messages? How many of them are accessible by team X? Have all datasets been purged within SLA? Discovering Violations ANSWERING HARDER QUESTIONS

Wide + Deep Metadata Comprehensive
coverage of data systems at LinkedIn We have > 20 systems! SQL, NoSQL, Indexes, Blob Stores, … Deeper understanding of each dataset Schema is not enough Need to understand semantics Discovering Violations REQUIREMENTS

A METADATA REFINERY APPROACH WhereHows
Architecture @ 10,000 ft ML driven refinements

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox DATA LIFECYCLE MANAGEMENT METADATA

METADATA DATA DEMOCRACY <> DATA
PROTECTION More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox DATA LIFECYCLE MANAGEMENT

FREEDOM OF EXPRESSION Many Transformation
Engines @ LinkedIn In Motion At Rest

HARD TO CHANGE ANYTHING UNDERNEATH!
Challenge for Infrastructure Providers (Pig scripts) My Raw Data Native readers, dependencies on path, format hard-coded Hard to move to better formats without breaking everyone or copying data twice My Raw Data

HARD TO CHANGE ANYTHING UPSTREAM!
Semantic Challenges Data is unclean (bad data on certain dates) Data models are in constant flux (split event into multiple) Have to change data processing logic everywhere! My Raw Data

AN API TO MANAGE EVOLUTION
We need “microservices” for Data My Data API My Raw Data

A DATA ACCESS LAYER FOR
LINKEDIN We built Dali to solve this Logical Tables + Views Logical FileSystem Abstract away underlying physical details to allow users to focus solely on the logical concerns

Dali: Implementation Details in Context
Dali FileSystem Processing Engine (MR, Spark) Dali Datasets (Tables+Views) Dataflow APIs (MR, Spark, Scalding) Query Layers (Pig, Hive, Spark) Dali CLI Data Catalog Git + Artifactory View Def + UDFs Dataset Owner Data Source Data Sink

Simple to Complex Different Types
Basic Restrictions Access to dataset based on business need Privacy by Default Analysts shouldn’t get access to raw PII by default Consent-based Access Access to certain data elements only available if member has consented for that particular use- case Access Restrictions REQUIREMENTS

STEP 1: DATA + METADATA
Solving for Compliant Access Schema = { int memberId String firstName String lastName Position[] positions educationHistory[] educationHistory … } MemberProfile MEMBER_ID NAME PROFILE DATA NAME : is_pii MEMBER_ID : is_pii Raw Dataset Meta Data

STEP 2: A MEMBER’S PREFERENCES
Privacy Preferences

A BITMAP DATASET: ONE PER
MEMBER Privacy Preferences Member Privacy Preferences

Solving for Compliant Access With
Dali Raw Dataset Meta Data Member Privacy Preferences Dali Reader responsibility: Given: (Dataset, Metadata, UseCase) Generate: Dataset and Column-level transformations (obfuscate, null, …) Auto-join with Member Privacy Preferences (filter out data elements that are not consented to) Processing Logic Dali Reader Library Use Case = X

Solving for Compliant Purging With
Dali + Gobblin Raw Dataset Meta Data Member Privacy Preferences Gobblin Purger Dali Reader Library Use Case = Purge Member’s Delete Requests Purged Dataset

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox DATA LIFECYCLE MANAGEMENT METADATA DATA ACCESS LAYER

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox : Solved ! METADATA DATA ACCESS LAYER DATA LIFECYCLE MANAGEMENT

DATA DEMOCRACY + DATA PROTECTION
The Technology Blueprint WhereHows* Dali Apache Gobblin* * Open Source : We can collaborate on these together! DATA LIFECYCLE MANAGEMENTDATA ACCESS LAYER METADATA

Core company value, implemented by
Technology & Process Privacy By Design Privacy : Technology + Process SUSTAINABILITY IS CRITICAL Product : Security & Privacy Review Data : Data Model Review Legal : Regulation change -> Tech requirements Company-wide : “Horizontal” Initiatives

Getting Stricter and more complex
Data Protection Key Takeaways THE BEAST IS REAL Stricter regulations in a digital world Increasingly more complex to implement This is an accelerating global trend

We’ve established a blueprint to
sustainably address privacy Learnings at LinkedIn Key Takeaways THE BEAST CAN BE TAMED ! Privacy By Design : baked into technology stack & product development process Standardization : To solve at scale, certain parts need to be centralized and standardized Company-wide : Needs co-ordinated effort across various functions

DATA DEMOCRACY <> DATA PROTECTION
More Data Discover Data Easy Access Less Data Discover Violations Restricted Access The Data Paradox : Solved ! METADATA DATA ACCESS LAYER DATA LIFECYCLE MANAGEMENT

Thank You!