1. Designing Data
Architectures
for Robust Decision
Making
Gwen Shapira / Software Engineer

2. 2©2014 Cloudera, Inc. All rights reserved.
• 15 years of moving data around
• Formerly consultant
• Now Cloudera Engineer:
– Sqoop Committer
– Kafka
– Flume
• @gwenshap
About Me

3. 3©2014 Cloudera, Inc. All rights reserved.
There’s a book on that!

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About you:
You know Hadoop

5. “Big Data”
is stuck at
The Lab.

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We want to move to The Factory

7. 7Click to enter confidentiality information

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What does it mean to “Systemize”?
• Ability to easily add new data sources
• Easily improve and expend analytics
• Ease data access by standardizing metadata and storage
• Ability to discover mistakes and to recover from them
• Ability to safely experiment with new approaches
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We will discuss:
• Actual decision making
• Data Science
• Machine learning
• Algorithms
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We will not discuss:
• Architectures
• Patterns
• Ingest
• Storage
• Schemas
• Metadata
• Streaming
• Experimenting
• Recovery

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So how do we build
real data
architectures?
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The Data Bus

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Client Source
Data Pipelines Start like this.

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Client Source
Client
Client
Client
Then we reuse them

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Client Backend
Client
Client
Client
Then we add consumers to the
existing sources
Another
Backend

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Client Backend
Client
Client
Client
Then it starts to look like this
Another
Backend
Another
Backend
Another
Backend

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Client Backend
Client
Client
Client
With maybe some of this
Another
Backend
Another
Backend
Another
Backend

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Adding applications should be easier
We need:
• Shared infrastructure for sending records
• Infrastructure must scale
• Set of agreed-upon record schemas

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Kafka Based Ingest Architecture
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Source System Source System Source System Source System
Kafka decouples Data Pipelines
Hadoop
Security
Systems
Real-time
monitoring
Data
Warehouse
Kafka
Producer
s
Brokers
Consume
rs
Kafka decouples Data Pipelines

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Retain All Data
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Data Pipeline – Traditional View
Raw data
Raw data Clean data
Aggregated dataClean data Enriched data
Input Output
Waste of
diskspace

21. 21©2014 Cloudera, Inc. All rights reserved.
It is all valuable data
Raw data
Raw data Clean data
Aggregated dataClean data Enriched data
Filtered data
Dash
board
Report
Data
scientis
t
Alerts
OMG

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Hadoop Based ETL – The FileSystem is the
DB
/user/…
/user/gshapira/testdata/orders
/data/<database>/<table>/<partition>
/data/<biz unit>/<app>/<dataset>/partition
/data/pharmacy/fraud/orders/date=20131101
/etl/<biz unit>/<app>/<dataset>/<stage>
/etl/pharmacy/fraud/orders/validated

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Store intermediate data
/etl/<biz unit>/<app>/<dataset>/<stage>/<dataset_id>
/etl/pharmacy/fraud/orders/raw/date=20131101
/etl/pharmacy/fraud/orders/deduped/date=20131101
/etl/pharmacy/fraud/orders/validated/date=20131101
/etl/pharmacy/fraud/orders_labs/merged/date=20131101
/etl/pharmacy/fraud/orders_labs/aggregated/date=20131101
/etl/pharmacy/fraud/orders_labs/ranked/date=20131101
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Batch ETL is old news
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Small Problem!
• HDFS is optimized for large chunks of data
• Don’t write individual events of micro-batches
• Think 100M-2G batches
• What do we do with small events?
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Well, we have this data bus…
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Partition
1
Partition
2
Partition
3
Writes
Old New

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Kafka has topics
How about?
<biz unit>.<app>.<dataset>.<stage>
pharmacy.fraud.orders.raw
pharmacy.fraud.orders.deduped
pharmacy.fraud.orders.validated
pharmacy.fraud.orders_labs.merged
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28. 28©2014 Cloudera, Inc. All rights reserved.
It’s (almost) all topics
Raw data
Raw data Clean data
Aggregated dataClean data
Filtered data
Dash
board
Report
Data
scientis
t
Alerts
OMG
Enriched Data

