Data Streaming With Apache Nifi

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Data Streaming With Apache Nifi

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Author : Matt Mueyon
language : en
Publisher: Independently Published
Release Date : 2024-04-14

Data Streaming With Apache Nifi written by Matt Mueyon and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-14 with Computers categories.

Unlock the full potential of data streaming and real-time data pipeline construction with "Data Streaming with Apache NiFi: Building Real-Time Data Pipelines." This authoritative guide dives deep into the world of Apache NiFi, a revolutionary open-source tool designed to automate the flow of data between systems. From basic concepts and architecture to advanced techniques and security measures, this book covers everything you need to optimize your data workflows efficiently and effectively. Structured to facilitate incremental learning, the book starts with an introduction to Apache NiFi, exploring its core components and user-friendly interface. Subsequent chapters delve into the nuances of NiFi's architecture, the intricate workings of processors, and the art of data flow management and routing. Readers will also discover the power of NiFi Expression Language, crucial for manipulating data on-the-fly, and best practices for securing sensitive data within their flows. "Data Streaming with Apache NiFi" is not just about theory; it's a practical guide replete with real-world examples, case studies, and expert insights. Whether you're new to data streaming or an experienced engineer looking to refine your skills, this book is an indispensable resource for building robust, efficient, and secure real-time data pipelines. Master the art of data ingestion, processing, and distribution across various systems with ease. Embrace the challenges of high-volume data processing and learn to troubleshoot common issues, all while ensuring your data flows are secure and compliant. Step into the future of data integration with "Data Streaming with Apache NiFi: Building Real-Time Data Pipelines." Start optimizing your real-time data pipelines today for scalability, efficiency, and reliability, and transform the way you manage data across your organization.

Advanced Data Streaming With Apache Nifi Engineering Real Time Data Pipelines For Professionals

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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-08

Advanced Data Streaming With Apache Nifi Engineering Real Time Data Pipelines For Professionals written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-08 with Computers categories.

Unlock the full potential of data streaming and real-time pipeline construction with "Advanced Data Streaming with Apache NiFi: Engineering Real-Time Data Pipelines for Professionals." This authoritative guide delves deep into the world of Apache NiFi, a revolutionary open-source tool designed to automate the flow of data between systems. From foundational concepts and architecture to advanced techniques and security measures, this book covers everything professionals need to optimize their data workflows efficiently and effectively. Structured to facilitate incremental learning, the book begins with an introduction to Apache NiFi, exploring its core components and user-friendly interface. Subsequent chapters dive into the intricacies of NiFi’s architecture, the detailed workings of processors, and the art of data flow management and routing. Readers will also uncover the power of the NiFi Expression Language for on-the-fly data manipulation and best practices for securing sensitive data within their flows. "Advanced Data Streaming with Apache NiFi" is not just theoretical; it is a practical guide filled with real-world examples, case studies, and expert insights. Whether you are new to data streaming or an experienced engineer looking to refine your skills, this book is an indispensable resource for building robust, efficient, and secure real-time data pipelines. Master the art of data ingestion, processing, and distribution across various systems with ease. Tackle the challenges of high-volume data processing and learn to troubleshoot common issues, all while ensuring your data flows are secure and compliant. Step into the future of data integration with "Advanced Data Streaming with Apache NiFi: Engineering Real-Time Data Pipelines for Professionals." Start optimizing your real-time data pipelines today for scalability, efficiency, and reliability, and transform the way you manage data across your organization.

Machine Learning For Streaming Data With Python

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Author : Joos Korstanje
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-07-15

Machine Learning For Streaming Data With Python written by Joos Korstanje 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 2022-07-15 with Computers categories.

Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features • Work on streaming use cases that are not taught in most data science courses • Gain experience with state-of-the-art tools for streaming data • Mitigate various challenges while handling streaming data Book Description Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models. What you will learn • Understand the challenges and advantages of working with streaming data • Develop real-time insights from streaming data • Understand the implementation of streaming data with various use cases to boost your knowledge • Develop a PCA alternative that can work on real-time data • Explore best practices for handling streaming data that you absolutely need to remember • Develop an API for real-time machine learning inference Who this book is for This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.

Data Engineering For Data Driven Marketing

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Author : Balamurugan Baluswamy
language : en
Publisher: Emerald Group Publishing
Release Date : 2025-03-10

Data Engineering For Data Driven Marketing written by Balamurugan Baluswamy and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-10 with Business & Economics categories.

Offering a thorough exploration of the symbiotic relationship between data engineering and modern marketing strategies, Data Engineering for Data-Driven Marketing uses a strategic lens to delve into methodologies of collecting, transforming, and storing diverse data sources.

Data Science And Security

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Author : Samiksha Shukla
language : en
Publisher: Springer Nature
Release Date : 2022-07-01

Data Science And Security written by Samiksha Shukla and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-01 with Technology & Engineering categories.

This book presents best selected papers presented at the International Conference on Data Science for Computational Security (IDSCS 2022), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during 11 – 12 February 2022. The book proposes new technologies and discusses future solutions and applications of data science, data analytics and security. The book targets current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing and cloud computing.

Data Engineering For Ai

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Author : Sundeep Goud Katta
language : en
Publisher: BPB Publications
Release Date : 2025-06-26

Data Engineering For Ai written by Sundeep Goud Katta and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-26 with Computers categories.

