Book Image

Scalable Data Architecture with Java

By : Sinchan Banerjee
Book Image

Scalable Data Architecture with Java

By: Sinchan Banerjee

Overview of this book

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.
Table of Contents (19 chapters)
1
Section 1 – Foundation of Data Systems
5
Section 2 – Building Data Processing Pipelines
11
Section 3 – Enabling Data as a Service
14
Section 4 – Choosing Suitable Data Architecture

Querying the ODL using AWS Athena

In this section, we will learn how to perform data querying on the ODL that we have created using our architecture. We will focus on how to set up Athena on our output folder to do easy data discovery and querying:

  1. Navigate to AWS Athena via the AWS Management Console. Click on Explore the query editor. First, go to the Manage settings form of the Query editor area and set up an S3 bucket where the query results can be stored. You can create an empty bucket for this purpose:

Figure 5.26 – Setting up AWS Athena

  1. We will create an Athena table on top of our S3 output bucket. For this, we will create a DDL to create a table called ecom_odl, which is a partitioned table on the year and month columns. The DDL of the table can be seen in the following code snippet:
    CREATE EXTERNAL TABLE IF NOT EXISTS ecom_odl(
         category_id bigint,
         product_id bigint,...