Introduction to DynamoDB and its use cases

Introduction to DynamoDB and its use cases

Recipe Objective - Introduction to DynamoDB and its use cases?

The Amazon DynamoDB is a widely used service and is defined as the fully managed proprietary NoSQL database service which supports the key-value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. Amazon DynamoDB exposes a similar data model to and derives its name from Dynamo but has a different underlying implementation. Dynamo had the multi-leader design requiring clients to resolve version conflicts and DynamoDB uses synchronous replication across the multiple data centres for high durability and availability. Amazon DynamoDB was announced by the Amazon CTO Werner Vogels on January 18, 2012, and is presented as an evolution of the Amazon SimpleDB. Amazon DynamoDB offers reliable performance even as it scales further, a managed experience so users won't be SSH-ing into the servers to upgrade the crypto libraries and the small, simple API allowing for simple key-value access as well as more advanced query patterns. Amazon DynamoDB offers built-in security, continuous backups, automated multi-region replication, in-memory caching and data export tools. Amazon DynamoDB offers security to the user's data encryption at the rest automatic backup and restores with guaranteed reliability with an SLA of 99.99&% availability.

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Benefits of Amazon DynamoDB

  • The Amazon DynamoDB offers users the ability to auto-scale by tracking how close the usage is to the upper bounds. This can allow users systems to adjust according to the amount of data traffic, helping users to avoid issues with the performance while reducing costs and thus helping in performance and scalability. The Amazon DynamoDB offers Access to the control rules as to when the data gets more specific and personal, it becomes more important to have effective access control so, users want to easily apply access control to the right people without creating bottlenecks in other people’s workflow. The fine-grained access control of DynamoDB allows the table owner to gain a higher level of control over data in the table. Amazon DynamoDB streams allow developers to further receive and update the item-level data before and after changes in that data and this is because DynamoDB streams provide the time-ordered sequence of changes made to the data within the last 24 hours. So, with streams, users can easily use the API to make changes to the full-text search data store such as the Elasticsearch, push incremental backups to Amazon S3, or maintain an up-to-date read-cache.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon DynamoDB and the Use cases of Amazon DynamoDB.

Use cases of Amazon DynamoDB

    • It helps in developing software applications.

Amazon DynamoDB helps in building internet-scale applications supporting user-content metadata and caches that require high concurrency and connections for the millions of users, and millions of requests per second.

    • It helps in Creating media metadata stores.

Amazon DynamoDB offers Scale throughput and concurrency for media and entertainment workloads such as real-time video streaming and interactive content, and deliver lower latency with multi-region replication across the AWS Regions.

    • It offers seamless retail experiences.

Amazon DynamoDB uses design patterns for deploying shopping carts, workflow engines, inventory tracking, and customer profiles. Amazon DynamoDB supports high-traffic, extreme-scaled events and can handle millions of queries per second.

    • It offers a Scale gaming platform.

Amazon DynamoDB offers Focus on driving innovation with no operational overhead. Build out a user game platform with player data, session history, and the leaderboards for millions of concurrent users.

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I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

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