Volume 11 | Issue 4 |

 Abstract

In recent years, cloud computing and big data analytics have become key technologies in the IT business. Surprisingly, combining the two technologies yields significant benefits for organizations. Cloud computing is transforming IT service delivery and user interaction with IT resources. Big Data is a data analysis approach made possible by recent advancements in information and communication technologies. Big data analysis demands significant computational resources, making it unaffordable for small and medium organizations. In this article, we discuss the benefits and pitfalls of delivering big data analytics using cloud computing.Weclaim that cloud computing can meet the storage and computational needs for big data analytics. Consolidating these two main technologies can boost big data mining and help organizations make better decisions. We emphasize the dangers and concerns to consider while implementing a cloud-based service model (CLaaS).

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 Keywords

Cloudcomputing;BigData;Business;Intelligence;CLaaS.

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 Abstract

The purpose of the study was to determine the impact of game specific training on selected skill performance variables of cricket players. To achieve this objective, 40 cricketers were selected from Salem District, with ages ranging from 18 to 25 years. The participants were divided into two groups: an experimental group and a control group. Pre- and post-tests were conducted on the following skill performance areas: Throw for Accuracy, Foot Movement, and Bowling Accuracy. The subjects underwent the respective experimental training for a duration of six weeks. The collected data were analyzed using a dependent t- test. The results of the study indicate a significant improvement in the selected skill performance variables. Furthermore, the findings suggest that the specific training was effective in enhancing the skill performance of the participants.

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Skill performance, throw for accuracy, foot movement and bowling accuracy

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In order to compensate for the shortcomings of wired mechanical equipment condition monitoring systems, the use of wireless sensor networks is suggested. It is crucial to construct a dual-processor wireless sensor network node in order to capture vibration signals from mechanical equipment. Developing a complete wireless sensor network mechanical vibration monitoring system also requires research into the self-organizing network, wireless transmission protocol, and on-chip processing function. The clustering network structure is suitable for large-scale networks, has strong scalability, can rapidly adjust to system changes, and is beneficial for using distributed algorithms. The issue of restricted collection and storage capacity when a wireless sensor network is used to mechanical vibration signal acquisition is partially resolved by the creation of high-performance vibration measurement nodes. The experimental findings demonstrate that while the node's time domain signal collection and amplitude will be small, the node's on-chip processing results match those of the host computer, with a small amplitude difference and a maximum error of 8.18%, suggesting that the node's performance can meet on-chip processing requirements. Experiments confirm that the design strategy is feasible.

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Monitoring, Vibration Condition, Wireless Sensor Network, Transmission Protocol.

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 Abstract

TESTING OF POLITICAL THOUGHT OF ARASTU

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TESTING OF POLITICAL THOUGHT OF ARASTU

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Tea garden along with its factory is known as tea industry. Thousands of acres of unfertile land of Dooars of North Bengal came under the ambit of the tea plantation from long past. To satisfy the growing demand of green tea leaf, the concept of Bought leaf tea factory is introduced in India. Consequently, the plain fertile area of Mekhliganj, Cooch Behar covered very rapidly by the mushroom like growth of small tea garden. This study showed that by nature, 85% the soil of this very area is, deep to very deep fine loamy & coarse loamy and suitable for crop plant like Paddy, Wheat, Jute, Tobacco and other winter vegetables etc. Tea plant needs huge amount of water, in every year a large amount of water come out of the soil. Water table of soil gradually declined. An average crop of 2000 kg made-tea/hector will remove around 100 kg nitrogen, 20 kg phosphate and 40 kg potash per hector per year from soil in addition to nutrients locked up in the bush frame. So, the growers of small holdings used to provide large amount of fertilizer mainly of chemical nature such as urea and potash to the soil to satisfy the fertilizer requirement of the tea plants. Nature of soil degrades in course of time. Unscientific use of herbicides like Glyphosate, Paraquat became the cause of complete eradication of ferns and other angiospermic herb. This paper, focus that the profit- making attitude of cultivating tea plant in plain area ultimately leads to spoil the soil nature, lowered the ground water table and loss of biodiversity of this very region.

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Tea, Bought-leaf factory, Herbicides

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 Abstract

Varn Sankar Jaatiyon ke Sansarg se Utpann Antyaj ek Sanakshipt Adhyayan

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Varn Sankar Jaatiyon ke Sansarg se Utpann Antyaj ek Sanakshipt Adhyayan

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Cloud data pipeline architectures are at the forefront of modern data engineering, enabling organizations to process, transform, and analyse vast amounts of data efficiently. As businesses increasingly adopt cloud solutions, selecting the right data pipeline architecture becomes critical to achieving optimal performance, scalability, and cost-effectiveness. This paper presents a comparative analysis of two prominent cloud data pipeline architectures: Snowflake and Azure Data Factory. Both platforms offer robust solutions for managing and orchestrating data pipelines, but they differ in their underlying technologies, capabilities, and use case suitability. Snowflake, a cloud-native data platform, has gained significant traction due to its unique architecture that decouples storage and compute, enabling elastic scaling and seamless data sharing. It supports multi-cloud deployments, offering flexibility to organizations with diverse cloud strategies. Snowflake's ability to handle structured and semi-structured data, combined with its advanced query optimization features, makes it a compelling choice for enterprises focused on high-performance analytics and data warehousing. On the other hand, Azure Data Factory (ADF) is a comprehensive data integration service within the Azure ecosystem. ADF is designed to facilitate the creation, scheduling, and management of complex data pipelines, supporting both batch and real-time data processing. As part of the Azure ecosystem, ADF seamlessly integrates with other Azure services, providing a unified platform for organizations already invested in Microsoft's cloud offerings. ADF's rich set of connectors and pre-built activities allows for easy integration with various data sources and destinations, making it a versatile tool for diverse data engineering tasks. This comparative analysis explores the strengths and weaknesses of Snowflake and Azure Data Factory across several key dimensions, including scalability, performance, ease of use, integration capabilities, cost efficiency, and security. The study delves into real-world use cases and performance benchmarks to highlight scenarios where each platform excels or faces limitations. Additionally, it examines the impact of each platform's architecture on data processing efficiency, considering factors such as data ingestion speed, transformation capabilities, and support for complex data workflows. The findings reveal that while Snowflake excels in scenarios requiring high-performance analytics and cross-cloud flexibility, Azure Data Factory offers a more integrated and cost-effective solution for organizations deeply embedded in the Azure ecosystem. The choice between Snowflake and Azure Data Factory ultimately depends on the specific needs and priorities of the organization, including factors such as existing cloud infrastructure, budget constraints, and the complexity of data workflows.

