Page 102 - Data Science class 11
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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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