Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

Learn the fundamentals of seven essential NoSQL databases in just one week with this book. Covering MongoDB, DynamoDB, Redis, Cassandra, Neo4j, InfluxDB, and HBase, you'll explore their functionalities and practical applications. Designed to give you a working understanding of NoSQL database types, this guide helps aspiring DBAs and developers comprehend and utilize modern data solutions. What this Book will help me do Master the fundamentals of MongoDB, including high-performance, high-availability, and scaling features. Gain hands-on experience with Neo4j to perform database queries and integrate with Python and Java applications. Learn efficient querying with Redis for storage and retrieval tasks. Understand Cassandra's powerful solution for scalable and fault-tolerant systems. Get well-versed with HBase for creating tables, and reading and writing data efficiently. Author(s) Sudarshan Kadambi and Xun (Brian) Wu bring a wealth of experience in database technologies. They have worked extensively in the software development and database management fields. With their practical and concise teaching approach, the authors make complex topics accessible for readers. Who is it for? This book is ideal for budding DBAs and developers looking to understand NoSQL databases. It is particularly useful for those transitioning from relational databases who want to learn about modern database technologies. Suitable for both beginners and those with some database knowledge, it aims to bridge skill gaps and expand the reader's technical expertise.

Mastering Apache Storm is your step-by-step guide to mastering real-time data streaming with this robust framework. You'll learn how to process big data efficiently and integrate Apache Storm with popular technologies like Kafka, HBase, and Redis to maximize its potential. This book walks you through from basic concepts to advanced implementations of Apache Storm in real-world scenarios. What this Book will help me do Understand the core features and operation of Apache Storm for real-time data streaming. Integrate Apache Storm with other Big Data frameworks like Kafka, HBase, Redis, and Hadoop. Effectively deploy and manage multi-node Apache Storm clusters in real-world environments. Monitor and analyze your data streams and system health effectively using built-in and external tools. Learn to implement fault-tolerant, scalable, and distributed stream processing applications in Apache Storm. Author(s) None Jain is an experienced software developer and technical instructor specializing in distributed systems and real-time data processing. With years of experience working with Apache Storm and related technologies, their teachings focus on practical, hands-on learning to equip readers with actionable skills. Who is it for? This book is ideal for Java developers aspiring to build expertise in real-time data streaming and distributed processing applications using Apache Storm. Beginners can start with the fundamentals provided, while those with prior knowledge can delve into intermediate and advanced implementations.

"HBase High Performance Cookbook" is your guide to mastering the optimization, scaling, and tuning of HBase systems. Covering everything from configuring HBase clusters to designing scalable table structures and performance tuning, this comprehensive book provides practical advice and strategies for leveraging HBase's full potential. By following this book's recipes, you'll supercharge your HBase expertise. What this Book will help me do Understand how to configure HBase for optimal performance, improving your data system's efficiency. Learn to design table structures to maximize scalability and functionality in HBase. Gain skills in performing CRUD operations and using advanced features like MapReduce within HBase. Discover practices for integrating HBase with other technologies such as ElasticSearch. Master the steps involved in setting up and optimizing HBase in cloud environments for enhanced performance. Author(s) Ruchir Choudhry is a seasoned data management professional with extensive experience in distributed database systems. He possesses deep expertise in HBase, Hadoop, and other big data technologies. His practical and engaging writing style aims to demystify complex technical topics, making them accessible to developers and architects alike. Who is it for? This book is tailored for developers and system architects looking to deepen their understanding of HBase. Whether you are experienced with other NoSQL databases or are new to HBase, this book provides extensive practical knowledge. Ideal for professionals working in big data applications or those eager to optimize and scale their database systems effectively.

Leverage Phoenix as an ANSI SQL engine built on top of the highly distributed and scalable NoSQL framework HBase. Learn the basics and best practices that are being adopted in Phoenix to enable a high write and read throughput in a big data space. This book includes real-world cases such as Internet of Things devices that send continuous streams to Phoenix, and the book explains how key features such as joins, indexes, transactions, and functions help you understand the simple, flexible, and powerful API that Phoenix provides. Examples are provided using real-time data and data-driven businesses that show you how to collect, analyze, and act in seconds. Pro Apache Phoenix covers the nuances of setting up a distributed HBase cluster with Phoenix libraries, running performance benchmarks, configuring parameters for production scenarios, and viewing the results. The book also shows how Phoenix plays well with other key frameworks in the Hadoop ecosystem such as Apache Spark, Pig, Flume, and Sqoop. You will learn how to: Handle a petabyte data store by applying familiar SQL techniques Store, analyze, and manipulate data in a NoSQL Hadoop echo system with HBase Apply best practices while working with a scalable data store on Hadoop and HBase Integrate popular frameworks (Apache Spark, Pig, Flume) to simplify big data analysis Demonstrate real-time use cases and big data modeling techniques Who This Book Is For Data engineers, Big Data administrators, and architects

Learn the fundamental foundations and concepts of the Apache HBase (NoSQL) open source database. It covers the HBase data model, architecture, schema design, API, and administration. Apache HBase is the database for the Apache Hadoop framework. HBase is a column family based NoSQL database that provides a flexible schema model. What You'll Learn Work with the core concepts of HBase Discover the HBase data model, schema design, and architecture Use the HBase API and administration Who This Book Is For Apache HBase (NoSQL) database users, designers, developers, and admins.

