Scaling Data Analytics Workloads on AWS

Serverless Application Development
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The marketing sector currently gathers and utilizes the collected data from different phases of the targeted consumer journey. Serverless Data Analytics is the need of the hour, as they are known to establish the metrics and create actionable insights that are beneficial in investing in customers, thereby boosting the return on investment of the business.

Whether you are a developer or a data scientist in the marketing sector, you should choose the containers for different services, such as data collection, data preparation, the creation of different marketing learning models, and statistical analysis performance. As the collected marketing data and marketing types are enhanced faster, you need the prerequisite solution for the data scaling and data costs of the prerequisite data analytics integration. As you go through this write-up, you will find the solution to scaling and performing with dynamic traffic.

 It is known to be cost-optimized for on-demand consumption. It makes the right use of container-based synchronous science applications, which get deployed with different container-based asynchronous architectures on the Data Analytics on AWS Lambda. Such a type of serverless architecture is known to automate the data analytics workflows, which use different event-based prompts.

What are synchronous container applications?

The data science applications get deployed to the delicate container instances. The load balancer, or Amazon API Gateway, routes the requests. The Amazon API Gateway is known to route different HTTP requests, such as synchronous invocations, to different instance-based container hosts. The request target is regarded as the container-based application, which is known to execute the machine learning service, referred to as SciLarn.

 Such service controllers get configured with the prerequisite dependency packages, like SciPy, Pandas, and Scikit-Learn. The containers get deployed on various targets, like Amazon Elastic Compute Cloud, Amazon Elastic Container Service, and AWS Elastic Beanstalk. Such services execute synchronously, and they scale through a time-based consumption pricing model along with different Amazon auto-scaling groups.

What are the challenges of adopting synchronous architectures?

As you try to adopt the synchronous architecture, you will face certain challenges associated with performance, scale, and costs. Now, we will discuss certain challenges during the adoption of synchronous architectures:

If the resources are not used on a wide scale and remain idle, using them 24*7 can lead to enhanced expenses if they are not sized in an appropriate manner.

Lack of native AWS service integrations

It is not possible to use different native integrations with various AWS services, like Amazon EventBridge, Amazon Simple Storage Service, and Amazon Simple Queue Service.

Operating blocking

Such service does not proceed before failure processing and Lambda returns. The sender must take prerequisite measures to handle the operating block.

Reasons to opt for Lambda Container Image Support

In this section, we are going to talk about synchronous SciLearn applications, which get deployed on different instance-based hosts, in the form of asynchronous event-based applications that are executed on the Lambda. The specific solution includes:

Similar tooling

The synchronous and asynchronous solutions adopt ECR, or Amazon Elastic Container Registry, for application artifact storage. Thus, they are equipped with the deployment pipeline tools and the same build to inspect the Dockerfiles. It indicates that the team will spend less effort and time understanding the use of the new tool.

Hassle-free dependency management using Dockerfile

Dockerfile provides a suitable choice for downloading and installing different language-compatible dependencies and native operating system packages.

Cost and performance

Lamba allows you to perform sub-second autoscaling along with demand alignment. It leads to reduced operational overhead, enhanced availability, and cost efficiency.


AWS offers over 200 service integrations for the function deployment, as well as the container images, on Lambda without the need to develop them on your own.

Larger app artifacts up to 10 GB

It leads to enhanced application dependency support, thereby giving more opportunity to host packages and files for different deployment packages.

Asynchronous event scaling

AWS provides different options for scale processing automatically and independently, including asynchronous invocation, Lambda, and the Elastic Beanstalk Worker environment. Such options include

  • They include different events in the SQS queue.
  • They are designed to include different items from the specific queue only, as they are equipped with the available capacity for task processing.
  • They will offload different tasks from a single component of the application after sending them to the queue, thereby processing them asynchronously.
  • Such asynchronous invocations are known to add the default, retry mechanisms, and tunable failure processing ‘on success’ and ‘on failure’ event destinations.

Integrations with different destinations

It is possible to log the ‘on success’ and ‘on failure’ events in the SQS queue, the EventBridge event bus, Amazon Simple Notification Service and different Lambda functions. All the four get integrated with the majority of AWS solutions. The on failure events on the other hand are sent to the dead letter SQS queue, as they are not delivered to the destination queues. Thus, they are reprocessed, catering to the needs.

 Thus, the message processing issues are isolated. The messages present in the SQS queue prompt the Lambda function. It executes the Scilearn container in Lambda for different data analysis workflows, which get integrated with SQS dead letter queue, to facilitate failures processing.

With Lambda function deployment as the container image, they can reap benefits from automatic scaling, operational simplicity, and different native integrations. Hence, it is considered to be the most suitable architecture for different data analytics use cases. It is possible to architect the synchronous service depending on the past instance-based hosts, after which it is designed to get asynchronized on Amazon Lambda. Through the new support for different container-based images and converting the workload into an asynchronous event-based architecture, you will gain success in overcoming different challenges.

 Lambda allows you to integrate different services. The lambda role helps in maintaining and simplifying the granular permission structure. Lambda Serverless Data Engineering makes use of the Amazon SQS queue in the event source form. Thus, it will scale to about 60 more instances in every minute. The computed resources are regarded as a vital part of the app architecture. The operation of idle resources and computing resources, the overprovisioning, results in enhanced costs. As Lambda remains serverless, it will lead to costs during the function invocation, thereby leading to resource applications for different requests.


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