Streaming Data and Stream Processing with Apache Kafka

11
Streaming Data and
Stream Processing with
Apache Kafka™
David Tucker, Director of Partner Engineering,
Confluent
Sid Goel, Partner and Solution Architect, KPI Partners

33
The opportunity: The shift to streams & digital transformation
By 2020, 70% of
organizations will adopt
data streaming to enable
real-time analytics.
- Gartner | Nov 2016
Streaming ingestion and
analytics will become a
must-have for digital
winners.
- Forrester | Nov.
2015

44
More Facts & Figures
90% of CEO’s believe the digital economy will have a major
impact on their industry.
- MITSloan / Capgemini (2013)
#1 most important capability executives hope to improve via
digital transformation: Ability to support real-time
transactions.
- The Economist (2015)
Digital disruptors will displace 40% of incumbent
companies over the next 5 years.
- Center forDigital Transformation (2015)

55
Vision of a Streaming Enterprise
Search
NewSQL / NoSQL
RDBMS Monitoring
Document StoreReal-time Analytics Data Warehouse
Mobile Apps
Legacy Apps
Hadoop
Streaming Platform

66
What Can You Do with a Streaming Platform ?
• Publish and Subscribe to streams of data
• Analogous to traditional messaging systems
• Store streams of data
• Consumers can look back in time
• Process streams of data
• Analyze and correlate events in real time

77
The typical architecture
Search Security
Fraud Detection Application
User Tracking Operational Logs Operational Metrics
Data Warehouse
App
Databases
Storage
Interfaces
Monitoring App
Databases
Storage
Interfaces

88
Challenges abound
Search Security
Hadoop
Data
Warehouse
App
Databases
Storage
Interfaces
Monitoring
App
Databases
Storage
Interfaces
Diverse data sets, arriving at
an increasing rate
Many complex
data pipelines
Require a separate cluster
for real-time
Difficult & time consuming
to change
Require mission critical
availability into most
recent/relevant data
Difficult to handle
massive amounts
of data

99
Modernized architecture using Apache Kafka
Search Security
Streams API
App
Streams API
Monitoring
App Data
Warehouse

1010
Search Security
Streams API
App
Streams API
Monitoring
App Data
Warehouse
Modernized architecture using Apache Kafka
Pub/sub to data streams,
alleviate back pressure
Lightweight, easy to modify
with minimal disruption
Decoupled from upstream
apps creating agility
Real-time, context specific
data in the moment
Handle any
volume of data
with ease Scale to meet demands of
diverse streams

1111
Stream Data is
The Faster the Better
Stream Data can be
Big or Fast (Lambda)
Stream Data will be
Big AND Fast
(Kappa)
Our vision: from big data to stream data
Apache Kafka is the Enabling Technology of this Transition
Big Data was
The More the Better
ValueofData
Volume of Data
ValueofData
Age of Data
Job 1 Job 2
Streams
Table 1 Table 2
DB
Speed Table Batch Table
DB
Streams Hadoop

1212
Kafka Adoption in Large Enterprises Growing Rapidly
Travel Global Banks Insurance Telecom
6 of top 10 7 of top 10 8 of top 10 9 of top 10
Over 35% of the Fortune 500 are using Apache
Kafka™

1313
Industries & Use Cases
Universal Use Cases: IoT, Data Pipelines, Microservices, Monitoring
Industry Use Cases
Financial Services Fraud Detection, Trade Data Capture, Customer 360
Retail Inventory Management, Product Catalog, A/B Testing, Proactive Alerts
Automotive Connected Car, Manufacturing Data Processing
Enterprise Tech Analytics, Security Operations, Collect Performance Data
Telecom Personalized Ad Placement, Customer 360, Network Integrity Systems
Entertainment/Media Log Delivery, Increase Ad Delivery Operations, Cross-Device Insights
Travel/ Leisure Visitor Segmentation, Fraud Detection
Consumer Tech Streaming Video, Personalized Customer Experience, Device Telemetry and Analytics
Healthcare Patient Monitoring, Pharma Substance control, Patient Relapse, Lab Results Alerts

1515
Kafka Adoption Across Key Companies
Financial Services Enterprise Tech Consumer Tech
Entertainment & Media Telecom Retail Travel & Leisure

