Big data was once limited to processing batches of huge, unstructured data collected by organizations over time. Apart from the heavy investment, it had the additional disadvantage of producing delayed results. Real-time operations, on the other hand, hold the advantage of making businesses extremely responsive to customer data. Over time, organizations have realised that remaining relevant and updated calls for a real-time monitoring of data.
Real-time processing a.k.a. stream processing ensures that data is presented freshly for marketers and analysts to leverage. Real-time analysis of big data not only aids the upkeep of recommendation engines but also assists in quick maintenance, fault tolerance, and prompt fraud detection.
The Lambda Architecture was the first to attempt integration between batch and real-time data. However, the architecture initially separated the duties of the two, which made it a complex system. When Amazon Web Services (AWS) came into being, it offered an automatic advantage over Lambda owing to its integrated approach to processing and storing the two types of data.
Ever since AWS has come into the picture, data management and extracting insights from customer data has become a seamless process. AWS uses an advanced Ethernet networking technology, making it quick and highly customisable in terms of scalability. It securely enables no/low admin interference, shifting the focus from the infrastructure to the information that really matters – data. Thus, it has helped bust the myth of big data being equivalent to big costs.
AWS contains the following elements:
- Data Store
It offers an array of stream storage tools such as:
- Amazon DynamoDB
- Amazon Kinesis Streams
- Amazon Kinesis Firehose
- Amazon SQS
- Amazon S3
The AWS stream processing technologies include:
- AWS Lambda
- Amazon Kinesis Applications
- Amazon Kinesis Analytics
- Amazon SQS Applications
The options for data store to store and analyse the data are:
- Amazon ElastiCache
- Amazon DynamoDB
- Amazon RDS/Aurora
- Amazon Elastisearch
- Amazon S3
- Amazon Glacier
Data Analysis tools offered by AWS:
- Amazon Kinesis Data Analytics
Best Practices for Implementing Scalable Real-time Architecture using AWS
- The first step when implementing the architecture revolves around understanding the key metrics that your business needs to track. It is also essential to figure out what one intends to do with the information that is collected
- Think about providing a degree of flexibility in adding components to these key metrics to accommodate the evolution in your business. Also, make it easy to deploy and ensure that it has the capacity to handle the basic minimum number of events
- Select a monitoring solution to observe and log the history of the AWS API calls for security tracking and auditing compliance. This tracking should also take place in real-time
- Latency is the time taken for the system to generate answers and exploitable data. Consider an environment that has low latency in data processing. While implementing low-latency, care should be taken to not compromise on reliability
- Enact a solution that is not limited to specific operating systems and can surpass these boundaries. It should have the capacity to assimilate the AWS workloads in tandem with on-premise activities
- Implement a solution that runs in parallel with your current business process yet keep it flexible for future developments. Therefore, if your business is to develop in the future, it may not require drastic operation shifts. It should also complement the skill set possessed by the employees of your firm
Amazon Web Services can be easily combined up with other platform to build a custom architecture that will be suitable for your real-time data needs with respect to your business model. AWS helps you process, analyse, and store streaming and batch source data simultaneously. Fortunately, you can use AWS based on your need and test out the performance and scalability of your architecture. It offers almost limitless throughput, cost-effectiveness, high availability, and security while drastically reducing the turn-around time. Therefore, AWS is one of the most dependable platforms for building a scalable real-time architecture.