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1.2 hoW to Fast-track Data EcosystEms
Creating a data ecosystem begins with a thorough understanding of the problem and the desired outcome. Some
of the data ecosystems make business value by eliminating high-friction stages in a procedure, and some strive to
completely disrupt a market by bringing together a diverse group of partners who can address all aspects of the
business needs. Some data ecosystems, on the other hand, provide insights that produce value for specific business
sections. Organisations can use these insights to improve the quality of the product.
In information technology, data ecosystems are the platforms that are developed to be relatively centralized and
static. Now, data is captured and used throughout organisations and IT professionals have less central control. The
infrastructure organisations use to collect data must constantly adapt and evolve. Every organisation develops its own
ecosystem, also known as a technology stack, and fills it with a patchwork of hardware and software. The best data
ecosystems are built around a product analytics platform that ties the ecosystem together.
A tech stack is the combination of technologies
a company uses to build and run an application
or project. It consists of programming languages,
frameworks, a database, front-end and back-end tools,
and applications connected via APIs.
1.3 hoW Data EcosystEm is EvoLving
In the initial phases of data science, the data used for taking business decisions or any academic purpose was
very small in volume, structured and static. The arrangement of this small and structured data was very easy using
spreadsheet programs like OpenOffice Calc, MS Excel, etc. The data analysis was done through traditional tools and
analytics generated through descriptive or predictive modelling. However, data platforms and frameworks have been
constantly evolving.
Nowadays, the size of data is increasing at Massive, Integrated & Dynamic
a remarkable speed and becoming large, Artificial Intelligence
dynamic and unstructured. This data is known Deep Networks
as big data. Professionals are looking for new
and advanced techniques to analyse this Large, Unstructured & In motion
data. They also need to learn about different Support Vector Machines
concepts like sensor-based data, Internet Machine Learning
of Things (IoT) data, machine learning and Sensor based/IOT
support vector machines. Many tools and
techniques or systems are required to get raw Small, Structured & Static
data converted into structured data and then Classifications
convert it into meaningful information as per Predictive Modelling
our requirement. Descriptive statistics
Google has been running a massive-scale Fig. 1.1
Data Ecosystems for its applications like
Search Engine, Youtube and the Ads platform. The technologies and infrastructure that Google Cloud Platform uses
is what has made Google so popular. The geographically distributed offerings performed by Google at this scale
(Big data) are enterprise ready and well-featured. Google has shown leadership in developing innovations that have
been made available to the open-source community. These innovations are being used extensively by other public
cloud vendors and Gartner clients. Examples of these include the Kubernetes container management framework,
TensorFlow machine learning platform and the Apache Beam data processing programming model.
Fig. 1.1 depicts how Data Ecosystem is evolving.
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