This document summarizes Adam Muise's proposed agenda for a March 26, 2013 data integration working session. The agenda includes introductions, discussing common data integration patterns, a roundtable on user group members' CDC/ETL use cases, and an overview of new data integration solutions like Hadoop data lakes, streaming technologies, data governance tools, and LinkedIn's Databus system.

1. Data Integration in 2013:
A working session
Adam Muise
March 26 2013
Note: This deck is purposely sparse. Want value?
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2. Proposed Agenda
• Introductions
• Discuss common Data Integration Patterns
• Round-table of User Group Member CDC/ETL Use Cases
• New Data Integration Solutions: A change from the Old Guard:
– Hadoop and the Data Lake
– Streaming (+ Hadoop)
– Data Lake Governance / Management (InfoTrellis)
– Databus (LinkedIn)
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3. Introductions
Who let you in?
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4. General Data Integration Patterns
• Enterprise Application Integration*
– Metadata lookup
– Validation
– Extra-app communication
• Enterprise Service Bus (SOA, Message Bus/Hub)*
• Federation*
– Bridging multiple databases with a query layer
– Eg: Composite
• Extract Transform Load (ETL)*
– Collection
– Aggregation
– Format/Schema transformation
• Data Lake
– Landing Zone for multiple datasets in one store
– Mixed schema, often raw structured/unstructured data
– Eg: Hadoop
* Source: Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture, Anthony David Giordano, 2010, IBM Press.
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5. Use Case Roundtable
Data that’s keeping you up at night…
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6. Scotia iTrade: Geoffrey Li
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7. New Data Integration Solutions
Fresh Ideas to new and old problems…
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8. Hadoop: The Data Lake
Publish Event
Signal Data
Transformation
Model/ Transform &
Apply Metadata Aggregate
Publish
Exchange
Explore
Visualize
Extract & Report
Load
Analyze
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9. Streaming & Hadoop
http://developer.yahoo.com/blogs/ydn/posts/2013/02/storm-and-hadoop-convergence-of-big-data-and-low-latency-processing/
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10. Streaming & Hadoop
http://developer.yahoo.com/blogs/ydn/posts/2013/02/storm-and-hadoop-convergence-of-big-data-and-low-latency-processing/
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11. DataBus (LinkedIn)
Databus is a low latency change capture system which has become an
integral part of LinkedIn’s data processing pipeline. Databus addresses a
fundamental requirement to reliably capture, flow and processes primary
data changes. Databus provides the following features:
1. Isolation between sources and consumers
2. Guaranteed in order and at least once delivery with high availability
3. Consumption from an arbitrary time point in the change stream including full bootstrap
capability of the entire data.
4. Partitioned consumption
5. Source consistency preservation
https://github.com/linkedin/databus/wiki
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12. DataBus (LinkedIn)
https://github.com/linkedin/databus/wiki
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