DESCRIPTION Data engineering is the critical discipline of building and maintaining the systems that enable organizations to collect, store, process, and analyze vast amounts of data, especially for advanced applications like AI and ML. It is about ensuring that it is reliable, accessible, and high-quality for everyone who needs it. This book provides a thorough exploration of the complete data lifecycle, starting with data engineering's development and its vital link to AI. It provides an overview of scalable data practices, from legacy systems to cutting-edge techniques. The reader will explore real-time data collection, secure ingestion, optimized storage, and dynamic processing techniques. The book features detailed discussions on ETL and ELT frameworks, performance tuning, and quality assurance that are complemented by real-world case studies. All these empower the data engineers to design systems that are seamless and integrate well with AI pipelines, driving innovation across diverse industries. By the end of this book, readers will be well-equipped to design, implement, and manage scalable data engineering solutions that effectively support and drive AI initiatives within any organization. WHAT YOU WILL LEARN ● Design real-time data ingestion and processing systems. ● Implement optimized data storage solutions for AI workloads. ● Ensure data quality, compliance in dynamically changing environments. ● Build scalable data collection methods, including for AI training data. ● Apply data engineering solutions in complex, real-world AI projects. ● Conduct SQL analytics and craft insightful, AI-driven visualizations. WHO THIS BOOK IS FOR This book is for data engineers, AI practitioners, and curious professionals with a foundational understanding of databases, programming, and ETL processes. A basic understanding of computer science concepts, cloud computing, and analytics is helpful. TABLE OF CONTENTS 1. Introduction to Data Engineering in AI 2. Managing Data Collection 3. Data Ingestion in Action 4. Data Storage in Real-time 5. Data Processing Techniques and Best Practices 6. Data Integration and Interoperability 7. Ensuring Data Quality 8. Understanding Data Analytics 9. Data Visualization and Reporting 10. Operational Data Security 11. Protecting Data Privacy 12. Data Engineering Case Studies

Ultimate Big Data Analytics With Apache Hadoop

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Author : Simhadri Govindappa
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-09-09

Ultimate Big Data Analytics With Apache Hadoop written by Simhadri Govindappa and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-09 with Computers categories.

TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index

Stream Processing With Apache Flink

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Author : Fabian Hueske
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-04-11

Stream Processing With Apache Flink written by Fabian Hueske 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 2019-04-11 with Computers categories.

Get started with Apache Flink, the open source framework that powers some of the world’s largest stream processing applications. With this practical book, you’ll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink’s DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them. Learn concepts and challenges of distributed stateful stream processing Explore Flink’s system architecture, including its event-time processing mode and fault-tolerance model Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators Read data from and write data to external systems with exactly-once consistency Deploy and configure Flink clusters Operate continuously running streaming applications

Ai And Big Data On Ibm Power Systems Servers

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Author : Scott Vetter
language : en
Publisher: IBM Redbooks
Release Date : 2019-04-10

Ai And Big Data On Ibm Power Systems Servers written by Scott Vetter and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Computers categories.

As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power SystemsTM IBM SpectrumTM Scale IBM Data Science Experience (IBM DSX) IBM Elastic StorageTM Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

Designing Scalable Fault Tolerant Distributed Systems For Cloud Storage And Data Management

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Author : Vignesh Natarajan Prof Dr. Punit Goel
language : en
Publisher: DeepMisti Publication
Release Date : 2025-01-16

Designing Scalable Fault Tolerant Distributed Systems For Cloud Storage And Data Management written by Vignesh Natarajan Prof Dr. Punit Goel and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-16 with Computers categories.

In an increasingly connected world, where data powers innovation and fuels decision-making, the importance of reliable and scalable distributed systems cannot be overstated. From cloud storage solutions to complex data management platforms, these systems form the backbone of modern computing, enabling businesses to handle massive data volumes while ensuring high availability, fault tolerance, and performance. Yet, designing and implementing such systems is a challenging task, requiring a deep understanding of distributed architectures, fault-tolerant mechanisms, and cloud-native principles. Designing Scalable, Fault-Tolerant Distributed Systems for Cloud Storage and Data Management is a comprehensive guide for engineers, architects, and technology leaders seeking to master the art of building robust distributed systems in the cloud. This book is structured to provide both theoretical foundations and practical insights, covering: • Core principles of distributed systems, including consistency, partitioning, replication, and fault tolerance. • Architectures and design patterns for building scalable cloud storage solutions. • Best practices for achieving fault tolerance, disaster recovery, and high availability. • Tools, frameworks, and cloud platforms that support distributed systems development, such as Kubernetes, Cassandra, and AWS S3. • Case studies illustrating real-world implementations and lessons learned from industry leaders. Throughout this journey, you’ll learn how to address key challenges such as managing eventual consistency, ensuring secure data access, and optimizing for both cost and performance. Whether you’re developing systems for real-time analytics, content delivery, or large-scale data processing, this book offers actionable strategies to meet the demands of today’s distributed environments. As cloud computing continues to evolve, so too must the strategies for building distributed systems. With the rise of multi-cloud deployments, edge computing, and advanced machine learning applications, the ability to design systems that are scalable, resilient, and fault-tolerant is more crucial than ever. This book is more than a technical guide—it is a companion for those who aspire to push the boundaries of what’s possible with distributed systems. By the end, you’ll not only understand the fundamental principles but also possess the confidence to design and implement systems that meet the rigorous demands of the modern digital economy. Authors