This Book is published by www.HadoopExam.com (HadoopExam Learning Resources). Where you can find material and training's for preparing for BigData, Cloud Computing, Analytics, Data Science and popular Programming Language. This Book will contain 14 chapters, to cover NiFi concepts and providing 9+ use cases, so that you can understand the various fine grain detail about Apache NiFi. Also, it is recommended that you go through the NiFi Hands On Training provided by HadoopExam. In training we have created concepts as well as practicals by creating simple and complex workflow. While publishing this book there are 19 modules available, which are in-line with this book. As you know, NiFi recently become very popular to solve BigData, IOT (Internet of Things) , IOAT (Internet of Anything’s) etc. Having an exclusive skill will certainly give you edge with already lack of BigData resources. To help you HadoopExam.com brings full length Hands on training and this book to understand fundamental concepts of NiFi. We provide many Hands On session for creating simple to complex workflow/dataflow to process the data. As this is a continuously growing and fast paced technology. This technology not only helps in working BigData but also, wherever you need complex and simple DataFlow engine you can use this. NiFi can be integrated with existing technology e.g. Spark, HBase, Cassandra, RDBMS, HDFS and can even be customized as per your requirement. So start learning NiFi with HadoopExam.com premium training and book by getting subscription.

About Professional Certification of Apache Cassandra: Apache Cassandra is one of the most popular NoSQL Database currently being used by many of the organization, globally in every industry like Aviation, Finance, Retail, Social Networking etc. It proves that there is quite a huge demand for certified Cassandra professionals. Having certification make your selection in the company make much easier. This certification is conducted by the DataStax®, which has the Enterprise Version of the Apache Cassandra and Leader in providing support for the open source Apache Cassandra NoSQL database. Cassandra is one of the Unique NoSQL Database. So go for its certification, it will certainly help in - Getting the Job - Increase in your salary - Growth in your career. - Managing Tera Bytes of Data. - Learning Distributed Database - Using CQL (Cassandra Query Language) Cassandra Certification Information: - Number of questions: 60 Multiple Choice - Time allowed in minutes: 90 - Required passing score: 75% - Languages: English Exam Objectives: There are in total 5 sections and you will be asked total 60 questions in real exam. Please check each section below with regards to the exam objective 1. Apache Cassandra™ data modeling 2. Fundamentals of replication and consistency 3. The distributed and internal architecture of Apache Cassandra™ 4. Installation and configuration 5. Basic tooling

This book contains the questions answers and some FAQ about the Databricks Spark Certification for version 2.x, which is the latest release from Apache Spark. In this book we will be having in total 75 practice questions. Almost all required question would have in detail explanation to the questions and answers, wherever required. Don’t consider this book as a guide, it is more of question and answer practice book. This book also give some references as well like how to prepare further to ensure that you clear the certification exam. This book will particularly focus on the Python version of the certification preparation material. Please note these are practice questions and not dumps, hence just memorizing the question and answers will not help in the real exam. You need to understand the concepts in detail as well as you should be able to solve the programming questions at the end in real worlds work you should be able to write code using PySpark whether you are Data Engineer, Data Analytics Engineer, Data Scientists or Programmer. Hence, take the opportunity to learn each question and also go through the explanation of the questions.

Europe's most authoritative culinary reference comes to the New World A sound and comprehensive knowledge of cooking theory and technique is as essential to a great cook as a full complement of well-made kitchen tools. Based on the European culinary classic, Lehrbuch der Küche, Classical Cooking the Modern Way: Methods and Techniques provides a complete review of the most basic culinary principles and methods that recipes call for again and again. Whether used alone or with its companion volume, Classical Cooking the Modern Way: Recipes, this book is a cornerstone culinary reference that belongs in every kitchen. With everything needed to master the core repertoire of cooking methods, from grilling and broiling to braising, sautéing, and more, it explains in detail how to work with all of the main types of ingredientsincluding meat and poultry, fruits and vegetables, and pastas and grains. Contributions from 75 acclaimed European chefs offer a dynamic and informed perspective on classical cookinga fresh and contemporary look at the fundamentals with a dash of Continental flavor.

Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems

Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.

This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. --