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Dynamic, Fully-Automated ETL Pipeline on AWS

100% API automation processing millions of records daily

Built a dynamic ETL pipeline on AWS for a sales-engagement SaaS platform, using Glue, Redshift, Apache Hudi and Athena to automate extraction of millions of records daily and cut data-preparation time by 40%.

PythonPySparkAWSAWS GlueAmazon RedshiftApache HudiSQLAthenaAWS S3
Problem Statement

Manually extracting and transforming unstructured files was throttling the business. Data-prep cycles ran long, data reached analysts late, and there was a hard limit on how fast the organisation could turn raw data into decisions.

  • Manual processing of unstructured files caused prolonged data-preparation times.
  • Delayed data availability slowed analysis and time-sensitive decisions.
  • Heavy manual intervention made the pipeline brittle and hard to scale.
Headline Outcomes
−40%automation

Data-preparation time

100%hands-off ingestion

API automation

+30% fasterreal-time pipeline

Data availability

The Solution

An automated, dynamic ETL pipeline on AWS. It extracts and transforms unstructured files at scale, orchestrates millions of records per day through 100% API automation, and lands analytics-ready data in Redshift, using Apache Hudi for upsert-friendly incremental processing.

Automated extraction and transformation of unstructured files end-to-end on AWS.

100% API automation orchestrates the daily extraction of millions of records.

Apache Hudi enables efficient incremental upserts into Amazon Redshift.

Athena and S3 provide cheap, serverless query and storage across the data lake.

System Architecture

How the data flows

01

API Sources

Millions of records/day

02

Glue + PySpark

Extract & transform

03

Apache Hudi

Incremental upserts

04

S3 + Athena

Serverless lake

05

Redshift

Analytics warehouse

Result 01

Accelerated decision-making by cutting data-prep time 40%.

Result 02

Achieved fully hands-off ingestion of millions of records per day.

Result 03

Delivered timely insight through a resilient, automated pipeline.

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