Learn how to use the Apache Hadoop projects, including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout, and Apache Solr. From setting up the environment to running sample applications each chapter in this book is a practical tutorial on using an Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects, MapReduce and HDFS, and none discusses the other Apache Hadoop ecosystem projects and how they all work together as a cohesive big data development platform. What You Will Learn: Set up the environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5 Run a MapReduce job Store data with Apache Hive, and Apache HBase Index data in HDFS with Apache Solr Develop a Kafka messaging system Stream Logs to HDFS with Apache Flume Transfer data from MySQL database to Hive, HDFS, and HBase with Sqoop Create a Hive table over Apache Solr Develop a Mahout User Recommender System Who This Book Is For: Apache Hadoop developers. Pre-requisite knowledge of Linux and some knowledge of Hadoop is required.

Dive into the world of big data with "Big Data Analytics: Real Time Analytics Using Apache Spark and Hadoop." This comprehensive guide introduces readers to the fundamentals and practical applications of Apache Spark and Hadoop, covering essential topics like Spark SQL, DataFrames, structured streaming, and more. Learn how to harness the power of real-time analytics and big data tools effectively. What this Book will help me do Master the key components of Apache Spark and Hadoop ecosystems, including Spark SQL and MapReduce. Gain an understanding of DataFrames, DataSets, and structured streaming for seamless data handling. Develop skills in real-time analytics using Spark Streaming and technologies like Kafka and HBase. Learn to implement machine learning models using Spark's MLlib and ML Pipelines. Explore graph analytics with GraphX and leverage data visualization tools like Jupyter and Zeppelin. Author(s) Venkat Ankam, an expert in big data technologies, has years of experience working with Apache Hadoop and Spark. As an educator and technical consultant, Venkat has enabled numerous professionals to gain critical insights into big data ecosystems. With a pragmatic approach, his writings aim to guide readers through complex systems in a structured and easy-to-follow manner. Who is it for? This book is perfect for data analysts, data scientists, software architects, and programmers aiming to expand their knowledge of big data analytics. Readers should ideally have a basic programming background in languages like Python, Scala, R, or SQL. Prior hands-on experience with big data environments is not necessary but is an added advantage. This guide is created to cater to a range of skill levels, from beginners to intermediate learners.

HBase is a remarkable tool for indexing mass volumes of data, but getting started with this distributed database and its ecosystem can be daunting. With this hands-on guide, you’ll learn how to architect, design, and deploy your own HBase applications by examining real-world solutions. Along with HBase principles and cluster deployment guidelines, this book includes in-depth case studies that demonstrate how large companies solved specific use cases with HBase. Authors Jean-Marc Spaggiari and Kevin O’Dell also provide draft solutions and code examples to help you implement your own versions of those use cases, from master data management (MDM) and document storage to near real-time event processing. You’ll also learn troubleshooting techniques to help you avoid common deployment mistakes. Learn exactly what HBase does, what its ecosystem includes, and how to set up your environment Explore how real-world HBase instances were deployed and put into production Examine documented use cases for tracking healthcare claims, digital advertising, data management, and product quality Understand how HBase works with tools and techniques such as Spark, Kafka, MapReduce, and the Java API Learn how to identify the causes and understand the consequences of the most common HBase issues

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

This book is a step-by-step guide for learning how to use Spark for different types of big-data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, MLlib, and Spark ML. Big Data Analytics with Spark shows you how to use Spark and leverage its easy-to-use features to increase your productivity. You learn to perform fast data analysis using its in-memory caching and advanced execution engine, employ in-memory computing capabilities for building high-performance machine learning and low-latency interactive analytics applications, and much more. Moreover, the book shows you how to use Spark as a single integrated platform for a variety of data processing tasks, including ETL pipelines, BI, live data stream processing, graph analytics, and machine learning. The book also includes a chapter on Scala, the hottest functional programming language, and the language that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Kafka, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.

