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Practical Lakehouse Architecture

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Practical Lakehouse Architecture

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Author : Gaurav Ashok Thalpati
language : en
Publisher:
Release Date : 2024-10

Practical Lakehouse Architecture written by Gaurav Ashok Thalpati and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10 with Computers categories.

This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse

Practical Lakehouse Architecture

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Author : Gaurav Ashok Thalpati
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-07-24

Practical Lakehouse Architecture written by Gaurav Ashok Thalpati and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Computers categories.

Data Lakehouse In Action

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Author : Pradeep Menon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-17

Data Lakehouse In Action written by Pradeep Menon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-17 with Computers categories.

Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.

Practical Confluent Platform Architecture

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-14

Practical Confluent Platform Architecture written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-14 with Computers categories.

"Practical Confluent Platform Architecture" "Practical Confluent Platform Architecture" is a definitive guide for architects, engineers, and data professionals seeking to master the design and operation of enterprise-grade event streaming systems. The book begins by establishing a thorough understanding of Kafka’s evolution and its seamless integration into the Confluent Platform, meticulously explaining core concepts and architectural components such as brokers, ZooKeeper/KRaft, Kafka Connect, Schema Registry, and Control Center. Comprehensive explorations of cluster topologies—spanning single, multi-cluster, cloud-native, and hybrid deployments—lay the groundwork for architecting resilient, event-driven solutions, accompanied by thoughtful comparisons to alternative data-moving paradigms. Diving deeper, the text covers advanced cluster engineering, robust security frameworks, and rigorous schema governance practices. Readers will learn to design for high availability, optimize performance for high-throughput environments, and orchestrate cluster scaling, disaster recovery, and geo-replication strategies. A dedicated focus on security addresses all facets from encryption, authentication, and RBAC, to compliance with strict regulatory standards like GDPR and HIPAA. Practical schema management, real-time data quality monitoring, and change management strategies ensure consistent, governed data pipelines across dynamic and distributed environments. The latter chapters position readers to harness the full capabilities of Kafka’s stream processing, data integration, and operational observability. Through detailed guidance on Kafka Streams, ksqlDB, connector ecosystem architecture, and best practices for ETL pipelines and big data integrations, readers are empowered to build CI/CD-automated, self-healing event platforms. Contemporary topics—including hybrid and multi-cloud deployments, Infrastructure as Code, platform-level DevOps, and future trends such as serverless models and AI/ML integration—ensure this book is not only a comprehensive reference but also a vision for the evolving landscape of real-time data platforms.

Data Engineering With Apache Spark Delta Lake And Lakehouse

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Author : Manoj Kukreja
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-22

Data Engineering With Apache Spark Delta Lake And Lakehouse written by Manoj Kukreja and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Computers categories.

Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.

Building Medallion Architectures

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Author : Piethein Strengholt
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-03-28

Building Medallion Architectures written by Piethein Strengholt and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-28 with Computers categories.

To deliver the insights that give them a competitive advantage, organizations increasingly turn to the proven Medallion architecture. Yet implementing a robust data architecture can be difficult, particularly when it comes to using the Medallion architecture's Bronze, Silver, and Gold layers—done wrong, it can hamper your ability to make data-driven decisions. This practical guide helps you build a Medallion architecture the right way with Azure Databricks and Microsoft Fabric. Drawing on hands-on experience from the field, Piethein Strengholt demystifies common assumptions and complex problems you'll face when embarking on a new data architecture. Architects and engineers of all stripes will find answers to the most typical questions along with insights from real organizations about what's worked, what hasn't, and why. You'll learn: Learn how to build a Medallion architecture with Azure Databricks and Microsoft Fabric Gain insights from three real case studies that illustrate practical field experience and lessons learned Explore scaling considerations, including governance, security, generative AI, and more Make informed decisions when designing or implementing new data architectures Get proven patterns for success that align with broader organizational objectives

Practical Machine Learning On Databricks

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Author : Debu Sinha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-11-24

Practical Machine Learning On Databricks written by Debu Sinha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-24 with Computers categories.

Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations Key Features Learn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learn Transition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

Cloud Native Architectures For Digital Banking A Practical Guide To Building Scalable Financial Platforms With Microservices And Aws

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Author : Balkishan Arugula
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-04-19

Cloud Native Architectures For Digital Banking A Practical Guide To Building Scalable Financial Platforms With Microservices And Aws written by Balkishan Arugula and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-19 with Computers categories.

In the age of digital transformation, financial institutions are under growing pressure to deliver agile, secure, and highly scalable digital services. Cloud-Native Architectures for Digital Banking is a comprehensive, real-world guide for architects, engineers, and IT leaders seeking to modernize banking systems using microservices, cloud-native patterns, and AWS technologies. Written by veteran architect Balkishan Arugula, this book explores the full transformation journey —from legacy constraints to cloud-enabled innovation. It covers foundational cloud-native principles, microservice design, DevSecOps practices, real-time analytics, regulatory compliance, and domain-driven architecture tailored specifically for the financial services industry. With deep dives into serverless and containerized solutions, identity and access management, observability, and secure data strategies, readers will gain the tools to build robust, cost-efficient, and resilient digital banking platforms. Detailed case studies—spanning neobank implementation to legacy modernization—highlight real-world strategies that enable banks to reduce technical debt, accelerate time-to-market, and drive continuous innovation. Whether you're building from scratch or modernizing a legacy core, Cloud-Native Architectures for Digital Banking offers a strategic blueprint for achieving scalable growth, regulatory compliance, and technology excellence in today’s competitive financial landscape.

