Batch querying and reporting is no longer enough for many organizations. Reducing time to insight – the time it takes to turn data into actionable insights – is becoming increasingly important to remain competitive. That’s why organizations are quickly evolving their data applications to support a broader set of real-time analytic use cases. In this webinar, we will review some of the common use cases for real-time analytics such as click-stream analysis, event data processing, and real-time analytics. We will show proven architectures for collecting, storing, and processing real-time data using a combination of AWS managed services, including Amazon Kinesis Streams, Amazon Kinesis Firehose, Amazon EMR, and AWS Lambda, as well open source tools, such as Apache Spark. Then, we will discuss common approaches and best practices to incorporate real-time analytics into your existing batch applications. Learning Objectives: • Understand how to incorporate real-time analytics into existing applications • Best practices to combine batch with real-time data flows • Learn common architectures and use cases for real-time analytics

1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Steve Abraham – Solutions Architect
May 26, 2016
May Webinar Series
Evolving Your Big Data Use Cases from
Batch to Real Time

2. Agenda
 Common Use Cases for Real-Time Analytics
 Patterns & Practices
 Tools
 Demo
 Q&A

3. Common Use Cases

4. Streaming Data Scenarios Across Verticals
Scenarios/
Verticals
Accelerated Ingest-
Transform-Load
Continuous Metrics
Generation
Responsive Data
Analysis
Digital Ad
Tech/Marketing
Publisher, bidder data
aggregation
Advertising metrics like
coverage, yield, and conversion
User engagement with ads,
optimized bid/buy engines
IoT
Sensor, device telemetry
data ingestion
Operational metrics and
dashboards
Device operational
intelligence and alerts
Gaming
Online data aggregation,
e.g., top 10 players
Massively multiplayer online
game (MMOG) live dashboard
Leader board generation,
player-skill match
Consumer
Online
Clickstream analytics
Metrics like impressions and
page views
Recommendation engines,
proactive care

5. Customer Use Cases
Sonos runs near real-time streaming
analytics on device data logs from their
connected hi-fi audio equipment.
Analyzing 30TB+ clickstream
data enabling real-time insights for
Publishers.
Zillow uses Lambda and Kinesis to
manage a global ingestion pipeline and
produce quality analytics in real-time
without building infrastructure.
Nordstorm recommendation team built
online stylist using Amazon Kinesis
Streams and AWS Lambda.

6. Patterns & Practices

7. Big Data : Batch or Stream?
Batch Processing
(Data Lake)
Real-time Processing
(Stream)
Log Analysis What went wrong an hour
ago?
Take corrective action /
notify admin
Billing Analysis How much did you spend
during the last billing
cycle?
Notify user that billing limit
is approaching
Customer Preferences Which ads were most
effective yesterday?
Which ad should the
customer see right now?
Fraud Audit trail / forensic
evidence
Stop fraud now

8. Data Streaming and Big Data
Kinesis
AWS
Lambda
Velocity
Size
Big Data
Data Streaming
Batch Processing

9. Two Main Processing Patterns
Stream processing (real time)
 Real-time response to events in data streams
Examples:
 Proactively detect hardware errors in device logs
 Notify when inventory drops below a threshold
 Fraud detection
Micro-batching (near real time)
 Near real-time operations on small batches of events in data streams
Examples:
 Aggregate and archive events
 Monitor performance SLAs

10. Data Streaming Design Pattern
Collect Process Analyze
Store
Data Collection
and Storage
Data
Processing
Event
Processing
Data
Analysis

11. Batch Layer
Amazon
Kinesis
data
process
store
Amazon
Kinesis
Firehose
Amazon S3
A
p
p
l
i
c
a
t
i
o
n
s
Amazon
Redshift
Amazon EMR
Presto
Hive
Pig
Spark
answer
Speed Layer
answer
Serving
Layer
Amazon
ES
Amazon
DynamoDB
Amazon
RDS
Amazon
ElastiCache
answer
KCL
AWS Lambda
Spark
Streaming
Storm
Batch /
Stream
Architecture

