Read book Progressive Big Data Architecture : A Practitioners Guide to Choosing Relevant Big Data Architecture by Bahaaldine Azarmi TXT, EPUB, PDF
9781484213278 English 1484213270 Most people think that Big Data projects start directly with the deployment of large distributed clusters of heavy map reduce jobs, whereas reality shows that there isn't any unique/perfect solution to solving problems when dealing with large volumes of data. By knowing the different Big Data integration patterns, you will understand why most of the time you will have to deploy a heterogeneous architecture that fulfills different needs, and furthermore what limits each pattern that may lead you to choose effective alternates. We will go through real concrete industry use cases that leverage these patterns such as REST API which requests large amount of data stored in No-SQL like Couchbase and Elasticsearch. We will see how massive data processing can be done in such No-SQL databases without the need of diving deep into Big Data. But when the volume is too high and the data structures gets too complex, the kind of pattern being employed reaches its limits and that's when we can start thinking of delegating complex data processing jobs to, for example, a Hadoop based Big Data architecture. The difficulty is to then choose a relevant combination of big data technologies available within the Hadoop ecosystem. We will focus on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern will be illustrated with practical examples, which uses the different apache projects such as Avro, Spark, Kafka, and so on. Traditional Big Data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book will also help you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints implied by dealing with high throughput of Big data. What you'll learn The difference between fundamentals Big Data patterns Leveraging No-SQL databases Big Data features Common Big Data enterprise patterns Choosing the best set tools available within the hadoop system for the your Big Data set up. How to employ machine learning earlier on in the project once the data being processed becomes substantially large. How to enhance the visibility and to monitor your Big Data Architecture through effective governance. Who this book is for Progressive Big Data Architecture is for developers, data architects, data scientists looking for a better understanding of how to choose the most relevant architecture/pattern for a Big Data project and also what are the tools and projects, which should be integrated in this pattern., This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.
9781484213278 English 1484213270 Most people think that Big Data projects start directly with the deployment of large distributed clusters of heavy map reduce jobs, whereas reality shows that there isn't any unique/perfect solution to solving problems when dealing with large volumes of data. By knowing the different Big Data integration patterns, you will understand why most of the time you will have to deploy a heterogeneous architecture that fulfills different needs, and furthermore what limits each pattern that may lead you to choose effective alternates. We will go through real concrete industry use cases that leverage these patterns such as REST API which requests large amount of data stored in No-SQL like Couchbase and Elasticsearch. We will see how massive data processing can be done in such No-SQL databases without the need of diving deep into Big Data. But when the volume is too high and the data structures gets too complex, the kind of pattern being employed reaches its limits and that's when we can start thinking of delegating complex data processing jobs to, for example, a Hadoop based Big Data architecture. The difficulty is to then choose a relevant combination of big data technologies available within the Hadoop ecosystem. We will focus on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern will be illustrated with practical examples, which uses the different apache projects such as Avro, Spark, Kafka, and so on. Traditional Big Data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book will also help you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints implied by dealing with high throughput of Big data. What you'll learn The difference between fundamentals Big Data patterns Leveraging No-SQL databases Big Data features Common Big Data enterprise patterns Choosing the best set tools available within the hadoop system for the your Big Data set up. How to employ machine learning earlier on in the project once the data being processed becomes substantially large. How to enhance the visibility and to monitor your Big Data Architecture through effective governance. Who this book is for Progressive Big Data Architecture is for developers, data architects, data scientists looking for a better understanding of how to choose the most relevant architecture/pattern for a Big Data project and also what are the tools and projects, which should be integrated in this pattern., This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.