Amazon DynamoDB Database Guide for Beginners


Table of Contents

With Amazon DynamoDB, you can run NoSQL applications as a serverless (non-relational) database. DynamoDB is highly scalable, which means that you can start very small and grow very large without having to change your deployment or architecture. 

In addition, it uses a flexible model that scales compute capacity automatically based on demand, reducing computer costs and saving money. It is particularly well suited to high-growth and high-volume applications such as mobile, gaming, IoT, and others.

Advantages of Amazon DynamoDB

DynamoDB by Amazon is an open source database management system that offers many advantages over other NoSQL solutions, such as Apache Cassandra and MongoDB. It is particularly advantageous to integrate DynamoDB with other AWS services. It is a good choice if you already use Amazon Web Services.

Simple installation

Serverless databases are easy to set up. To begin, open the AWS Management Console and follow the on-screen instructions. In contrast, creating a MongoDB instance on-premises is considerably more complex and may require resolving authentication errors.

Security for AWS

AWS Identity and Access Management (IAM) governs the security policies of DynamoDB. Additional AWS security features may be used to enhance the controls. It has been reported in the past that MongoDB has been compromised by improper configuration and management of the database.

How much will it cost?

The DynamoDB and MongoDB databases are both free for a pre-determined period of time. When the free trial period expires, DynamoDB charges based on reading and writing, whereas MongoDB charges based on storage usage.

What DynamoDB has to offer ?

The service features several enhancements that set it apart from the competition. Among them are:

DynamoDB accelerator (DAX) for Amazon

This service provides a fully managed, secure, scalable, and highly available DynamoDB cache. With this release, DynamoDB’s response time has significantly improved and is suitable for read-intensive workloads. Amazon Virtual Private Cloud (Amazon VPC) hosts and hosts DAX clusters. 

In order for your application to run in VPC, an Amazon EC2 instance must be installed with the DAX client. DAX clients send all requests to the DAX cluster, which retrieves the requested data if it is available (a cache hit).

In the case of unavailable data in the cluster, DynamoDB will be drawn from (a cache miss). Your application will receive results through the DAX cluster. It is possible to reduce the amount of time spent reading data from DynamoDB tables by caching data within DAX clusters. 

Restricted encryption

As part of DynamoDB 3.1, DynamoDB has added support for encryption at rest. It is quite easy to enable it in DynamoDB just by creating a new table and unchecking the option “Use default settings”. 

Next, in the section marked “Encryption at rest”, enter “Enable encryption” to enable encryption. To encrypt the tables and indexes, we will be using the AES-256 algorithm and the Key Management Service (KMS) of AWS. During DML and DDL operations, encryption should not cause any performance or latency issues.

Automatic scaling on a scheduled basis

The use of scheduled auto-scaling is a cost-effective, fault-tolerant, and highly available installation. DynamoDB’s auto-scaling feature and scaling policy are simple to define once you have a basic understanding of the steps.

Global tables

Development of robust, multi-regional applications is made easier with Global Tables. Thus, data is made available close to users located throughout the world, thereby reducing latency. An update to a table in a specific region will automatically synchronize with other tables located in other regions from the perspective of a developer. Customers are charged for replicated writes and reads as well as storage, although prices may vary between regions.

The low down

Despite its wide range of features and support by AWS, Amazon DynamoDB has an advantage over other NoSQL databases. It is a great relief for database administrators to have a serverless solution available. 

DBAs no longer have to maintain servers and infrastructure, thanks to serverless data management options. Hence, if you are seeking a NoSQL database service that is easy to set up, scales easily, and offers high performance, this is an excellent choice.

Architecture of DynamoDB

DynamoDB, among other AWS products, is renowned for its high availability, scalability, and security. The following characteristics contribute to its success:

  • Accessibility : Asynchronously updating all instances of data associated with an Amazon DynamoDB table takes place when your application writes data to it and receives a response (such as Okay). It is usually possible to achieve a consistent state for the data across all storage locations within a short period of time. 

This is because DynamoDB has high performance across three availability zones or facilities. The data is replicated using synchronous data replication on SSDs.

  • Scalability : AWS DynamoDB utilizes automatic partitioning to distribute the data across various partitions and to increase its performance as data volumes increase. It is AWS’s task to handle the partitioning of data so that customers can focus on what matters most to them.
  • Security : AWS’ identity and access management (IAM) can be integrated with DynamoDB to provide unique credentials for each user to access database resources. Additionally, authentication can also be accomplished by leveraging your LDAP or AD server. Data is also backed up to Amazon Simple Storage Service (S3) for maximum performance, durability, and security on a large scale.

How AWS DynamoDB works ?

In addition to scaling throughput in accordance with workload demands, AWS DynamoDB automatically partitions data as table sizes increase. 

