Advanced Analytics with Spark

Advanced Analytics with Spark Author Sandy Ryza
ISBN-10 9781491972908
Year 2017-06-12
Pages 280
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses

Advanced Analytics with Spark

Advanced Analytics with Spark Author Sandy Ryza
ISBN-10 9781491912737
Year 2015-04-02
Pages 276
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder

Machine Learning with Spark

Machine Learning with Spark Author Nick Pentreath
ISBN-10 9781783288526
Year 2015-02-20
Pages 338
Language en
Publisher Packt Publishing Ltd
DOWNLOAD NOW READ ONLINE

If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.

Big Data Analytics with Spark

Big Data Analytics with Spark Author Mohammed Guller
ISBN-10 9781484209646
Year 2015-12-29
Pages 277
Language en
Publisher Apress
DOWNLOAD NOW READ ONLINE

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn 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. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program 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, like Hive, Avro, Kafka and so on. 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 know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.

Learning Spark

Learning Spark Author Holden Karau
ISBN-10 9781449359058
Year 2015-01-28
Pages 276
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables

Mastering Spark for Data Science

Mastering Spark for Data Science Author Andrew Morgan
ISBN-10 9781785888281
Year 2017-03-29
Pages 560
Language en
Publisher Packt Publishing Ltd
DOWNLOAD NOW READ ONLINE

Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products About This Book Develop and apply advanced analytical techniques with Spark Learn how to tell a compelling story with data science using Spark's ecosystem Explore data at scale and work with cutting edge data science methods Who This Book Is For This book is for those who have beginner-level familiarity with the Spark architecture and data science applications, especially those who are looking for a challenge and want to learn cutting edge techniques. This book assumes working knowledge of data science, common machine learning methods, and popular data science tools, and assumes you have previously run proof of concept studies and built prototypes. What You Will Learn Learn the design patterns that integrate Spark into industrialized data science pipelines See how commercial data scientists design scalable code and reusable code for data science services Explore cutting edge data science methods so that you can study trends and causality Discover advanced programming techniques using RDD and the DataFrame and Dataset APIs Find out how Spark can be used as a universal ingestion engine tool and as a web scraper Practice the implementation of advanced topics in graph processing, such as community detection and contact chaining Get to know the best practices when performing Extended Exploratory Data Analysis, commonly used in commercial data science teams Study advanced Spark concepts, solution design patterns, and integration architectures Demonstrate powerful data science pipelines In Detail Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly. Style and approach This is an advanced guide for those with beginner-level familiarity with the Spark architecture and working with Data Science applications. Mastering Spark for Data Science is a practical tutorial that uses core Spark APIs and takes a deep dive into advanced libraries including: Spark SQL, visual streaming, and MLlib. This book expands on titles like: Machine Learning with Spark and Learning Spark. It is the next learning curve for those comfortable with Spark and looking to improve their skills.

Data Analytics with Hadoop

Data Analytics with Hadoop Author Benjamin Bengfort
ISBN-10 9781491913765
Year 2016-06-01
Pages 288
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib

Data Algorithms

Data Algorithms Author Mahmoud Parsian
ISBN-10 9781491906156
Year 2015-07-13
Pages 778
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

High Performance Spark

High Performance Spark Author Holden Karau
ISBN-10 9781491943175
Year 2017-05-25
Pages 358
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages

Mastering Apache Spark

Mastering Apache Spark Author Mike Frampton
ISBN-10 9781783987153
Year 2015-09-30
Pages 318
Language en
Publisher Packt Publishing Ltd
DOWNLOAD NOW READ ONLINE

Gain expertise in processing and storing data by using advanced techniques with Apache Spark About This Book Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan Evaluate how Cassandra and Hbase can be used for storage An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Extend the tools available for processing and storage Examine clustering and classification using MLlib Discover Spark stream processing via Flume, HDFS Create a schema in Spark SQL, and learn how a Spark schema can be populated with data Study Spark based graph processing using Spark GraphX Combine Spark with H20 and deep learning and learn why it is useful Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra Use Apache Spark in the cloud with Databricks and AWS In Detail Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

Hadoop Application Architectures

Hadoop Application Architectures Author Mark Grover
ISBN-10 9781491900079
Year 2015-06-30
Pages 400
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case. To reinforce those lessons, the book’s second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether you’re designing a new Hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process. This book covers: Factors to consider when using Hadoop to store and model data Best practices for moving data in and out of the system Data processing frameworks, including MapReduce, Spark, and Hive Common Hadoop processing patterns, such as removing duplicate records and using windowing analytics Giraph, GraphX, and other tools for large graph processing on Hadoop Using workflow orchestration and scheduling tools such as Apache Oozie Near-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache Flume Architecture examples for clickstream analysis, fraud detection, and data warehousing

Fast Data Processing With Spark

Fast Data Processing With Spark Author Holden Karau
ISBN-10 9781782167075
Year 2013-10-23
Pages 120
Language en
Publisher Packt Publishing Ltd
DOWNLOAD NOW READ ONLINE

This book will be a basic, step-by-step tutorial, which will help readers take advantage of all that Spark has to offer.Fastdata Processing with Spark is for software developers who want to learn how to write distributed programs with Spark. It will help developers who have had problems that were too much to be dealt with on a single computer. No previous experience with distributed programming is necessary. This book assumes knowledge of either Java, Scala, or Python.

Pro Spark Streaming

Pro Spark Streaming Author Zubair Nabi
ISBN-10 9781484214794
Year 2016-06-13
Pages 230
Language en
Publisher Apress
DOWNLOAD NOW READ ONLINE

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.

MapReduce Design Patterns

MapReduce Design Patterns Author Donald Miner
ISBN-10 9781449341985
Year 2012-11-21
Pages 250
Language en
Publisher "O'Reilly Media, Inc."
DOWNLOAD NOW READ ONLINE

Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide