Organizations process data in different ways based on a variety of factors. For some, scalability is most important. Others are more focused on timeliness and generating insights in real-time. Data volume variability is another common concern. Being able to scale up quickly and then return to a cost-effective status quo is crucial for many modern businesses.

Regardless of the specific factors that teams prioritize, in most cases, leaders are really trying to strike a balance between cost and performance. Fortunately, in the age of cloud computing and distributed data processing, it’s possible to get the best of all worlds. Serverless data processing, in particular, has many advantages. And with AWS Step Functions, large-scale serverless data processing is easier than ever.

Why is Serverless Architecture Beneficial for Data Processing?

Serverless architecture offers four primary benefits for any level of data processing:

  1. There are no servers to manage
  2. On a platform like AWS, serverless architecture integrates directly with other cloud data services
  3. Scaling up and down is fast and seamless
  4. Users only pay for what they use

Serverless architecture still involves servers. However, it’s the cloud service provider that is responsible for provisioning, updating, and maintaining them according to the organization’s requirements. Without this IT overhead, cloud engineering teams can develop and bring new features to market faster.

On AWS, integrating serverless solutions with other key data management services is easy. As a result, companies can implement robust and customized data processing pipelines that align with their unique needs.  Additionally, AWS’ serverless solutions are fully capable of handling memory-intensive workloads and can even provide local storage when needed.

Serverless architecture also eliminates the possibility of overprovisioning or underprovisioning resources. Everything scales directly with resource demands, which means companies don’t pay for unused compute and they are never left needing more.

Now, when it comes to large-scale data processing, one AWS service is especially crucial – AWS Step Functions. AWS Step Functions now has a distributed map capability that enables teams to achieve serverless workflow concurrency of up to 10,000 executions. This is a huge win for large-scale data processing in which teams need to process thousands of items simultaneously. When using AWS Step Functions’ distributed map, there are certain best practices to keep in mind. Here are 5:

Use MaxItems

When developing and testing serverless data processing workflows on AWS Step Functions, limit the number of records you process at one time by setting a MaxItems number. This helps keep costs down and validate that all of your logic is correct before scaling up to thousands of records.

Use Batching

When processing vast numbers of records, it’s important to batch them strategically. You want to create batches around a number or size of items that optimize costs, minimize inefficient workflows, and keep processing time down. You can use MaxItems again to test your batches and ensure that your approach is aligned with downstream data processing activities.

Use Concurrency

Even though AWS Step Functions allows for up to 10,000 concurrent executions, it may not make sense to operate at that volume depending on the API quotes tied to downstream services. Too many concurrent executions at the AWS Step Functions level can lead to overloaded databases or services with more limited scalability.

Choose the Best Workflow for Your Data Processing Job

In general, there are two types of workflows: standard and express. Standard workflows work best for long-running workflows. Express workflows are ideal for those lasting under five minutes and at a high volume.

Set an Appropriate Failure Threshold

With any data processing workflow, there will be failures. In AWS Step Functions distributed map, you can set a threshold for what percentage of failures is acceptable so that you don’t stop entire workflows or processes unnecessarily when a failure inevitably happens.

Go Serverless for Your Large-scale Data Processing with ClearScale and AWS

At ClearScale, we’ve been helping clients take full advantage of the AWS cloud since 2011.  We work on a wide range of cloud projects, including those that involve large-scale data processing with serverless architecture.

We’d love to help you implement the best practices highlighted here or work through a bigger data processing project. With the right recommendations and AWS services, you can find the best combination of cost efficiency and performance for processing your data at scale.

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