The document discusses the importance of streaming analytics for organizations to make real-time decisions by analyzing high-velocity data. It highlights the benefits of stream computing over traditional online analytical processing, showcasing applications across various industries, including healthcare and telecommunications. Notably, it mentions IBM Streams as a leading platform for implementing these analytics, offering enhanced developer productivity and efficiency.

1. A LITTLE BEE BOOK
“How it Works”
Streaming Analytics

2. This book belongs to:
A LITTLE BEE BOOK
“How it Works”
Streaming Analytics
Adapted from a variety of sources by Bob Yelland
With thanks to Avi Patwardhan & Kimberly Madia
For more copies of this book, or to read others in the series, visit: littlebeelibrary.com
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Sometimes two minutes is too late.
Organisations need to spot risks and opportunities in
high-velocity data – opportunities that often can be
detected and acted on only at a moment’s notice.
For time-sensitive processes such as thwarting
fraud, mitigating security threats or responding to
natural disasters, time is of the essence.
Real-time analytics (or stream computing) enables
continuous processing of data streams and can be
used to maximise the time value of data.
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A key difference between stream computing and
online analytical processing (OLAP) is that the
latter requires data to be “at rest” before running
analytics.
Stream computing is a processing paradigm that
brings the analytics to the data, rather than storing
the data first.
The ability to be able to analyse data in real time
shifts the conversation from how to manage big
data to how to make sense of, analyse and act on it
at high velocities.
Analysing “data in motion” leads to immediate and
accurate decision making.
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Stream computing can deliver a rapid return on
investment.
A healthcare firm realised 95% faster insight into
patient health by accelerating the execution of
complex algorithms. This saves lives by flagging the
risk of serious medical conditions. It also enables
effective targeting of patient care, thereby optimising
healthcare resources.
A utility company saved more than 700,000 gallons of
fuel and lowered costs for consumers by $24 million
by analysing the data from 2.3 million smart meters.
A telecommunication company improved marketing
effectiveness by 70% using behavioural based
segmentation to create dynamic offers.
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There are four broad ways that stream computing is
being used today:
Streaming Extract, Transform and Load (ETL) –
Data is continuously cleaned and aggregated before
being pushed into data stores.
Triggers – Anomalous behaviour is detected in real
time, and further downstream actions are triggered
accordingly.
Data enrichment – Live data is enriched with more
information by joining it with a static dataset, allowing
for a more complete real-time analysis.
Complex sessions and continuous learning –
Events related to a live session (e.g. website activity)
are grouped together and analysed.
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These uses have given rise to a number of industry
applications. For example:
Telecommunications
–– Call detail processing
–– Customer churn prediction
–– Device geomapping.
Travel and Transportation
–– Intelligent traffic management
–– Automotive telematics.
Energy and Utilities
–– Usage forecasting
–– Equipment monitoring.
Financial Services
–– Fraud detection & prevention
–– Targeted marketing
–– Cybersecurity monitoring.
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Stream Processing (ESP) and Complex Event
Processing (CEP) are very similar concepts, but there
are some important differences:
Speed through Parallelism
CEP is often centralised. Stream applications
are deployed across many nodes to maximise
parallelism and scalability.
Deeper Analysis
CEP uses a rules engine to evaluate if-then-else style
rules, or an in-memory SQL database to perform
continuous simple queries. ESP provides more
options to analyse data though a comprehensive
programming language (SPL).
Broader Data Types
CEP engines handle structured data. Streams
have been designed to analyse all manner of data,
including image, video and acoustic data types.
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Most stream computing platforms include two
core components: an application development
environment to build applications that ingest and
process data streams, and a runtime capability
designed to process data streams with low latency at
massive scale seamlessly across infrastructure.
In addition, streaming analytics toolkits improve the
productivity of developers and data scientists in
crafting complex analytics, such as natural language
processing, voice analytics and facial recognition.
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There are a number of open source offerings that
can support streaming analytics:
Apache Storm, written in Clojure, was created
by Twitter and is composed of other open source
components, especially ZooKeeper for cluster
management, ZeroMQ for multicast messaging, and
Kafka for queued messaging.
Apache Spark, written in Scala, is a general
framework for large-scale data processing that
supports lots of different programming languages
and concepts such as MapReduce, in-memory
processing, stream processing, graph processing
and machine learning.
Apache Akka is a toolkit and runtime for building
highly concurrent, distributed, and resilient
message‑driven applications on the Java Virtual
Machine (JVM).
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IBM InfoSphere Streams is an open platform that
blends the best elements of shareware, open source
software and open standards with powerful vendor-
developed technology.
IBM Streams is highly efficient, using 14.2 times fewer
hardware resources and delivering 12.3 times more
throughput compared to open source offerings.
It offers a highly scalable event server, integration
capabilities, and other typical features required for
implementing stream processing use cases.
IBM Streams is a leader in the Forrester Wave for
Big Data Analytics Platforms.
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The latest IBM Streams update focuses on developer
productivity.
The Integrated Development Environment (IDE) is
based on Eclipse and offers visual development and
configuration.
It delivers faster streaming application delivery
by allowing the creation of streaming applications
in Java.
A developer with no prior Streams knowledge can
create applications in under an hour using Java
APIs for streaming analytic libraries such as natural
language processing, spatial, temporal, acoustic,
image recognition and more.
Time is of the essence…
why not trial IBM Streams today?
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