{"id":254365,"date":"2026-07-13T03:29:00","date_gmt":"2026-07-13T00:29:00","guid":{"rendered":"https:\/\/1kitap1.com\/en\/big-data-in-practice-jitender-jain\/"},"modified":"2026-07-13T03:29:00","modified_gmt":"2026-07-13T00:29:00","slug":"big-data-in-practice-jitender-jain","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/big-data-in-practice-jitender-jain\/","title":{"rendered":"Big Data In Practice – Jitender Jain"},"content":{"rendered":"
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Log all input and output events for traceability. Separate compute from storage so scaling decisions remain independent. Combine serverless triggers with batch clusters for hybrid processing. These principles lead to systems that are responsive, cost-effective, and resilient to workload spikes. The following figure shows how serverless functions trigger autoscaling clusters that produce analytics outputs: Figure 5.3: Integration of serverless functions and autoscaling clusters in a cloud-based data architecture This flow illustrates how event-driven functions act as entry points that trigger larger jobs on autoscaling clusters.<\/p>\n

Monitoring and scaling policies work together to adjust compute resources while ensuring consistent performance. Hybrid and multi-cloud strategies for AI workloads As data systems grow in scale and complexity, many organizations choose hybrid and multi-cloud strategies instead of relying on a single provider. These approaches combine the strengths of public clouds, private data \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 centers, and sometimes multiple cloud vendors.<\/p>\n

The goal is to balance performance, cost, and compliance while ensuring that AI and analytics workloads can access data wherever it resides. A hybrid cloud combines on-premises or private infrastructure with public cloud services. It is often used when regulations or data-sovereignty rules require sensitive data to remain within company boundaries. A multi-cloud setup, on the other hand, distributes workloads across two or more public clouds such as AWS, Azure, and Google Cloud. This provides flexibility, avoids vendor lock-in, and enables teams to use the best tools from each platform.<\/p>\n

The following are the benefits of hybrid and multi-cloud models: Flexibility to place workloads where they perform best. Better resilience through geographic and provider diversity. Optimized costs by choosing the most efficient services per task. Compliance with data-residency and industry regulations. Access to specialized hardware such as graphics processing units (GPUs) or Tensor Processing Units (TPUs) for AI training. Hybrid data architecture Hybrid environments often use a mix of on-premises data warehouses and cloud data lakes connected through secure networking. Data is synchronized or replicated to ensure consistency between environments.<\/p>\n

Key components include: Secure connectivity: Virtual private networks (VPNs), direct connections, or private links connect on-prem systems to cloud platforms. Data replication: Services like AWS DataSync or Azure Data Factory move data between storage layers. \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 Consistent governance: Unified identity, encryption, and cataloging help maintain compliance. Edge processing: Small compute nodes near data sources handle preprocessing before sending data to the cloud. The following are the example scenarios: A bank that stores sensitive customer information on-premises but runs analytics on anonymized datasets in the cloud.<\/p>\n

A retail chain that processes transactions locally while sending aggregated sales trends to a cloud AI model. Multi-cloud integration for AI workloads In a multi-cloud setup, each provider contributes unique strengths to the overall architecture. For example, an organization may store data in S3, train AI models on Google Cloud GPUs, and visualize results through Azure Synapse.<\/p>\n

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All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The information contained in this book is true to correct and the best of author\u2019s and publisher\u2019s knowledge.<\/p>\n

The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information. www.bpbonline.com 1kitap1.com\/en Dedicated to My loving parents: Shri Prem Chand Jain and Shrimati Kusum Lata Jain, whose love, strength, and encouragement have been instrumental in shaping my journey and contributions.<\/p>\n

– Jitender Jain My beloved parents: Shri Ram Niwas Gupta and Shrimati Sharda Gupta, whose unwavering guidance, values, and blessings have shaped my life, my character, and my work. – Medha Gupta 1kitap1.com\/en \u2022 \u2022 About the Authors Jitender Jain is a seasoned software engineer and AI architect with over 17 years in the technology industry.<\/p>\n

He has led huge enterprise projects in the finance and retail sectors, designing end-to-end platforms that handle billions of records for mission-critical applications. Notably, the author is the inventor and has many patents, like US11893819B2, which is enhancing OCR technology through AI for large-scale unstructured data processing in real-time.<\/p>\n

This patented work, involving automated income verification from document images, exemplifies how advanced AI solutions can be built by harnessing data through big data technologies. Throughout his career, he has implemented enterprise data pipelines using API driven methodology leveraging Spark and Kafka, and has a deep understanding of how to operationalize advanced AI models in real- time environments.<\/p>\n

He has exposure to finance and retail sectors, where he built sophisticated enterprise-level software systems leveraging big data, engineering, and AI to drive business innovation. He is an influential speaker at multiple industry and academia conferences and is also a Forbes Technology Council member. Here to share practical knowledge and help professionals apply big data tools to solve real-world problems. Medha Gupta is a senior data engineer with multiple years of experience. She specializes in building scalable data pipelines and analytics platforms that support data-driven decision-making.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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