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DynamoDB Applied Design Patterns

DynamoDB Applied Design Patterns

By : Uchit Hamendra Vyas
3 (2)
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DynamoDB Applied Design Patterns

DynamoDB Applied Design Patterns

3 (2)
By: Uchit Hamendra Vyas
Buy this Book

Overview of this book

If you are an intermediate to advanced DynamoDB developer looking to learn the best practices associated with efficient data modeling, this book is for you.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Modeling with DynamoDB
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1. Data Modeling with DynamoDB
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Data model
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Efficient use of primary keys
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Basics of indexes
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DynamoDB data types
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Summary
2
2. DynamoDB Interfaces
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2. DynamoDB Interfaces
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The management console
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The Eclipse plugin
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The command-line interface
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Summary
3
3. Tools and Libraries of AWS DynamoDB
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3. Tools and Libraries of AWS DynamoDB
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Creating your first SDK project
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Java SDK operations
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DynamoDB Local
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Summary
4
4. Working with Secondary Indexes
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4. Working with Secondary Indexes
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Secondary indexes
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Projection
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Item sharding
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Best practices with secondary indexes
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Summary
5
5. Query and Scan Operations in DynamoDB
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5. Query and Scan Operations in DynamoDB
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Querying tables
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Scanning tables
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Parallel scanning
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Summary
6
6. Working with the DynamoDB API
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6. Working with the DynamoDB API
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Data format
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HTTP requests
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Error handling
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Operations in DynamoDB
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Summary
7
7. Distributed Locking with DynamoDB
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7. Distributed Locking with DynamoDB
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Distributed locking
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Importance of distributed locking
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Summary
8
8. DynamoDB with Redshift, Data Pipeline, and MapReduce
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8. DynamoDB with Redshift, Data Pipeline, and MapReduce
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Loading data from DynamoDB into Redshift
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Importing and exporting data between DynamoDB and S3
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Exporting, importing, querying, and joining tables using AWS MapReduce
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Summary
9
9. DynamoDB – Best Practices
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9. DynamoDB – Best Practices
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DynamoDB use cases
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Real-time problem statements and their solutions
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AWS DynamoDB on mobile
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Uniform workload
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Time series tables
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When to use and when not to use DynamoDB
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Summary
10
A. Comparing DynamoDB
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A. Comparing DynamoDB
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DynamoDB versus MongoDB
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DynamoDB versus Cassandra
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DynamoDB versus S3
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DynamoDB versus Redis
11
Index
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Index

Exporting, importing, querying, and joining tables using AWS MapReduce

In this section, you will learn how to use Amazon Elastic MapReduce (Amazon EMR) with a tailored version of Apache Hive that includes connectivity to DynamoDB to execute operations on data stored in DynamoDB. You can find more information about Apache Hive at https://hive.apache.org/. You will perform actions such as the following:

  • Loading DynamoDB data tables into the Hadoop Distributed File System (HDFS)

  • Querying live DynamoDB data

  • Joining data stored in DynamoDB and exporting or querying it

With Amazon EMR and Hive, you can quickly and efficiently process large amounts of data, such as the data stored in DynamoDB. Apache Hive is a layer that you can use to query a MapReduce cluster using an easy, SQL-like query language called HiveQL. It runs on the Hadoop architecture. To start with the earlier operations, you will have to launch the EMR cluster, specify the location of the DynamoDB tables, and provide the Hive command...

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