EDA's role in event-driven digital biz strategy

Business Growth Is
Fueled By Your
Event-Centric Digital
Biz Strategy
By Walid Aly-Hassan
Consultant @ TalentEinstein.com

Table of Contents
1. EDA’s role in your event-centric digital business strategy
2. EDA system architecture including Benefits of EDA & key
technology including Kafka, Spark & Cassandra
3. Personalization & examples of companies offering it, by
leveraging EDA, to fuel business growth
4. What Gartner, the global research & advisory firm, is
saying about EDA
By Walid Aly-Hassan Consultant @ TalentEinstein.com

1. EDA’s role in your
event-centric digital
business strategy

EDA Is A Key Technology Approach
To Delivering Your Event-Centric
Digital Biz Strategy
Digital business demands a rapid response to events, in order to gain the
competitive edge necessary to grow. Organizations must be able to respond to and
take advantage of ‘business moments’ and these real-time requirements are
driving companies to make their application software more event-driven with EDA.

What is EDA?

EDA is Event-Driven
Architecture! A key
technology to delivering
your event-centric digital biz
strategy for biz growth.

Business leaders leverage
EDA technology to
enable a necessary digital
transformation to
RETAIN & GROW
MARKET SHARE
for their business.

2. EDA system architecture
including Benefits of EDA &
key technology including
Kafka, Spark & Cassandra

Benefits Of Implementing EDA With Kafka
● Large and elastic scalability regarding nodes, volume, throughput with fault tolerance &
failover —all on commodity hardware, in any public cloud environments, or via hybrid deployments.
● Architecture flexibility Build small services, big services, sometimes still even monoliths.
● Event-driven microservices Asynchronously connected microservices model complex
business flows and move data to where it is needed.
● Openness without technology or data format lock-in The next new standard,
protocol, programming language or framework is coming for sure. The central event streaming
platform is open even if some sources or sinks use a proprietary data format or technology.
● Independent & decoupled business services managed as products, with their own
lifecycle regarding development, testing, deployment and monitoring. Loose coupling allows for
independent speed of processing between different producers and consumers, on/offline modes and
handling backpressure.
● Multi-tenancy to ensure that only the right user can create, write to and read from different data
streams in a single cluster.
● Industrialized deployment using containers, devops, etc., deployed where needed, whether
on premise, in the public cloud or in a hybrid environment. By Walid Aly-Hassan Consultant @ TalentEinstein.com

Benefits Of Spark in EDA
a. Swift Processing Using Apache Spark, we achieve a high data processing speed of about
100x faster in memory and 10x faster on the disk. This is made possible by reducing the number of
read-write to disk.
b. Dynamic in Nature We can easily develop a parallel application, as Spark provides 80
high-level operators.
c. In-Memory Computation in Spark
With in-memory processing, we can increase the processing speed. Here the data is being cached so we need
not fetch data from the disk every time thus the time is saved. Spark has DAG execution engine which
facilitates in-memory computation and acyclic data flow resulting in high speed.
d. Reusability we can reuse the Spark code for batch-processing, join stream against historical
data or run ad-hoc queries on stream state.

Benefits Of Spark in EDA (Continued…)
e. Fault Tolerance in Spark
Apacke spark provides fault tolerance through Spark abstraction-RDD. Spark RDDs (Resilient Distributed
Datasets) are designed to handle the failure of any worker node in the cluster. Thus, it ensures that the loss
of data reduces to zero.
f. Real-Time Stream Processing
Spark has a provision for real-time stream processing. Earlier the problem with Hadoop MapReduce was
that it can handle and process data which is already present, but not the real-time data. but with Spark
Streaming we can solve this problem.
g. Lazy Evaluation in Apache Spark
All the transformations we make in Spark RDD are Lazy in nature, that is it does not give the result right
away rather a new RDD is formed from the existing one. Thus, this increases the efficiency of the system.

h. Support Multiple Languages
In Spark, there is Support for multiple languages like Java, R, Scala, Python. Thus, it provides dynamicity
and overcomes the limitation of Hadoop that it can build applications only in Java.
i. Active, Progressive and Expanding Spark Community
Developers from over 50 companies were involved in making of Apache Spark. This project was initiated in
the year 2009 and is still expanding and now there are about 250 developers who contributed to its
expansion. It is the most important project of Apache Community.
j. Support for Sophisticated Analysis
Spark comes with dedicated tools for streaming data, interactive/declarative queries, machine learning
which add-on to map and reduce.

k. Integrated with Hadoop
Spark can run independently and also on Hadoop YARN Cluster Manager and thus it can read existing
Hadoop data. Thus, Spark is flexible.
l. Spark GraphX
Spark has GraphX, which is a component for graph and graph-parallel computation. It simplifies the graph
analytics tasks by the collection of graph algorithm and builders.
m. Cost Efficient
Apache Spark is cost effective solution for Big Data problem as in Hadoop large amount of storage and the
large data center is required during replication.