Here is how it’s done:

  • Inspecting. A central dashboard that monitors DynamoDB’s performance, resource utilization, and operational health is known as CloudWatch. Using CloudWatch, you can monitor and trigger alarms when a certain threshold is breached.
  • Throughput. In the production and/or processing industries, throughput capacity can be defined as the maximum rate of production. A DynamoDB instance can be scaled automatically, provisionally, or with restricted capacity via DynamoDB’s autoscaling.
  • Consistency in data reading. In light of the fact that AWS DynamoDB is a NoSQL database, it may not support ACID properties that are commonly found in relational databases. 

Although ultimately consistent reads are not immediately reflected in a DynamoDB table. They do after waiting and retrying, as well as strongly consistent reads when the last saved data is returned following a successful write transaction.

  • Zero Management. You won’t need to manage infrastructure layers when you use this managed service model. Among others, AWS provides network and storage connectivity, OS patching, as well as server health and backups, among others.
  • Open APIs and easy integration. APIs provided by DynamoDB allows customers to manage tables and indexes as well as execute a variety of data operations (such as read and write operations).  Besides Amazon Redshift, ElasticStack, and IAM, it also supports integration with other cloud platforms.

Amazon RDS vs DynamoDB

Data are stored in DynamoDB, a NoSQL database service hosted on Amazon Web Services. Due to its rapid response time, high reliability, and scalability, working with NoSQL data is simplified.

By contrast, Amazon RDS stands for “Relational Database Service” (RDBMS) and we primarily use it for manipulating and processing structured and relational data (SQL). This is in contrast to DynamoDB, which is the name of the database engine itself as opposed to the name of the service. Thus, we are able to choose among six different database engines, such as Amazon Aurora, MySQL, and MariaDB.

Amazon RDS and DynamoDB are both managed by AWS, so administrators can focus more on designing and working with the databases. Additionally, both of these systems provide automated scalability and backup functions and are highly reliable.

DynamoDB is a NoSQL database engine, whereas Amazon RDS is a relational database service.

DynamoDB’s ability to accommodate tables of any size makes it stand out in terms of storage size. However, with RDS, the level of storage depends on the database engine we are using.

The use of Amazon DynamoDB with AWS Lambda

To process records in an Amazon DynamoDB stream, you may use a DynamoDB Amazon, Amazon lambda DynamoDB function. DynamoDB Streams allows you to trigger a Lambda function to run when a table in DynamoDB is updated in order to perform additional processing.

It is possible to invoke a lambda function synchronously with an event containing stream records, which is read by Lambda. A batch of records has been read by Lambda and your function is invoked to process them.

Integrate Alexa skills with Amazon Web Services

Amazon Web Services (AWS) stores your code and resources when you create an Alexa-hosted skill. Amazon Alexa DynamoDB can provide access to three AWS Lambda endpoints. The media will be stored in an Amazon S3 bucket and the data will be stored in Amazon DynamoDB tables. 

Your personal AWS account can be used to provide additional resources if your skill requires additional Amazon Web Services or if your use of Amazon Web Services exceeds the limits.

Alexa Resource Names (ARN) let you extend the functionality of skills hosted on Alexa seamlessly using AWS Lambda resources. In this manner, you can connect your own Amazon DynamoDB table to your Alexa-hosted skill.

Amazon CloudSearch DynamoDB data

For searching DynamoDB data, the easiest way is to use the Amazon CloudSearch console. It is possible to configure the console to analyze table data and suggest indexing options based on the table attributes within the configuration wizard. Those suggestions may be modified to determine which attributes of the table will be indexed.

You can map a maximum of 200 unique attributes to index fields when configuring a search domain automatically from a DynamoDB table. In order to be able to upload data from DynamoDB tables with 200 or fewer fields, you cannot configure a search domain with more than 200 field attributes. A field is enabled in the suggested configuration when Amazon CloudSearch detects an attribute with several distinct values.


1. Amazon DynamoDB is what type of database solution?

DynamoDB is designed by Amazon as a high-performance, no-SQL, key-value database that can run applications at any scale in a fully managed, serverless fashion. As part of its highly integrated security features, DynamoDB offers continuous backups, automated replication across multiple regions, data caching in memory, and data export tools.

2. What is a primary key in Amazon DynamoDB?

You must specify the primary key of a table along with the table name when you create a table. Each item in the table is uniquely identified by its primary key, which cannot be the same for any two items.

3. What is meant by DynamoDB?

Amazon Web Services (AWS) DynamoDB is a NoSQL database hosted by the company. This database provides reliable performance even as it scales. You won’t have to SSH into servers to update the crypto libraries; a simple, straightforward API that allows basic key-value access and more advanced query patterns.

4. What is the basic data model in Amazon DynamoDB?

As shown in Relational Modeling, DynamoDB table designs correspond to relational order entry schemas. As part of DynamoDB’s Adjacency List Design Pattern, it represents relational data structures in an indirect way, a common method of representing relational data structures.

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