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 Keywords

Cloud Data Pipeline o Snowflake o Azure Data Factory o Data Engineering o Data Integration o Scalability o Performance o Elastic Scaling o Data Warehousing o Real-time Data Processing o Multi-cloud Deployment o Query Optimization o Data Ingestion o Cost Efficiency o Data Management

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In the era of digital transformation, optimizing enterprise resource planning (ERP) systems is pivotal for large enterprises aiming to enhance operational efficiency and competitiveness. This research paper presents a comparative analysis of strategies for optimizing SAP S/4HANA in large enterprises. SAP S/4HANA, SAP's next-generation business suite, offers significant advantages, including real-time analytics, streamlined processes, and enhanced user experiences. However, its implementation and optimization pose challenges that vary across industries and organizational structures. This study investigates these challenges and evaluates optimization strategies to provide a comprehensive understanding of the factors influencing successful SAP S/4HANA deployment. Through a systematic review of existing literature and case studies, this research identifies critical factors for optimizing SAP S/4HANA, including data migration, process re-engineering, and customization. It highlights the importance of a clear implementation roadmap, stakeholder engagement, and continuous performance monitoring. The paper compares various approaches adopted by large enterprises, such as phased versus big-bang implementation, on-premises versus cloud deployment, and the integration of artificial intelligence and machine learning for predictive analytics and decision-making. The comparative analysis reveals that a phased implementation approach, coupled with cloud deployment, tends to offer greater flexibility and scalability, allowing enterprises to adapt to evolving business needs. Additionally, the integration of AI and machine learning within SAP S/4HANA enhances predictive capabilities, enabling proactive decision-making and improved resource allocation. Customization, while beneficial for addressing specific business requirements, is identified as a double-edged sword that can lead to increased complexity and maintenance overhead if not managed judiciously. Furthermore, the study underscores the significance of change management and training programs in ensuring user adoption and minimizing disruption during the transition to SAP S/4HANA. It also emphasizes the role of continuous performance monitoring and optimization post-implementation to sustain the benefits of the new system.

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 Keywords

Digital transformation o Enterprise resource planning o SAP S/4HANA o Large enterprises o Optimization strategies o Real-time analytics o Process re-engineering o Customization o Implementation roadmap o Stakeholder engagement o Performance monitoring o Phased implementation o Cloud deployment o Artificial intelligence o Predictive analytics

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Water stands as one of the most crucial and indispensable resources for all life on Earth, including humans. However, the rampant spread of pollution and contamination poses a severe threat to its quality and availability. A significant contributor to this crisis is the unchecked disposal of untreated industrial waste into nearby bodies of water like lakes, ponds, and rivers. It's imperative that educational institutions prioritize the establishment of efficient sewage treatment facilities to manage the wastewater generated on their premises. Before constructing such a facility, a comprehensive analysis of the wastewater's characteristics is essential. Parameters such as pH level, total solids, suspended particles, hardness, alkalinity, fluoride, iron, ammonia, nitrate, and phosphate require thorough examination. The primary goal of a sewage treatment plant is to receive and process wastewater from various sources like hostels, colleges, and canteens, eliminating any harmful substances that could endanger the surrounding environment. The ultimate aim of sewage treatment is twofold: to produce a treated effluent that meets environmental safety standards and to generate treated sludge suitable for disposal or potential reuse, typically as fertilizer in agriculture. This process involves the removal of diverse pollutants including solids, organic matter, nutrients, salts, metals, and pathogens. Effective wastewater management is paramount for safeguarding both environmental integrity and public health. Various methods are employed for sewage/wastewater treatment, all aimed at reducing water and organic content. The overarching objective of wastewater management is to ensure environmental protection while addressing public health and socioeconomic considerations. This project focuses on the meticulous design of a comprehensive sewage treatment system, encompassing key components such as storage tanks, screen chambers, primary sedimentation tanks, the activated sludge process (ASP), and secondary sedimentation tanks.

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sewage, BOD, Solids, ASP, public health. Sewage

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Forests play a vital role of our planet's ecosystem and have played a very crucial role in the development of human civilization. They provide a wide range of natural resources. Forest has cultural, spiritual and recreational significance also. From the ancient times, forest is the main part of the human life. The medieval historical sources also provide ample information about the Indian forest and the covered area. Forest as symbols of power for the different states and authorities, so that rules and regulation for promoting agriculture to enhance economic resources are majorly defined by the historical sources. The present paper is based on majorly on the primary sources of the Mughal empire. In the Mughal period forests played a significant role in the economy, cultural and ecology.

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Terai forest, sarkar, suba, Doab

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