Get Started Fast with Apache Hadoop ® 2, YARN, and Today’s Hadoop Ecosystem With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models. Hadoop ® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it. Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more. This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist. Coverage Includes Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduce Understanding Hadoop-based Data Lakes versus RDBMS Data Warehouses Installing Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clusters Exploring the Hadoop Distributed File System (HDFS) Understanding the essentials of MapReduce and YARN application programming Simplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBase Observing application progress, controlling jobs, and managing workflows Managing Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configuration Learning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark

Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours In just 24 lessons of one hour or less, Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours helps you leverage Hadoop’s power on a flexible, scalable cloud platform using Microsoft’s newest business intelligence, visualization, and productivity tools. This book’s straightforward, step-by-step approach shows you how to provision, configure, monitor, and troubleshoot HDInsight and use Hadoop cloud services to solve real analytics problems. You’ll gain more of Hadoop’s benefits, with less complexity–even if you’re completely new to Big Data analytics. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Practical, hands-on examples show you how to apply what you learn Quizzes and exercises help you test your knowledge and stretch your skills Notes and tips point out shortcuts and solutions Learn how to… Master core Big Data and NoSQL concepts, value propositions, and use cases Work with key Hadoop features, such as HDFS2 and YARN Quickly install, configure, and monitor Hadoop (HDInsight) clusters in the cloud Automate provisioning, customize clusters, install additional Hadoop projects, and administer clusters Integrate, analyze, and report with Microsoft BI and Power BI Automate workflows for data transformation, integration, and other tasks Use Apache HBase on HDInsight Use Sqoop or SSIS to move data to or from HDInsight Perform R-based statistical computing on HDInsight datasets Accelerate analytics with Apache Spark Run real-time analytics on high-velocity data streams Write MapReduce, Hive, and Pig programs Register your book at informit.com/register for convenient access to downloads, updates, and corrections as they become available.

If you’re a business team leader, CIO, business analyst, or developer interested in how Apache Hadoop and Apache HBase-related technologies can address problems involving large-scale data in cost-effective ways, this book is for you. Using real-world stories and situations, authors Ted Dunning and Ellen Friedman show Hadoop newcomers and seasoned users alike how NoSQL databases and Hadoop can solve a variety of business and research issues. You’ll learn about early decisions and pre-planning that can make the process easier and more productive. If you’re already using these technologies, you’ll discover ways to gain the full range of benefits possible with Hadoop. While you don’t need a deep technical background to get started, this book does provide expert guidance to help managers, architects, and practitioners succeed with their Hadoop projects. Examine a day in the life of big data: India’s ambitious Aadhaar project Review tools in the Hadoop ecosystem such as Apache’s Spark, Storm, and Drill to learn how they can help you Pick up a collection of technical and strategic tips that have helped others succeed with Hadoop Learn from several prototypical Hadoop use cases, based on how organizations have actually applied the technology Explore real-world stories that reveal how MapR customers combine use cases when putting Hadoop and NoSQL to work, including in production Ted Dunning is Chief Applications Architect at MapR Technologies, and committer and PMC member of the Apache’s Drill, Storm, Mahout, and ZooKeeper projects. He is also mentor for Apache’s Datafu, Kylin, Zeppelin, Calcite, and Samoa projects. Ellen Friedman is a solutions consultant, speaker, and author, writing mainly about big data topics. She is a committer for the Apache Mahout project and a contributor to the Apache Drill project.

Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, youâ??ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters. Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. Youâ??ll learn about recent changes to Hadoop, and explore new case studies on Hadoopâ??s role in healthcare systems and genomics data processing. Learn fundamental components such as MapReduce, HDFS, and YARN Explore MapReduce in depth, including steps for developing applications with it Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN Learn two data formats: Avro for data serialization and Parquet for nested data Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer) Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with Hadoop Learn the HBase distributed database and the ZooKeeper distributed configuration service

If your organization is about to enter the world of big data, you not only need to decide whether Apache Hadoop is the right platform to use, but also which of its many components are best suited to your task. This field guide makes the exercise manageable by breaking down the Hadoop ecosystem into short, digestible sections. You’ll quickly understand how Hadoop’s projects, subprojects, and related technologies work together. Each chapter introduces a different topic—such as core technologies or data transfer—and explains why certain components may or may not be useful for particular needs. When it comes to data, Hadoop is a whole new ballgame, but with this handy reference, you’ll have a good grasp of the playing field. Topics include: Core technologies—Hadoop Distributed File System (HDFS), MapReduce, YARN, and Spark Database and data management—Cassandra, HBase, MongoDB, and Hive Serialization—Avro, JSON, and Parquet Management and monitoring—Puppet, Chef, Zookeeper, and Oozie Analytic helpers—Pig, Mahout, and MLLib Data transfer—Scoop, Flume, distcp, and Storm Security, access control, auditing—Sentry, Kerberos, and Knox Cloud computing and virtualization—Serengeti, Docker, and Whirr

