Ideal Data Store for Storing Unstructured Data with Indexing | AWS DVA-C01 Exam Solution

Choose the Perfect Data Store for Your Application | AWS Certified Developer - Associate Exam

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Question

Your application has the requirement to store data in a backend data store.

Indexing should be possible on the data, but the data does not conform to any schema.

Which of the following would be the ideal data store to choose for this application?

Answers

Explanations

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A. B. C. D.

Answer - B.

The below AWS Documentation mentions the differences between AWS DynamoDB and other traditional database systems.

One of the major differences is the schemaless nature of the database.

Option A is invalid since this is normally used for databases that perform to a particular schema.

Option C is invalid since this is normally used for columnar based databases.

Option D is invalid since this is normally used for object-level storage.

For more information on the differences, please refer to the below link-

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/SQLtoNoSQL.html
Characteristic Relational Database Management System (RDBMS) Optimal Workloads Data Model Data Access Performance Scaling Ad hoc queries; processing). data warehousing; OLAP (online analytical The relational model requires a well-defined schema, where data is normalized into tables, rows and columns. In addition, all of the relationships are defined among tables, columns, indexes, and other database elements. SQL (Structured Query Language) is the standard for storing and retrieving data. Relational databases offer a rich set of tools for simplifying the development of database-driven applications, but all of these tools use SQL. Relational databases are optimized for storage, so performance generally depends on the disk subsystem. Developers and database administrators must optimize queries, indexes, and table structures in order to achieve peak performance. Itis easiest to scale up with faster hardware. It is also possible for database tables to span across multiple hosts in a distributed system, but this requires additional investment. Relational databases have maximum sizes for the number and size of files, which imposes upper limits on scalability. Amazon DynamoDB Web-scale applications, including social networks, gaming, media sharing, and loT (Internet of Things). DynamoDB is schemaless. Every table must have a primary key to uniquely identify each data item, but there are no similar constraints on other non-key attributes. DynamoDB can manage structured or semi-structured data, including JSON documents. You can use the AWS Management Console or the AWS CLI to work with DynamoDB and perform ad hoc tasks. Applications can leverage the AWS software development kits (SDKs) to work with DynamoDB using object-based, document- centric, or low-level interfaces. DynamoDB is optimized for compute, so performance is mainly a function of the underlying hardware and network latency. As a managed service, DynamoDB insulates you and your applications from these implementation details, so that you can focus on designing and building robust, high-performance applications. DynamoDB is designed to scale out using distributed clusters of hardware. This design allows increased throughput without increased latency. Customers specify their throughput requirements, and DynamoDB allocates sufficient resources to meet those requirements. There are no upper limits on the number of items per table, nor the total size of that table.

The ideal data store to choose for an application that requires indexing on data without any fixed schema would be AWS DynamoDB, which is a NoSQL database service provided by AWS.

DynamoDB is designed to handle massive amounts of data and provides fast and predictable performance with seamless scalability. It is a fully managed service, which means that AWS takes care of the underlying infrastructure, backups, and software updates.

DynamoDB is a NoSQL database, which means that it doesn't require a fixed schema, and you can store data in a flexible way. This is ideal when you have data that doesn't fit into a fixed schema, as it allows you to add new attributes to your data without having to modify your existing schema.

Furthermore, DynamoDB provides indexing capabilities, which allow you to quickly and efficiently retrieve data based on specific criteria. You can create indexes on one or more attributes, which can be used to filter and sort your data.

In contrast, AWS RDS is a managed relational database service, which requires a fixed schema and is ideal for applications that require a structured data model. AWS Redshift is a data warehousing service, which is optimized for running complex queries and analytics on structured data. AWS S3, on the other hand, is an object storage service that is designed for storing and retrieving unstructured data such as files and multimedia content, and does not provide indexing capabilities.

Therefore, based on the requirements of the application in question, AWS DynamoDB would be the ideal data store to choose.

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