Benefits Of Cassandra in EDA
● Peer to Peer Architecture Cassandra follows a peer-to-peer architecture, instead of master-slave
architecture. Hence, there is no single point of failure in Cassandra. Moreover, any number of servers/nodes can be added to any
Cassandra cluster in any of the datacenters. As all the machines are at equal level, any server can entertain request from any client.
Undoubtedly, with its robust architecture and exceptional characteristics, Cassandra has raised the bar far above than other databases.
● Elastic Scalability One of the biggest advantages of using Cassandra is its elastic scalability. Cassandra cluster
can be easily scaled-up or scaled-down. Interestingly, any number of nodes can be added or deleted in Cassandra cluster without much
disturbance. You don’t have to restart the cluster or change queries related Cassandra application while scaling up or down. This is why
Cassandra is popular of having a very high throughput for the highest number of nodes. As scaling happens, read and write throughput
both increase simultaneously with zero downtime or any pause to the applications.
● High Availability and Fault Tolerance Another striking feature of Cassandra is Data
replication which makes Cassandra highly available and fault-tolerant. Replication means each data is stored at more than one location.
This is because, even if one node fails, the user should be able to retrieve the data with ease from another location. In a Cassandra
cluster, each row is replicated based on the row key. You can set the number of replicas you want to create. Just like scaling, data
replication can also happen across multiple data centres. This further leads to high level back-up and recovery competencies in
Cassandra.
● High Performance The basic idea behind developing Cassandra was to harness the hidden capabilities of
several multicore machines. Cassandra has made this dream come true! Cassandra has demonstrated brilliant performance under large
sets of data. Thus, Cassandra is loved by those organizations that deal with huge amount of data every day and at the same time cannot
afford to lose such data.

Benefits Of Cassandra in EDA (Continued…)
● Column Oriented Cassandra has a very high-level data model – this is column-oriented. It means, Cassandra
stores columns based on the column names, leading to very quick slicing. Unlike traditional databases, where column names only consist
of metadata, in Cassandra column names can also consist of the actual data. Thus, Cassandra rows can consist of masses of columns, in
contrast to a relational database that consists of a few number of columns. Cassandra is endowed with a rich data model.
● Tunable Consistency Characteristics like Tunable Consistency, makes Cassandra an incomparable database.
In Cassandra, Consistency can be of two types – Eventual consistency and Strong consistency. You can adopt any of these, based on
your requirements. Eventual consistency makes sure that the client is approved as soon as the cluster accepts the write. Whereas, Strong
consistency means that any update is broadcasted to all machines or all the nodes where the particular data is situated. You also have the
freedom to blend both eventual and strong consistency. For instance, you can go for eventual consistency in case of remote data centers
where latency is quite high and go for Strong consistency for local data centers where latency is low.
● Schema-Free Since its creation, Cassandra is famous for being a Schema-less/schema-free database in its column
family. In Cassandra, columns can be created at your will within the rows. Cassandra data model is also famously known as a
schema-optional data model. In contrast to a traditional database, in Cassandra there is no need to show all the columns needed by your
application at the surface as each row is not expected to have the same set of columns.

3. Personalization &
examples of
companies offering it,
by leveraging EDA, to
fuel business growth

Amazon & Netflix are
successfully retaining & growing
market share by offering a
Hyper-Personalized Customer
Experience driven by Big Data &
AI powered event-driven
Contextual Engagement made
possible via a EDA Platform.

It’s a testament to EDA that Netflix’s
personalization system is built on top of it
considering that the system Saves
Netflix $1Billion per
year through
reduced churn.

It’s a testament to EDA that Amazon’s
personalization system is built on top of it
considering that the system
Generates 35% of
Amazon’s Sales.

4. What Gartner, the
global research &
advisory firm, is
saying about EDA

What Gartner, the global research &
advisory firm, is saying
Leading digital
organizations
realize that EDA is
the secret sauce
to a competetive
advantage.
Gartner
Leaders that
master
“Event-driven IT”
will have the
advantage.
Gartner
EDA will become
an essential skill
in supporting
digital
transformation by
2018.
Gartner

EDA's role in event-driven digital biz strategy

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to EDA's role in event-driven digital biz strategy

Similar to EDA's role in event-driven digital biz strategy (20)

Recently uploaded

Recently uploaded (20)

EDA's role in event-driven digital biz strategy