Explore insights from vast datasets with "Hadoop MapReduce v2 Cookbook - Second Edition." This book serves as a practical guide for developers and system administrators who aim to master big data processing using Hadoop v2. By engaging with its step-by-step recipes, you will learn to harness the Hadoop MapReduce ecosystem for scalable and efficient data solutions. What this Book will help me do Master the configuration and management of Hadoop YARN, MapReduce v2, and HDFS clusters. Integrate big data tools such as Hive, HBase, Pig, Mahout, and Nutch with Hadoop v2. Develop analytics solutions for large-scale datasets using MapReduce-based applications. Address specific challenges like data classification, recommendations, and text analytics leveraging Hadoop MapReduce. Deploy and manage big data clusters effectively, including options for cloud environments. Author(s) The authors behind "Hadoop MapReduce v2 Cookbook - Second Edition" combine their deep expertise in big data technology and years of experience working directly with Hadoop. They have helped numerous organizations implement scalable data processing solutions and are passionate about teaching others. Their approach ensures readers gain both foundational knowledge and practical skills. Who is it for? This book is perfect for developers and system administrators who want to learn Hadoop MapReduce v2, including configuring and managing big data clusters. Beginners with basic Java knowledge can follow along to advance their skills in big data processing. Ideal for those transitioning to Hadoop v2 or requiring practical recipes for immediate application. Great for professionals aiming to deepen their expertise in scalable data technologies.

In "Learning HBase", you'll dive deep into the core functionalities of Apache HBase and understand its applications in handling Big Data environments. By exploring both theoretical concepts and practical scenarios, you'll acquire the skills to set up, manage, and optimize HBase clusters. What this Book will help me do Understand and explain the components of the HBase ecosystem. Install and configure HBase clusters for optimized performance. Develop and maintain applications using HBase's structured storage model. Troubleshoot and resolve common issues in HBase deployments. Leverage Hadoop tools and advanced techniques to enhance HBase capabilities. Author(s) None Shriparv is a skilled technologist with a robust background in Big Data tools and application development. With hands-on expertise in distributed storage systems and data analytics, they lend exceptional insights into managing HBase environments. Their approach combines clarity, practicality, and a focus on real-world applicability. Who is it for? This book is ideal for system administrators and developers who are starting their journey in Big Data technology. With clear explanations and hands-on scenarios, it suits those seeking foundational and intermediate knowledge of the HBase ecosystem. Suitably designed, it helps students, early-career professionals, and mid-level technologists enhance their expertise. If you work in Big Data and want to grow your skill set in distributed storage systems, this book is for you.

Hbase Essentials provides a hands-on introduction to HBase, a distributed database built on top of the Hadoop ecosystem. Through practical examples and clear explanations, you will learn how to set up, use, and administer HBase to manage high-volume, high-velocity data efficiently. What this Book will help me do Understand the importance and use cases of HBase for managing Big Data. Successfully set up and configure an HBase cluster in your environment. Develop data models in HBase and perform CRUD operations effectively. Learn advanced HBase features like counters, coprocessors, and integration with MapReduce. Master cluster management and performance tuning for optimal HBase operations. Author(s) None Garg is a seasoned Big Data engineer with extensive experience in distributed databases and the Hadoop ecosystem. Having worked on complex data systems, None brings practical insights to understanding and implementing HBase. Known for a clear and approachable writing style, None aims to make learning technical subjects accessible. Who is it for? Hbase Essentials is ideal for developers and Big Data engineers keen to build expertise in distributed databases. If you have a basic understanding of HDFS or MapReduce or have experience with NoSQL databases, this book will accelerate your knowledge of HBase. It's tailored for those seeking to leverage HBase for scalable and reliable data solutions. Whether you're starting with HBase or expanding your Big Data skillset, this guide provides the tools to succeed.

How can you get your data from frontend servers to Hadoop in near real time? With this complete reference guide, you’ll learn Flume’s rich set of features for collecting, aggregating, and writing large amounts of streaming data to the Hadoop Distributed File System (HDFS), Apache HBase, SolrCloud, Elastic Search, and other systems. Using Flume shows operations engineers how to configure, deploy, and monitor a Flume cluster, and teaches developers how to write Flume plugins and custom components for their specific use-cases. You’ll learn about Flume’s design and implementation, as well as various features that make it highly scalable, flexible, and reliable. Code examples and exercises are available on GitHub. Learn how Flume provides a steady rate of flow by acting as a buffer between data producers and consumers Dive into key Flume components, including sources that accept data and sinks that write and deliver it Write custom plugins to customize the way Flume receives, modifies, formats, and writes data Explore APIs for sending data to Flume agents from your own applications Plan and deploy Flume in a scalable and flexible way—and monitor your cluster once it’s running