12. Primitive: Multi-Stage Decoupled “Data Bus”
 Multiple Stages
 Storage Decouples Processing Stages
Store Process Store ProcessData Answers
process
store

13. Primitive: Multi-Stream Processing
Amazon
Kinesis
AWS LambdaData
Amazon
DynamoDB
Amazon
Firehose
Amazon S3
 Read from Kinesis
 Write to Multiple Data Stores
process
store

14. Amazon EMR
Primitive: Analysis Frameworks
Amazon
Kinesis
AWS
Lambda
Amazon S3
Data
Amazon
DynamoDB
Answer
Spark
Streaming
Amazon
Firehose
Spark
SQL
 Can Read from Multiple Inputs
process
store

15. Real-time Analytics
KCL
AWS Lambda
Spark
Streaming
Apache
Storm
Amazon
SNS
Amazon
ML
Notifications
Amazon
ElastiCache
(Redis)
Amazon
DynamoDB
Amazon
RDS
Amazon
ES
Alert
App state
Real-time Prediction
KPI
Amazon
KinesisData
process
store

16. Tools

17. Plethora of Tools
Amazon
Glacier
Amazon S3
Amazon
DynamoDB
Amazon RDS
Amazon EMR
Amazon
Redshift
Amazon
Kinesis
Amazon Kinesis-
enabled app
AWS Lambda Amazon ML
Amazon SQS
Amazon
ElastiCache
DynamoDB
Streams Amazon Elasticsearch
Service
AWS IoT

19. Amazon Kinesis Firehose
Load massive volumes of streaming data into Amazon S3,
Amazon Redshift and Amazon Elasticsearch
Capture and submit
streaming data to
Firehose
Analyze streaming data using your
favorite BI tools
Firehose loads streaming data
continuously into S3, Amazon Redshift
and Amazon Elasticsearch
Zero administration: Capture and deliver streaming data into Amazon S3, Amazon Redshift and Amazon
Elasticsearch without writing an application or managing infrastructure.
Direct-to-data store integration: Batch, compress, and encrypt streaming data for delivery into data
destinations in as little as 60 secs using simple configurations.
Seamless elasticity: Seamlessly scales to match data throughput w/o intervention

20. Data Sources
App.4
[Machine
Learning]
AWSEndpoint
App.1
[Aggregate &
De-Duplicate]
App.2
[Metric
Extraction]
Amazon S3
Amazon Redshift
App.3
[Sliding
Window
Analysis]
Availability
Zone
Shard 1
Shard 2
Shard N
AWS Lambda
Amazon EMR
Availability
Zone
Availability
Zone
Amazon Kinesis Streams
Managed service for real-time streaming
Data Sources
Data Sources
Data Sources

21.  Streams are made of shards
 Each shard ingests up to 1MB/sec,
and 1000 records/sec
 Each shard emits up to 2 MB/sec
 All data is stored for 24 hours by
default; storage can be extended for
up to 7 days
 Scale Kinesis streams using scaling
utility
 Replay data inside of 24-hour window
or extended window
Amazon Kinesis Streams
Managed ability to capture and store data

22. Amazon Kinesis Firehose vs. Amazon Kinesis
Streams
Amazon Kinesis Streams is for use cases that require custom
processing, per incoming record, with sub-1 second processing
latency, and a choice of stream processing frameworks.
Amazon Kinesis Firehose is for use cases that require zero
administration, ability to use existing analytics tools based on
Amazon S3, Amazon Redshift and Amazon Elasticsearch, and a
data latency of 60 seconds or higher.

