In my previous article I have given information about Hive vs Spark. In current article I would like to give information on DynamoDB which is use for big data handling with its core concepts in detail. A fully managed NoSQL database service, Amazon DynamoDB offers quick and reliable performance along with seamless scaling. The administrative costs of running and maintaining a distributed database can be offloaded thanks to DynamoDB, so you no longer have to worry about hardware provisioning, setup, replication, software patching, or cluster growth.
We can build database tables using DynamoDB that can handle any volume of request traffic and store and retrieve any quantity of data. The throughput capacity of your tables can be scaled up or down without causing any downtime or performance degradation. You can use the AWS Management Console to keep track of resource usage and performance indicators. In a nutshell, the following are some of the reasons why Amazon’s DynamoDB service is one of the best options for managing and storing data for businesses of all sizes: DynamoDB automatically replicates across multiple Availability Zones in an AWS Region, providing built-in high availability and data durability, and you can use global tables to keep DynamoDB tables in sync across AWS Regions. This allows you to handle your throughput and storage requirements while maintaining consistent and quick performance.
What is DynamoDB Consistency Model?
DynamoDB has two types of Consistency model which is generally called ad read consistency model.
1.Strongly consistent reads :
When your application, or a component of your application, requires a highly consistent read, DynamoDB also allows users the freedom and control to request it. DynamoDB responds to a strongly consistent read request with the most recent information, which reflects the successful updates from all previous write operations.
2.Eventually Consistent Reads :
It is the default method. The read-throughput is maximized by the eventual consistency option. The average time to achieve consistency across all copies of data is one second. The results of a recently finished write operation might not be reflected in the response when you read data from a DynamoDB table. The response can contain some outdated information. After a brief interval, the answer ought to contain the most recent data if you issue another read request.
Technologies and Core Concepts of DynamoDB :
DynamoDB stores data in tables, just like other databases. A table is a group of elements without fixed schemas connected to them. Let’s take the Employee table as an example; this table contains data about the employees’ personal and professional lives.
The item is single data record in table. The number of items you can store in a table in DynamoDB is unlimited, therefore you can store anything from 0 to an infinite number of items. The number of qualities that any item can have can vary. DynamoDB In other conventional databases, rows or records are equivalent to items. Each item in the Employee table will correspond to one employee.
It is the attribute connected to the item. The characteristics that make up each thing are one or more. Similar to columns in other relational database technologies, DynamoDB attributes are. The Employee table, for instance, has characteristics like EmployeeID, EmpName, EmpStart Date, etc.
Limitations of DynamoDB
- A minimum of one read capacity unit and one write capacity unit must be present in every table and global secondary index.
- There are no restrictions on table sizes, however unless you request a higher maximum, accounts are limited to 256 tables.
- Per table, a maximum of five local and twenty global secondary (default quota) indexes are allowed.
I have tried to cover the Core concepts of DynamoDB in the above article . If you like this article or if you have any issues with the same kindly comment in comments section.