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Benefits
• Recover from accidents
• Debug suspicious results
• Fix algorithm errors
• Experiment with new algorithms
• Expend pipelines
• Jump-start expended pipelines
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Kinda Lambda

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Lambda Architecture
• Immutable events
• Store intermediate stages
• Combine Batches and Streams
• Reprocessing
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What we don’t like
Maintaining two applications
Often in two languages
That do the same thing
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Pain Avoidance #1 – Use Spark +
SparkStreaming
• Spark is awesome for batch, so why not?
– The New Kid that isn’t that New Anymore
– Easily 10x less code
– Extremely Easy and Powerful API
– Very good for machine learning
– Scala, Java, and Python
– RDDs
– DAG Engine
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Spark Streaming
• Calling Spark in a Loop
• Extends RDDs with DStream
• Very Little Code Changes from ETL to Streaming
Confidentiality Information Goes Here

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Spark Streaming
Confidentiality Information Goes Here
Single Pass
Source Receiver RDD
Source Receiver RDD
RDD
Filter Count Print
Source Receiver RDD
RDD
RDD
Single Pass
Filter Count Print
Pre-first
Batch
First
Batch
Second
Batch

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Small Example
val sparkConf = new SparkConf()
.setMaster(args(0)).setAppName(this.getClass.getCanonicalName)
val ssc = new StreamingContext(sparkConf, Seconds(10))
// Create the DStream from data sent over the network
val dStream = ssc.socketTextStream(args(1), args(2).toInt, StorageLevel.MEMORY_AND_DISK_SER)
// Counting the errors in each RDD in the stream
val errCountStream = dStream.transform(rdd => ErrorCount.countErrors(rdd))
val stateStream = errCountStream.updateStateByKey[Int](updateFunc)
errCountStream.foreachRDD(rdd => {
System.out.println("Errors this minute:%d".format(rdd.first()._2))
})
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Pain Avoidance #2 – Split the Stream
Why do we even need stream + batch?
• Batch efficiencies
• Re-process to fix errors
• Re-process after delayed arrival
What if we could re-play data?
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Lets Re-Process with new algorithm
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Streaming App v1
Streaming App v2
Result set 1
Result set 2
App

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Lets Re-Process with new algorithm
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Streaming App v1
Streaming App v2
Result set 1
Result set 2
App

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Oh no, we just got a bunch of data for
yesterday!
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Streaming App
Streaming App
Today
Yesterday

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Note:
No need to choose between the
approaches.
There are good reasons to do both.
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Prediction:
Batch vs. Streaming distinction is going
away.
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Yes, you really need
a Schema
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Schema is a MUST HAVE for
data integration
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Client Backend
Client
Client
Client
Another
Backend
Another
Backend
Another
Backend

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Remember that we want this?
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Source System Source System Source System Source System
Hadoop
Security
Systems
Real-time
monitoring
Data
Warehouse
Kafka
Producer
s
Brokers
Consume
rs

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This means we need this:
Click to enter confidentiality information
Source System Source System Source System Source System
Hadoop
Security
Systems
Real-time
monitoring
Data
Warehouse
Kafka
Schema
Repository

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We can do it in few ways
• People go around asking each other:
“So, what does the 5th field of the messages in topic Blah contain?”
• There’s utility code for reading/writing messages that everyone
reuses
• Schema embedded in the message
• A centralized repository for schemas
– Each message has Schema ID
– Each topic has Schema ID
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I Avro
• Define Schema
• Generate code for objects
• Serialize / Deserialize into Bytes or JSON
• Embed schema in files / records… or not
• Support for our favorite languages… Except Go.
• Schema Evolution
– Add and remove fields without breaking anything
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Schemas are Agile
• Leave out MySQL and your favorite DBA for a second
• Schemas allow adding readers and writers easily
• Schemas allow modifying readers and writers independently
• Schemas can evolve as the system grows
• Allows validating data soon after its written
– No need to throw away data that doesn’t fit!
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Woah, that was lots of
stuff!
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Recap – if you remember nothing else…
• After the POC, its time for production
• Goal: Evolve fast without breaking things
For this you need:
• Keep all data
• Design pipeline for error recovery – batch or stream
• Integrate with a data bus
• And Schemas

54. Thank you