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Scalable Data Architecture with Java

You're reading from   Scalable Data Architecture with Java Build efficient enterprise-grade data architecting solutions using Java

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781801073080
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Sinchan Banerjee Sinchan Banerjee
Author Profile Icon Sinchan Banerjee
Sinchan Banerjee
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Table of Contents (19) Chapters Close

Preface 1. Section 1 – Foundation of Data Systems
2. Chapter 1: Basics of Modern Data Architecture FREE CHAPTER 3. Chapter 2: Data Storage and Databases 4. Chapter 3: Identifying the Right Data Platform 5. Section 2 – Building Data Processing Pipelines
6. Chapter 4: ETL Data Load – A Batch-Based Solution to Ingesting Data in a Data Warehouse 7. Chapter 5: Architecting a Batch Processing Pipeline 8. Chapter 6: Architecting a Real-Time Processing Pipeline 9. Chapter 7: Core Architectural Design Patterns 10. Chapter 8: Enabling Data Security and Governance 11. Section 3 – Enabling Data as a Service
12. Chapter 9: Exposing MongoDB Data as a Service 13. Chapter 10: Federated and Scalable DaaS with GraphQL 14. Section 4 – Choosing Suitable Data Architecture
15. Chapter 11: Measuring Performance and Benchmarking Your Applications 16. Chapter 12: Evaluating, Recommending, and Presenting Your Solutions 17. Index 18. Other Books You May Enjoy

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,...
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