1616
Confluent Enterprise
The only enterprise streaming platform
based entirely on Apache KafkaTM

1717
Confluent Platform: Enterprise Streaming based on Apache Kafka™
Database
Changes
Log Events loT Data
Web
Events
…
CRM
Data Warehouse
Database
Hadoop
Data
Integration
…
Monitoring
Analytics
Custom Apps
Transformations
Real-time
Applications
…
Apache Open Source Confluent Open Source Confluent Enterprise
Confluent Platform
Apache Kafka™
Data Compatibility
Monitoring & Administration
Operations
Clients Connectors
Complete Open Trusted Enterprise Grade

1818
Feature Benefit Apache Kafka Confluent Open Source Confluent Enterprise
Apache Kafka
High throughput, low latency, high availability, secure distributed streaming
platform
Kafka Connect API Advanced API for connecting external sources/destinations into Kafka
Kafka Streams API
Simple library that enables streaming application development within the Kafka
framework
Additional Clients Supports non-Java clients; C, C++, Python, etc.
REST Proxy
Provides universal access to Kafka from any network connected device via
HTTP
Schema Registry
Central registry for the format of Kafka data – guarantees all data is always
consumable
Pre-Built Connectors
HDFS, JDBC, elasticsearch and other connectors fully certified
and fully supported by Confluent
Confluent Control Center Enables easy connector management and stream monitoring
Auto Data Balancing Rebalancing data across cluster to remove bottlenecks
Replication Multi-datacenter replication simplifies and automates MDC Kafka clusters
Support
Enterprise class support to keep your Kafka environment running at top
performance
Community Community 24x7x365
Confluent Completes Kafka

1919
How do I get streams of data
into and out of my apps?
Connect Clients REST

2020
Apache KafkaTM Connect – Streaming Data Capture
JDBC
IRC / Twitter
CDC
Elastic
NoSQL
HDFS
KafkaConnectAPI
KafkaPipeline
Connector
Connector
Connector
Connector
Connector
Connector
Sources Sinks
Fault tolerant
Manage hundreds of data sources
and sinks
Preserves data schema
Part of Apache Kafkaproject
Integrated within Confluent
Platform’s Control Center

2121
Kafka Connect API, Part of the Apache KafkaTM Project
Connect any source to any target system with Apache Kafka
Integrated
• 100% compatible with Kafka v0.9 and
higher
• Integrated with Confluent’s Schema
Registry
• Easy to manage with Confluent Control
Center
Flexible
• 40+ open source connectors available
• Easy to develop additional connectors
• Flexible support for data types and
formats
Compatible
• Maintains critical metadata
• Preserves schema information
• Supports schema evolution
Reliable
• Automated failover
• At-least-once guaranteed
• Balances workload between nodes

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Kafka Connect API Library of Connectors
* Denotes Connectors developed at Confluent and distributed by Confluent. Extensive validation and testing have been performed.
Databases
*
Analytics
*
Applications / Other
Datastore/File Store
*
*

2323
New in Kafka 0.10.2: Single Message Transforms for Kafka Connect
Modify events before storing
in Kafka:
• Mask sensitive information
• Add identifiers
• Tag events
• Store lineage
• Remove unnecessary columns
Modify events going out of
Kafka:
• Route high priority events to
faster data stores
• Direct events to different
ElasticSearch indexes
• Cast data types to match
destination
• Remove unnecessary columns

2424
Kafka Clients
Ruby Proxy http/REST
Stdin/stdout
Apache Kafka Native Clients
Confluent Native Clients
Community Supported Clients

2525
REST Proxy: Talking to Non-native Kafka Apps and Outside the Firewall
REST Proxy
Non-Java Applications
Native Kafka Java
Applications
Schema Registry
REST / HTTP
Simplifies
administrative actions
Simplifies message
creation and consumption
Provides a RESTful
interface to a Kafka
cluster

2626
How do I maintain my data
formats and ensure compatibility?

2727
The Challenge of Data Compatibility at Scale
App 1
App 2
App 3
Many sources without a policy
causes mayhem in a centralized
data pipeline
Ensuring downstream systems
can use the data is key to an
operational stream pipeline
Example: Date formats
Even within a single application,
different formats can be
presented
Incompatibly formatted message

2828
Schema Registry
Elastic
Cassandra
HDFS
Example Consumers
Serializer
App 1
Serializer
App 2
!
Kafka Topic!
Schema
Registry
Define the expected fields for each Kafka topic
Automatically handle schema changes (e.g. new fields)
Prevent backwards incompatible changes
Supports multi-datacenter environments

