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Developing Software Applications Under the Guidance of Data-Driven Decision-Making Principles

This article underscores the vital role of data in the creation of applications that deliver precise outputs aligned with business requirements. To architect and cultivate an application that yields precise outputs in alignment with business requirements, paramount emphasis must be given to the foundational data and the pertinent data scenarios shaping the application. Software application development guided by data-driven decision-making involves identifying the critical data elements and extracting crucial insights to design an efficient and relevant application. Listed below are the key aspects essential for developing a valuable and relevant solution that effectively navigates the complexities inherent in data.

Identification of Critical Data Elements

Through a collaborative effort between business specialists and the technical team, a thorough analysis of requirements is undertaken. The primary objective is to identify the critical data elements essential for the application's success. This initial step involves delineating a list of input elements, processing elements for all processing hops, and result elements. The culmination of this process serves as the foundational basis for all subsequent stages in the data-driven development journey. This collaborative analysis ensures a holistic understanding of the requirements, fostering a seamless connection between business objectives and technical implementation. 

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Developing Intelligent and Relevant Software Applications Through the Utilization of AI and ML Technologies

The focal point of this article centers on harnessing the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) to enhance the relevance and value of software applications. The key focus of this article is to illuminate the critical aspect of ensuring the sustained relevance and value of AI/ML capabilities integrated into software solutions. These capabilities constitute the core of applications, imbuing them with intelligent and self-decisioning functionalities that notably elevate the overall performance and utility of the software. 

The application of AI and ML capabilities has the potential to yield components endowed with predictive intelligence, thereby enhancing user experiences for end-users. Additionally, it can contribute to the development of more automated and highly optimized applications, leading to reduced maintenance and operational costs. 

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An Approach to Process Skewed Dataset in High Volume Distributed Data Processing

In the modern computing landscape, we have a lot of advancement and rapid development in the field of computing. The processing paradigm is changing so frequently with so many distributed processing software available in the market. These software provides powerful features to process voluminous data with a very scalable architecture, thus providing flexibility to dynamically scale up and scale down depending on the processing needs. 

Many of these distributed software are used by organizations as part of tech modernization initiatives to replace their old legacy applications to gain benefits leveraging scalability and cloud-native features and processing data faster using modern processing techniques. 

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Solving Unique Search Requirements Using TreeMap Data Structure

TreeMap is a Java collection that structures the data in the form of an ordered key and their respective values. It has been available since JDK 1.2. Internally, TreeMap uses a red-black tree to structure the data, which is a self-balancing binary tree. The keys in TreeMap are unique identifiers, and by default, TreeMap arranges the data in a natural ordering of keys. 

For the integer keys, it is stored in the ascending order of the keys, and for the String key, the data will be stored in the alphabetic order. We can always override the default ordering of data and can inject custom sorting by using a comparator during the TreeMap creation to sort in any defined manner as per the logic provided in the comparator. We will discuss in detail a few of the custom hybrid sorting approaches along with some code examples in this article.

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An Approach To Construct Curated Processed Dataset for Data Analytics

Organizations in today’s world have increased focus on data as a tool to perform various kinds of profound analysis to extract vital outcomes, which will help organizations make the right decision to define future strategies.

For the success of an organization in today’s world, having the right data processing strategy on all the organization’s data to harness and extract different kinds of perspectives and information is becoming paramount to the success of the organization.

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Technical Approach to Design an Extensible and Scalable Data Processing Framework

Modern distributed data processing applications provide curated and succinct output datasets to the downstream analytics to produce optimized dashboarding and reporting to support multiple sets of stakeholders for informed decision-making. The output of the data processing pipeline must be pertinent to the objective as well as provide summarized and to-the-point information from the backend data processing pipeline. The middleware data processing thus becomes the backbone of these analytics applications to consume voluminous datasets from multiple upstreams and process the complex analytics logic to generate the summarized outcomes, which are then consumed by analytics engines to generate different kinds of reports and dashboards for multiple purposes. The most broadly defined objective for these analytics systems is as listed below:

  1. Forecasting application to predict the outcome for the future based on the historical trend to decide the future strategies for the organization.
  2. Reporting for senior leadership to display the performance of the organization and assess profitability.
  3. Reporting to external stakeholders for companies’ performance and future guidance.
  4. Regulatory reporting to external and internal regulators.
  5. Various kinds of compliance and risk reporting.
  6. Provide processed and summarized output to data scientists, data stewards, and data engineers to aid them in their data analysis needs.

There can be many more needs for the organization, which will require the analytics processing output to generate the summarized information, which will be consumed by the analytics application for generating the reports, charts, and dashboards.