Practical Guide To H2o Ai

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-31

Practical Guide To H2o Ai written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-31 with Computers categories.

"Practical Guide to H2O.ai" "Practical Guide to H2O.ai" is a comprehensive resource designed for data scientists, machine learning engineers, and IT professionals who seek to master the full capabilities of H2O.ai’s powerful platform. This guide delivers a deep dive into the architecture and components of the H2O ecosystem—including H2O-3 and Driverless AI—while demystifying its integration within diverse enterprise environments, whether on-premises, cloud, or hybrid. Readers will gain actionable insights into secure system deployment, cluster management, large-scale data ingestion, and optimized ETL workflows, ensuring robust infrastructure that meets the demands of modern data-driven organizations. Structured to support both practical adoption and technical excellence, the book traverses core machine learning tasks, from advanced preprocessing and feature engineering to supervised and unsupervised learning with leading algorithms such as GBM, XGBoost, and deep neural networks. Special emphasis is placed on scalable automation through H2O AutoML, presenting real-world case studies while showcasing best practices in algorithm selection, hyperparameter optimization, and model evaluation. Dedicated chapters explore explainable AI and responsible ML practices—covering interpretability, bias mitigation, compliance, and data privacy—empowering readers to build transparent, auditable, and trustworthy solutions for complex, regulated domains. With detailed coverage of emerging fields like natural language processing, time series analysis, MLOps, and distributed deep learning, "Practical Guide to H2O.ai" is an indispensable reference for leveraging H2O.ai at scale. Topics such as advanced model deployment, real-time inference, CI/CD integration, and production troubleshooting combine theory with hands-on strategies for operationalizing machine learning workflows. Whether you are scaling to petabyte data, orchestrating containerized clusters, or exploring cutting-edge areas like federated learning and edge ML, this guide equips you with the knowledge and tools to drive innovation and achieve enterprise-level AI success.

Practical Data Analytics For Bfsi Leveraging Data Science For Driving Decisions In Banking Financial Services And Insurance Operations

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Author : Bharat Sikka
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2023-09-02

Practical Data Analytics For Bfsi Leveraging Data Science For Driving Decisions In Banking Financial Services And Insurance Operations written by Bharat Sikka and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with Computers categories.

Revolutionizing BFSI with Data Analytics Key Features ● Real-world examples and exercises will ground you in the practical application of analytics techniques specific to BFSI. ● Master Python for essential coding, SQL for data manipulation, and industry-leading tools like IBM SPSS and Power BI for sophisticated analyses. ● Understand how data-driven strategies generate profits, mitigate risks, and redefine customer support dynamics within the BFSI sphere. Book Description Are you looking to unlock the transformative potential of data analytics in the dynamic world of Banking, Financial Services, and Insurance (BFSI)? This book is your essential guide to mastering the intricate interplay of data science and analytics that underpins the BFSI landscape. Designed for intermediate-level practitioners, as well as those aspiring to join the ranks of BFSI analytics professionals, this book is your compass in the data-driven realm of banking. Address the unique challenges and opportunities of the BFSI sector using Artificial Intelligence and Machine Learning models for a data driven analysis. What you will learn ● Delve into the world of Data Science, including Artificial Intelligence and Machine Learning, with a focus on their application within BFSI. ● Explore hands-on examples and step-by-step tutorials that provide practical solutions to real-world challenges faced by banking institutions. ● Develop skills in essential programming languages such as Python (fundamentals) and SQL (intermediate), crucial for effective data manipulation and analysis. ● Gain insights into how businesses adapt data-driven strategies to make informed decisions, leading to improved operational efficiency. Who is this book for? This book is tailored for professionals already engaged in or seeking roles within Data Analytics in the BFSI industry. Additionally, it serves as a strategic resource for business leaders and upper management, guiding them in shaping data platforms and products within their organizations. Table of Contents 1. Introduction to BFSI and Data Driven Banking 2. Introduction to Analytics and Data Science 3. Major Areas of Analytics Utilization 4. Understanding Infrastructures behind BFSI for Analytics 5. Data Governance and AI/ML Model Governance in BFSI 6. Domains of BFSI and team planning 7. Customer Demographic Analysis and Customer Segmentation 8. Text Mining and Social Media Analytics 9. Lead Generation Through Analytical Reasoning and Machine Learning 10. Cross Sell and Up Sell of Products through Machine Learning 11. Pricing Optimization 12. Data Envelopment Analysis 13. ATM Cash Forecasting 14. Unstructured Data Analytics 15. Fraud Modelling 16. Detection of Money Laundering and Analysis 17. Credit Risk and Stressed Assets 18. High Performance Architectures: On-Premises and Cloud 19. Growing Trends in the Data-Driven Future of BFSI Index

Practical Lakehouse Architecture

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