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Building Scalable Data Pipelines with Serverless Architecture

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Every business enterprise looks for different means to seek useful insights from the data to offer business value. The data scientists, analysts, and developers spend their valuable time providing and maintaining the infrastructure. Choosing serverless architecture provides the suitable opportunity to create the applications without the need to worry about the scaling of the servers, provisioning of the servers, or resource utilization.

Apacha Kaftka happens to be the data pipeline, which allows several data producers, such as Amazon Elastic Compute Cloud and Internet of Things developers, to publish the streaming data continuously and categorize it through the data pipeline. In this write-up, we will talk about the serverless architecture that is useful to create the Apache Kafka AWS Data Pipeline. Such architecture allows the consumption of data from various client applications and visualization of it through the Amazon OpenSearch service and the Amazon QuickSight service.

Data generation through serverless architecture

Let’s take the example of the use case in which the eCommerce app runs on AWS Fargate and generates clickstream data. Before we proceed towards the data processing in real time and in batches, we will tell you the ways in which data gets produced and consumed via serverless services.

 You can consume such a clickstream through serverless architecture. The clickstream data happens to be unstructured. Hence, implementing the uniform schema happens to be a challenging one. It is recommended to opt for the AWS Glue Schema Registry to enforce the uniform schema.

You can process the Clickstream data in real time and in batches. Thus, the architecture diagram showcases the below-mentioned flows:

Serverless architecture for batch processing

Choosing AWS Lambda helps consume the messages from the Kafka data pipeline in different batches, after which they are sent to the Amazon S3 bucket. It is possible to use Amazon S3 as the data lake for data storage from different sources. You can perform schema validation via the AWS Glue Schema Registry. It plays an integral role in preventing the failure of the downstream system owing to the changes in the schema metadata.

 Besides this, it helps in enforcing the schema at the prerequisite source, after which it is sent to the Apache Kafka pipeline. It leads to schema validation before consumption and ingestion. Amazon QuickSight is responsible for the consumption of processed and transformed data from the Amazon S3 bucket. You will be able to play with data and pull useful insights.

Serverless architecture for real-time processing

As the data gets processed in real-time, it is possible to make the right use of Amazon Kinesis Data Analytics for the consumption of different messages in real-time from Amazon’s serverless architecture. You can execute the schema validation through the AWS Glue Schema Registry. It allows the prevention of downstream system failures owing to the schema metadata changes.

Furthermore, it is useful in enforcing the schema at the source, after which it gets pushed within the Apache Kafka pipeline, which leads to schema validation before consumption and ingestion. After this, the transformed data is left to the Amazon OpenSearch service. Now, you will be able to witness the dashboard visualization, which is produced according to the Kinesis Analytics application’s ingested data.

This serverless architecture can be leveraged to create the data pipeline for batch processing in real time. It will help if AWS Data Engineer remembers that the architecture might change according to the use case. Choosing AWS serverless solutions helps create applications without the need to worry about scaling servers, provisioning servers, or resource utilization management. In addition, the Cloud Data Security helps in deriving the crucial insights necessary for making real business decisions.

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