Scalable Big Data Architecture

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-SQLto 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 tointegrate into that pattern.

This book not only gives a landscape of Big Data ecosystem, but will guide the readers on the reasons to use a project regarding a Big Data use case, as well A step by step guide that will walk you through common Big Data patterns--helping you to understand the context & perimeter one should focus on for their specific needs This book help the readers to get visibility on how Big Data can solve data processing problem through real industry use cases The readers will understand the limits of each pattern, and then will be able to compose them into a heterogeneous architecture Understanding the fundamentals of machine learning and how to handle it

Autorentext

Bahaaldine Azarmi is the co-founder and CTO of reach five, a Social Data Marketing Platform. Bahaaldine has a strong background and expertise skills in REST API and Big Data architecture. Prior to founding reach five, Bahaaldine worked as a technical architect & evangelist for large software vendors such as Oracle & Talend. He has a master’s degree of computer science from Polytech’Paris engineering school, Paris.

Inhalt

Chapter 1: I think I have a Big (data) Problem. - Chapter 2: Early Big Data with No-SQL. - Chapter 3: Big Data processing jobs topology. - Chapter 4: Big Data Streaming Pattern. - Chapter 5: Querying and Analysing Patterns. - Chapter 6: How About Learning from your Data?. - Chapter 7: Governance Considerations

<p