NiFi Fundamentals & Cookbook PDF Download
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Author: HadoopExam Learning Resources
Publisher: HadoopExam Learning Resources
ISBN:
Category : Computers
Languages : en
Pages : 130
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Book Description
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.
Author: HadoopExam Learning Resources
Publisher: HadoopExam Learning Resources
ISBN:
Category : Computers
Languages : en
Pages : 130
Get Book Here
Book Description
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.
Author:
Publisher: HadoopExam Learning Resources
ISBN:
Category : Education
Languages : en
Pages : 120
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Book Description
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
Author:
Publisher: HadoopExam Learning Resources
ISBN:
Category : Business & Economics
Languages : en
Pages : 183
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Book Description
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.
Author: Philip Pauli
Publisher: John Wiley & Sons
ISBN: 0471291870
Category : Cooking
Languages : en
Pages : 442
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Book Description
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.
Author: Neha Narkhede
Publisher: "O'Reilly Media, Inc."
ISBN: 1491936118
Category : Computers
Languages : en
Pages : 315
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Book Description
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
Author: Paul Crickard
Publisher: Packt Publishing Ltd
ISBN: 1839212306
Category : Computers
Languages : en
Pages : 357
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Book Description
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.
Author: Robert Ilijason
Publisher: Apress
ISBN: 1484257812
Category : Business & Economics
Languages : en
Pages : 281
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Book Description
Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything about configuring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloudGet started with Databricks using SQL and Python in either Microsoft Azure or AWSUnderstand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.
Author: Thomas Lathbury
Publisher:
ISBN:
Category :
Languages : en
Pages : 462
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Book Description
Author: Venkat Ankam
Publisher: Packt Publishing Ltd
ISBN: 1785889702
Category : Computers
Languages : en
Pages : 326
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Book Description
A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science
Author: Ofer Mendelevitch
Publisher: Addison-Wesley Professional
ISBN: 0134029720
Category : Computers
Languages : en
Pages : 463
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Book Description
The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language
