Introduction
So many web-based applications lack of scaling reason is growth in numbers of users along with the increasing complexity of data traffic. This modern complexity of business require to process data faster and more robustly. That’s why uses of standard transactional databases is not always the best option to choose.
Amazone DynamoDB have been designed to manage this kind of new influx of data. Amazone DynamoDB is an Amazon Web Services database system that supports data structures and key-valued cloud services. It allows users the benefit of auto-scaling, in-memory caching, backup and restore options for all their internet-scale applications using DynamoDB.
But why would you want to use Amazone DynamoDB and what are some examples of use cases?
In this post, we will discuss on this state deeply, also some of the benefits of using Amazone DynamoDB.
What is Amazon DynamoDB?
Amazon DynamoDB is a fully-managed (“server less”) and NoSQL (no relational) database service. Amazone DynamoDB is highly scalable, easy to setup, no need to re-deploy or re-architect. It also offers a flexible model which uses automatic scaling of throughput capacity, in simple word it scales compute capacity based on demand, saving money and lowering entry costs. This makes it a great fit for mobile, gaming, IoT, and other high-growth and high-volume applications.
Languages and frameworks with a DynamoDB binding include Java, JavaScript, Node.js, Go, C# .NET, Perl, PHP, Python, Ruby, Rust, Haskell, Erlang, Django, and Grails.
Advantage of DynamoDB
- Simple Set-up
- AWS Security
- Performance and scalability
- Access to control rules
- Persistence of event stream data
- Time To Live
- Storage of inconsistent schema items
- Automatic data management
Additional DynamoDB features
The DynamoDB Triggers feature integrates with AWS Lambda to allow a developer to code actions based on updates to items in a DynamoDB table, such as sending a notification or connecting a table to another data source. The developer associates a Lambda function, which stores the logic code, with the stream on a DynamoDB table. AWS Lambda then reads updates to a table from a stream and executes the function.
The DynamoDB Streams feature provides a 24-hour chronological sequence of updates to items in a table. An admin can access the stream via an API call to take action based on updates, such as synchronizing information with another data store. An admin enables DynamoDB Streams on a per-table basis.
Both DynamoDB and MongoDB are free for a pre-defined period of time. After free usage expires, DynamoDB calculates cost on the basis of reads and writes, while MongoDB calculates cost according to consumed storage.
Use cases of Amazone DynamoDB
- Develop software applications
- Create media metadata stores
- Deliver seamless retail experiences
- Scale gaming platforms
Example Use Cases for Amazone DynamoDB
One of the reasons people don’t use DynamoDB is because they are uncertain whether it is a good fit for their project. We wanted to share some examples where companies are using DynamoDB to help manage the larger influx and of data at high speeds.
- Duolingo
Duolingo, an online learning site, uses DynamoDB to store approximately 31 billion data objects on their web server. This startup has around 18 million monthly users who perform around six billion exercises using the Duolingo app. Because their application has 24000 scan units per second and 300 write units per second DynamoDB terminated up being the proper acceptable them. The team had little information concerning DevOps and managing giant scale systems after they started. Attributable to Duolingo’s international usage and wish for customized information, DynamoDB is that the solely info that has been ready to meet their desires, each in terms of information storage and DevOps.
Also, the actual fact that DynamoDB scales mechanically meant that this little startup failed to have to be compelled to use their developers to manually regulate the dimensions. DynamoDB has simplified in addition as scaled to fulfill their desires.
Comparative Analysis between Amazon DynamoDB and Other Databases
Compared to other transactional databases, like Oracle, MSSQL, or PostgreSQL, AWS DynamoDB is schemaless, meaning it does not require conformation to a rigid schema of data types, tables, etc. This, though, also comes with a tradeoff: key advantages, like consistently high performance and millisecond latency, are compromised with ACID (atomicity, consistency, isolation, and durability) properties supported by a relational database. Compared to other NoSQL databases, AWS DynamoDB supports data models like key-value pair (see figure below), and document data structures such as JSON, XML and HTML. But DynamoDB lacks support for columnar data sets, like Cassandra and HBase, and graph models such as Orient DB.
I hope you like this Blog. Thank you.
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