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Key benefits

Learn how to adapt to the ever-evolving data architecture technology landscape

Understand how to choose the best suited technology, platform, and architecture to realize effective business value

Implement effective data security and governance principles

Description

Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data. This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert. You’ll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you’ll understand how to architect a batch and real-time data processing pipeline. You’ll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you’ll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics. By the end of this book, you’ll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.

What you will learn

Analyze and use the best data architecture patterns for problems

Understand when and how to choose Java tools for a data architecture

Build batch and real-time data engineering solutions using Java

Discover how to apply security and governance to a solution

Measure performance, publish benchmarks, and optimize solutions

Evaluate, choose, and present the best architectural alternatives

Understand how to publish Data as a Service using GraphQL and a REST API

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Hands-On Software Architecture with Java

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Domain-Driven Design with Java - A Practitioner's Guide

€31.99

Total € 109.97

Amazon Customer Nov 29, 2022

I thoroughly enjoyed reading this book. This is one of the best technical book on how to Architect the right Batch or Real time Data Pipelines. It help us to understand the core design Patterns and DAAS implementations . Had an opportunity of closely collaborating with Sinchan(Author) on multiple projects in the past few years, With confidence of the content published with respect to data scalability, optimization, security and performance strategies mentioned in the book, this will definitely assist us in all our existing and upcoming successful implementations/deliverables. This is an essential book for all the aspiring Data Architects.

Amazon Verified review

Geraldo Netto Jan 25, 2023

First things first, it goes from the ground up even the big picture of current data architecture/engineering and its different roles.Then, it moves to the most fundamental concepts on data and databases like data types and formats, a bit of storage, the classical data warehouse, a bit of relational and nosql including its modelingThen, we have a bit of cloud/hadoopAt this point, we reach the data pipeline going from classical ETL, batch and stream processing, including patterns for it - plain beauty, really!!!Data security and governance - certainly one of the areas that people most neglect, unfortunatelyThe third part is about data as a service discussing MongoDB, Spring boot, GraphQL with use casesAnd by the very end of the book, we still have guidelines for performance tuning and how to present/'sell' your solutionSo, the book simply covers the process end-to-endSome highlights:- it covers the whole data workflow- it's use case based- software/tools and patterns are widely deployed meaning that data architect job will actually touch one (if not all) of the software mentioned through the book- a derivation of above, it's easier to get help with popular software/tools than 'esoteric' or very new toolsOn the other side (maybe for a second edition :))- maybe discuss a bit more about parsing data specially the trick ones like multi-line logs- on performance tuning would be interesting to have more details on other GC algos like ZGC which is the default GC algo for JDK 15 and promises to deliver a fairly decent performance under millisecond for really large heaps- while the discussion on lambda pattern is brilliant, it would be interesting to discuss a bit more about the CAP theorem and PACELC theorem (an extension of CAP)Overall, a great introduction for data architecture on java

Amazon Verified review

JJ Jan 25, 2023

I found that the Author has a clear flow through the data engineering concepts, step by step adaptation and DaaS layer. It has covered the details we will need during implementation clearly, instead of a bird's eye view like most books. I would recommend for my colleagues

Amazon Verified review

Steven Fernandes Jan 06, 2023

Section 1 of the book starts with a short introduction to data engineering, the basic concepts of data engineering, and the role a Java data architect plays in data engineering. It then briefly discusses various data types, storage formats, data formats, and databases. It also discusses when to use them.It also explains how to identify the Right Data Platform, provides an overview of various platforms to deploy data pipelines, and how to choose the correct platform.Section 2 of the book gives an overview of ETL data load. It discusses how to approach, analyze, and architect an effective solution for a batch-based data ingestion problem using Spring Batch and Java. Next, it focuses on architecting a batch processing pipeline and discusses how to architect and implement a data analysis pipeline in AWS using S3, Apache Spark (Java), AWS Elastic MapReduce (EMR), and AWS Athena for a big data usecase. The book also provides a step-by-step guide to building a real-time streaming solution to predict the risk category of a loan application using Java, Kafka, andrelated technologies. It discusses common architectural patterns used to solve data engineering problems and when to use them. Section 2 ends with data governance and outlines how to apply it using a practical use case. It also briefly touches upon the topic of data security.Section 3 provides a step-by-step guide on building Data as a Service to expose MongoDB data using a REST API. It also discusses GraphQL, various GraphQL patterns, and how to publish data using GraphQL. The final section, 4, provides an overview of performance engineering, how to measure performance and create benchmarks, and how to optimize performance. It discusses evaluating and choosing the best-suited alternative among various architectures and how to present the recommended architecture effectively.

Amazon Verified review

POE Feb 09, 2023

This is a well-written Java book that focuses on a disparate set of topics. These topics include design patterns, data architecture (storage, databases, etc.), data processing pipelines, data security, benchmarks, data-as-a-service, and more. The book is written for seasoned Java developers. Technologies featured in the book include various AWS products, GraphQL. Kafka, Hadoop, and REST API. This book is a good edition to your Java library if you want to strengthen your data architecture knowledge with Java.

Amazon Verified review

Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java

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