Fintech is a term used to describe the application of technology to financial services. The term "fintech" is derived from the combination of the words "finance" and "technology". Fintech companies use technology to provide innovative solutions to traditional financial services, such as banking, lending, investing, payments, and insurance.
AWS (Amazon Web Services) offers a wide range of cloud computing services that can provide significant benefits to fintech companies.
Here are some of the main reasons why fintech companies may choose to use AWS:
Overall, AWS offers a powerful set of tools and services that can help fintech companies to grow, innovate, and succeed in a highly competitive and regulated industry.
Below is the list of AWS services that are widely used among FinTech companies.
Amazon S3 (Simple Storage Service) is a scalable, high-speed, low-cost web-based service designed for online backup and archiving of data and application programs.
It allows to upload, store, and download any type of files up to 5 TB in size. This service allows the subscribers to access the same systems that Amazon uses to run its own web sites. The subscriber has control over the accessibility of data, i.e., privately/publicly accessible.
To understand Amazon ECS, you first have to understand Docker. Docker is a client-server application that can be installed on Linux, Windows, and MacOS and that allows you to run Docker containers.
Containers are lightweight environments containing everything needed to run a specific application or part of an application. Multiple different containers can be run on one machine, so long as it has the Docker software installed.
Using Docker containers allows teams to have a consistent development environment by abstracting away the software, operating system, and hardware configuration into a standard building block that can be run on any machine.
Amazon Elastic Container (Amazon ECS) is an AWS cloud service used for managing containers. Using Amazon ECS, developers can run their apps on the cloud without configuring an environment to run the code. With the help of AWS accounts, deployment and management of scalable apps can be done by running them on a group of servers called clusters via API and task definitions. It can be accessed through AWS Management Consoles and SDKs.
With ECS the services can run seamlessly because ECS manages containers the applications can run in a highly available mode, that is, if something goes wrong then some other container gets found, and then the application runs in that container. There is a very minimal chance of your application going down.
AWS Lambda is a serverless and event-driven compute service. So, before proceeding we must understand serverless and event-driven.
Serverless: The term "serverless" typically pertains to applications that operate without the need for server provisioning or server management.
These applications are commonly referred to as "serverless applications". AWS Lambda is an event-driven, serverless computing platform provided by Amazon as a part of Amazon Web Services. Therefore, you don’t need to worry about which AWS resources to launch, or how will you manage them. Instead, you need to put the code on Lambda, and it runs.
How Lambda works:
The purpose of both IAM and KMS is to provide strong security controls that help protect AWS resources and data. IAM helps to manage user access and permissions, while KMS provides encryption key management to help secure sensitive data. By using these services, organizations can implement a strong security posture in the cloud and maintain control over their resources and data.
Amazon Web Services (AWS) identity and access management is simply the IAM system that is built into AWS. By using AWS IAM, you can create AWS users and groups and grant or deny them access to AWS services and resources. AWS IAM is available free of charge. AWS IAM service provides:
WS Key Management Service (KMS) gives you centralized control over the cryptographic keys used to protect your data. The service is integrated with other AWS services making it easier to encrypt data you store in these services and control access to the keys that decrypt it.
Before knowing about the Kinesis, you should know about the streaming data.
What is data streaming?
Streaming data refers to continuous, real-time data that is generated by a variety of sources and delivered to applications and users without delay.
This data is typically unstructured and comes from a wide range of sources, including sensors, social media feeds, online transactions, and web logs Kinesis is a platform on AWS that sends your streaming data.
It makes it easy to analyse load streaming data and also provides the ability for you to build custom applications based on your business needs. Streaming data is data which is generated continuously from thousands of data sources, and these data sources can send the data records simultaneously and in small size.
Kinesis is a managed, scalable, cloud-based service that allows real-time processing of streaming large amount of data per second. It is designed for real-time applications and allows developers to take in any amount of data from several sources, scaling up and down that can be run on EC2 instances.
It is used to capture, store, and process data from large, distributed streams such as event logs and social media feeds. After processing the data, Kinesis distributes it to multiple consumers simultaneously.
Core Services of Kinesis:
Amazon Elastic Map Reduce (Amazon EMR) is a web service that makes it easy to process large amounts of data quickly and cost-effectively.
Amazon EMR uses Hadoop, an open-source framework, to distribute your data and processing across resizable clusters of Amazon EC2 instances.
Using Map Reduce, a core component of the Hadoop software framework, developers can write programs that process massive amounts of unstructured data in distributed clusters of processors or standalone computers.
Amazon SageMaker is a cloud machine-learning platform that helps users in building, training, tuning and deploying machine learning models in a production ready hosted environment.
The SageMaker comes with a lot of built-in optimized ML algorithms which are widely used for training purposes. Now to build a model, we need data. We can either collect and prepare training data by ourselves or we can choose from the Amazon S3 buckets which are the storage service.
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases.
Amazon describes it as a service that allows you to easily create, operate, and scale open-source compatible in-memory data stores within the cloud. Simply, this means that it eliminates the complexity associated with setting up and managing a distributed cache environment.
Amazon ElastiCache supports two open-source in-memory engines: