Intel IT empowers business units to easily make rapid, impactful business decisions. Ingesting a variety of internal/external data sources has challenges. This slideset covers how Intel IT overcame the issues with Hadoop and Gobblin. Learn more at http://www.intel.com/itcenter

1. Seshu Edala, Dave Schaefer, Nghia Ngo – IT Architects
November 2015
Gobblin @ Intel

2. 2
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3. Outline
 Integrated Analytics Vision
 Data Ingestion Challenges
 Solution
 What we would like to do
 What we did
 Challenges
 Need Help
 Summary
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4. Integrated Analytics Vision & Mission
Our Vision: Customers are empowered to easily make rapid, impactful business decisions and
uncover new revenue channels through connected data & analytics
Our Mission: Provide clean, relatable, integrated data using a consistent approach to deliver
business recommendations and insights through visual and interactive usage
Transformed and
Connected Data
Raw Data Advanced
Analytics
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5. As Is – Data Ingestion Architecture
Firewall and
Proxy Channels
External Source
Systems
IT BI Hadoop Cluster
Gateway Node
Camel
Hadoop Storage
Internal Source
Systems
Logs
DataMart
EDW
DataMart
RDBMSFlat/CSV
Files
SFTP
Vendor
utility
Hadoop
Put
Python
script
HDFS Hive
Hadoop
Put
Custom
utility
Hadoop
Put
Hadoop
Put
Hadoop
Put
Data
Consumption
Transformation
Visualization
tools
Client Tools
Sales CRM
Marketing
campaign
management
Content
Tagging
Webinar
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6. Data Ingestion Challenges
Ingesting a variety of internal/external data sources, such as enterprise data warehouse,
enterprise master data, spreadsheets, social media feeds, marketing data, retailer data, etc.
This resulted in variety of challenges including:
• Individual project teams instrumenting their own methods for ingesting data from various
sources and building their own data pipelines
• Operational Complexity to manage the individual pipelines
• No reusability as each project team created redundant methods/codebases for ingesting
data sources
• High development cost as each team built their own data ingestion pipelines
• Inconsistency in the quality of project teams’ data ingestion codebases impacting data
qualify and reliability
• Job failures resulting from data format, quality, schema evolution and availability issues
• Skillset challenges
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No standardized reusable framework for data ingestion

7. Solution: Data Ingestion Architecture with Gobblin/Kite
Firewall and
Proxy Channels
External Source
Systems
IT BI Hadoop
Cluster
Gateway Node
DataMart
EDW
DataMart
Data Ingestion
Reusable Framework
Kafka
Validation
RestFul
APIs
And many
more….
Hadoop
Storage
Hive / HDFS /
Hbase
Internal Source
Systems
RDBMSFlat/CSV
Files
SFTP
Vendor
APIs
Gobblin
Interface
Logs
File
Adapter
Config
Files
Alert
CSV
Adapter
RDBMS
JDBC Connector
Data
Consumption
Visualization
tool
Client Tools
Sales CRM
Marketing
campaign
management
Content
tagging
Webinar
Retailer
Social media
feeds
K
i
t
e
7
UI

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What we set out to do?
Functionally evaluate Gobblin for ingesting and integrating data.
Prototype a non OOB source to extract data out of an “online campaign
automation provider”
Acceptance Criteria
 Bulk RestAPI
 Validate the correctness of data
 Data Consistency from end to end
 Notification, status and error logging
 Ability to log kickout records
 Training plan for implementation and adoption plan

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What we did
Data Scope
• 4 objects
• accounts
• contacts
• 9 activities
• 59 custom objects
Parallel load data
• Hive (not using compaction) *
• HDFS (BaseDataPublisher)
Functional UI ready
• Scheduling
• Job History
• Authoring job configurations
Functional backend ready
• Enterprise scheduler
• Gobblin Standalone
• Gobblin Map-Reduce *
Quality checking policies
• Row level
• Task level
Enterprise features
• Alerting
• Monitoring
• Profiling *
• Logging
* Needs more attention

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Process Flow
Establish connection
•Authentication
•Endpoint indirection
Object Determination
•Get Object Listing
•Get Schema Definition
•Slice Schema
Create Intent
•Create Exports
Establish size boundaries
•Create Syncs
•Poll Syncs
•Slice batches
Download
•Parallel batches
Rebuild data
•Reassemble
•Schema inferencing
•Data Conversion
Data Publishing
•Hive/Impala load
•View Definition
•Quality enforcement
Parallel download and reassembly of data blocks

11. 11
Gobblin Challenges
User Interface – Visual Execution and Evaluation
Data Routing – Complex enterprise integration patterns routing challenging to
implement
public enum Result {
PASSED, // The test passed
FAILED // The test failed
}

12. 12
Need Gobblin Community Help
 Address adoption challenges
 Intake process for third-party contributions.
– New Source - “online campaign automation provider”
– Spark based ingestion candidates (parquet, avro, json, JDBC, s3) and runtime
– Kite SDK
 Partnership with key big data vendors – CDH, HDP, MAPR – for internalizing Gobblin
capability
– Deployment, Management, Metrics, and Lineage Integration
 Implement queuing or pluggable schedulers that do not rely on PID and workdir states;
better integration with enterprise schedulers.
 Make Hive publishers native; versus offline compactions.
 Publish documentation for user community

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Summary
 Gobblin is a robust data integration framework that meets the scale, quality,
enterprise readiness imperatives expected;
 However, some features like usability, enterprise integration patterns,
scheduling, profiling, lineage, deployment, documentation could be improved.