2929
How do I build stream
processing apps?

3030
Kafka Streams API: the Easiest Way to Process Data in Apache Kafka™
Example Use Cases
• Microservices
• Large-scale continuous queries and transformations
• Event-triggered processes
• Reactive applications
• Customer 360-degree view, fraud detection, location-
based marketing, smart electrical grids, fleet
management, …
KeyBenefits of ApacheKafka’s Streams API
• Build Apps, Not Clusters: no additional cluster required
• Elastic, highly-performant, distributed, fault-tolerant,
secure
• Equally viable for small, medium, and large-scale use
cases
• “Run Everywhere”: integrates with your existing
deployment strategies such as containers, automation,
cloud
Your App
Kafka
Streams
API

3131
Architecture Example
Before: Complexity for development and operations, heavy footprint
1 2 3
Capture business
events in Kafka
Must process events with separate,
special-purpose clusters
Write results
back to Kafka
Your Processing Job

3232
Architecture Example
WithKafkaStreams:App-centric architecture that blends well into your existing infrastructure
1 2
3a
Capture business
events in Kafka
Process events fast, reliably, securely with
standard Java applications
Write results
back to
Kafka
3b
Query latest results directly from
external apps
AppApp
Your App
Kafka
Streams API

3333
New in Kafka 0.10.2 : Session windows in Kafka Streams API
Groupevents in astream basedon
session windows
• Sessions are periods of
activity terminated by a
gap of inactivity
• Purely time-based windows
are incorrect for session-
based data analysis
Input data
Colors represent
different users
event
Results
User sessions,
grouped by
event-time
session windows
processing-time
event-time
session windowing
Alice
Bob
Dave

3535
How do I synchronize and migrate data
to and from the cloud?

3636
Before:Hybrid Cloud Environments Today
DC1
DB2
DB1
DWH
App2
App3
App4
KV2KV3
DB3
App2-v2
App5
App7
App1-v2
AWS
App8
DWH
App1
Challenges
• Each team/department
must execute their own cloud
migration
• May be moving the same data
multiple times
• Each box represented here
require development, testing,
deployment, monitoring and
maintenance
KV

3737
DC1
After: Cloud Synchronization and Migrations with Confluent Platform
DB2
DB1
KV
DWH
App2
App4
KV2KV3
App2-v2
App5 App7
App1-v2
AWS
App8
DWH
App1
Kafka
Kafka
App3
Benefits
• Continuous low-latency
synchronization
• Centralized manageability and
monitoring
– Track at event level data
produced in all data centers
• Security and governance
– Track and control where data
comes from and who is
accessing it
• Cost Savings
– Move Data Once
DB3

3838
How do I manage and monitor
my streaming platform at scale?

3939
What Does End-to-End Mean?
“Clocks and Cables” Monitoring
How fast is the throughput?
How many CPU cycles are we
using?
End-to-End Monitoring
Did
you
leave?
Did
you
arrive?

4040
Confluent Control Center: Cluster Health & Administration
Cluster health dashboard
• Monitor the health of your Kafka clusters
and get alerts if any problems occur
• Measure system load, performance,
and operations
• View aggregate statistics or drill down
by broker or topic
Cluster administration
• Monitor topic configurations

4141
Confluent Control Center: End-to-end Monitoring
See exactly where your messages are going in your Kafka cluster

4242
Confluent Control Center: Connector Management

4343
Confluent Control Center: Alerting
Alerts
• Configure alerts on incomplete data
delivery, high latency, Kafka connector
status, and more
• Manage alerts for different users and
applications from a web UI
• Manage alerts for different users and
applications from a web UI
User authentication
• Control access to Confluent Control
Center
• Integrates with existing enterprise
authentication systems

4444
Auto Data Balancing
Dynamically move
partitions to optimize
resource utilization and
reliability
• Easily add and remove
nodes from your Kafka
cluster
• Rack aware algorithm
rebalances partitions
across
a cluster
• Traffic from balancer is
throttled when data
transfer occurs
Before
After
Rebalanc
e

4545
Multi-Datacenter Replication
An easy reliable way to run Kafka across datacenters
Improve reliability
• Easily configure & maintain cross
cluster replication
Simplify management
• Centralized configuration and
monitoring
• Replicate entire cluster or a subset of
topics
• Automatic replication of topic
configuration
• Use Kafka’s SASL for Kerberos,
Active Directory
• SSL encryption between datacenters

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Get Started with Apache Kafka Today!
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THE place to start with Apache Kafka!
Thoroughly tested and
quality assured
More extensible developer
experience
Easy upgrade path to
Confluent Enterprise

Streaming Data and Stream Processing with Apache Kafka

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