23. Streaming Data Ingestion

24. Putting Data into Amazon Kinesis Streams
Determine your partition key strategy
 Managed buffer or streaming MapReduce job
 Ensure high cardinality for your shards
Provision adequate shards
 For ingress needs
 Egress needs for all consuming applications: if more
than two simultaneous applications
 Include headroom for catching up with data in stream

25. Putting Data into Amazon Kinesis
Amazon Kinesis Agent – (supports pre-processing)
 http://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html
Pre-batch before Puts for better efficiency
 Consider Flume, Fluentd as collectors/agents
 See https://github.com/awslabs/aws-fluent-plugin-kinesis
Make a tweak to your existing logging
 log4j appender option
 See https://github.com/awslabs/kinesis-log4j-appender

26. Amazon Kinesis Producer Library
 Writes to one or more Amazon Kinesis streams with automatic,
configurable retry mechanism
 Collects records and uses PutRecords to write multiple records to
multiple shards per request
 Aggregates user records to increase payload size and improve
throughput
 Integrates seamlessly with KCL to de-aggregate batched records
 Use Amazon Kinesis Producer Library with AWS Lambda (New!)
 Submits Amazon CloudWatch metrics on your behalf to provide
visibility into producer performance

27. Record Order and Multiple Shards
Unordered processing
 Randomize partition key to distribute events over
many shards and use multiple workers
Exact order processing
 Control partition key to ensure events are grouped
into the same shard and read by the same worker
Need both? Use global sequence number
Producer
Get Global
Sequence
Unordered
Stream
Campaign Centric
Stream
Fraud Inspection
Stream
Get Event Metadata

28. Sample Code for Scaling Shards
java -cp
KinesisScalingUtils.jar-complete.jar
-Dstream-name=MyStream
-Dscaling-action=scaleUp
-Dcount=10
-Dregion=eu-west-1 ScalingClient
Options:
 stream-name - The name of the stream to be scaled
 scaling-action - The action to be taken to scale. Must be one of "scaleUp”,
"scaleDown" or “resize”
 count - Number of shards by which to absolutely scale up or down, or resize
See https://github.com/awslabs/amazon-kinesis-scaling-utils

29. Amazon Kinesis Stream Processing

30. Amazon Kinesis Client Library
 Build Kinesis Applications with Kinesis Client Library (KCL)
 Open source client library available for Java, Ruby, Python,
Node.JS dev
 Deploy on your EC2 instances
 KCL Application includes three components:
1. Record Processor Factory – Creates the record processor
2. Record Processor – Processor unit that processes data from a
shard in Amazon Kinesis Streams
3. Worker – Processing unit that maps to each application instance

31. State Management with Kinesis Client Library
 One record processor maps to one shard and processes data records from
that shard
 One worker maps to one or more record processors
 Balances shard-worker associations when worker / instance counts change
 Balances shard-worker associations when shards split or merge

32. Other Options
 Third-party connectors(for example, Splunk)
 AWS IoT platform
 AWS Lambda
 Amazon EMR with Apache Spark, Pig or Hive

33. Apache Spark and Amazon Kinesis Streams
Apache Spark is an in-memory analytics cluster using
RDD for fast processing
Spark Streaming can read directly from an Amazon
Kinesis stream
Amazon software license linking – Add ASL dependency
to SBT/MAVEN project, artifactId = spark-
streaming-kinesis-asl_2.10
Example: Counting tweets on a sliding window
KinesisUtils.createStream(‘twitter-stream’)
.filter(_.getText.contains(”Open-Source"))
.countByWindow(Seconds(5))

34. Amazon EMR
Amazon Kinesis
StreamsStreaming Input
Tumbling/Fixed Window
Aggregation
Periodic Output
Amazon Redshift
COPY from Amazon EMR
Common Integration Pattern with Amazon EMR
Tumbling Window Reporting

35. Amazon Kinesis Streams with AWS Lambda

36. Amazon Kinesis - KCL

37. Amazon Kinesis - Lambda

38. Demo

39. Key Takeaways
• Streaming vs. Batching
• Loosely Coupled
• Managed Services
• Right Tools

40. Questions?

41. Thank you!