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AWS serverless development

Axar7 builds serverless systems on AWS for work that arrives in bursts and has to finish reliably: queues, state machines and functions instead of servers sized for the peak. Our CRM Export Pipeline for a B2B lead-data platform runs on Amazon SQS, AWS Step Functions and AWS Lambda, handling around 7,000 export jobs a day of up to 25,000 records each.

See the work

Updated

On record

export jobs a day
~7,000
CRM Export Pipeline case study →
records in a single job, at most
25,000
CRM Export Pipeline case study →

01How it works

AWS serverless development, step by step

  1. 01

    Requests wait on a queue

    A user exports a list of the companies or contacts they found on the platform, and only the records that are new to them draw on their credits. Each export request goes onto an Amazon SQS queue as soon as a user asks for it. The queue absorbs bursts, so the system takes jobs at the rate it can run them and no request is dropped while it catches up.

  2. 02

    Step Functions run the job

    AWS Step Functions orchestrate each job through the same states whatever its size: map the records to the destination's fields, match them for create or update, deliver them, report on them. A 25,000-record export and a small one take the same path.

  3. 03

    Lambda scales with the work

    Each state runs in AWS Lambda functions, as many as the jobs in flight need. There are no servers to size for the peak, and the processing framework is built for low latency.

  4. 04

    Back to none, with a report

    When the queue is empty the functions stop. Every job ends in a CSV report that marks each record succeeded, failed or skipped, and the export joins the user's history, with role-based access and a full audit trail.

02Scope

What we build

  • Queue-based intake with Amazon SQS
  • Event routing with Amazon EventBridge
  • Job orchestration with AWS Step Functions
  • Processing in AWS Lambda, scaled to the work
  • Email through Amazon SES, logs in CloudWatch
  • Per-job reports, history and audit trails

03Proof

Where we have done this

  1. 01

    CRM Export Pipeline

04Stack

What we build it with

  • Amazon EventBridge
  • AWS Lambda
  • AWS Step Functions
  • Amazon SQS
  • Amazon SES
  • Amazon CloudWatch Logs
  • Amazon EC2
  • Docker
  • CI/CD

05Questions

Common questions

Why serverless for this kind of workload?

Because the work arrives unevenly. Queued jobs and functions that scale with them mean there are no servers to size for the peak, and a large job and a small one take the same path.

How large can a job be?

In the CRM Export Pipeline, up to 25,000 records per job, at around 7,000 jobs a day.

How do you know what happened to each record?

Every job ends in a CSV report that marks each record succeeded, failed or skipped, kept in the user's export history.

Which AWS services do you use?

Mainly EventBridge, Lambda, Step Functions, SQS, SES and CloudWatch Logs, with EC2 where a long-running server fits better than functions.

How long does a serverless build take?

It depends on the project. Simpler requirements take a few weeks, up to a month; complex builds take months.

06Related

Related services

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