AWS PySpark ETL Pipeline

@kumarashish99

As an AWS certified professional with over 5 years of experience in backend development and DevOps engineering, I believe I possess the necessary skills to develop your AWS PySpark ETL Pipeline efficiently. My expertise in building secure, scalable cloud infrastructures and leveraging advanced platform services like Glue, EMR, Lambda, and S3 aligns perfectly with your project requirements. My proficiency in Python and experience with PySpark mean I have a deep understanding of data manipulation and processing at scale. I am comfortable working with a mix of data file formats and have expertise in automatic format detection and schema inference - crucial for standardizing and loading data from diverse sources as mentioned. All deliverables you seek for this project align closely with my capabilities. From building efficient PySpark scripts or Glue jobs to spinning up AWS resources using infra-as-code templates; from creating well-documented READMEs to managing both deployment & scheduling - I am committed to driving this project towards success

@raj00565

As someone who's been working with AWS for over a decade, I am highly proficient in building exactly the kind of end-to-end ETL pipeline you need. But what sets me apart are the skills that stretch beyond just Amazon Web Services. I believe in holistic solutions, and that's why my expertise in AWS Lambda and NoSQL databases like Couch & Mongo could bring tremendous value to your project. Considering your requirement for a PySpark-based ETL pipeline engineered for production reliability, I assure you of my ability to design a scalable and modular architecture. Leaning on my 12+ years of experience in system administration, network engineering, and DevOps, I will carefully craft your infrastructure as code templates using CloudFormation or Terraform to reduce development time and enhance cost-efficiency. Moreover, my deep understanding of data engineering guarantees solid adherence to your desired practices such as parameter-driven jobs, unit testing, logging, alerting, and retry logic. I appreciate the emphasis you place on data quality - in my hands, successful implementation with precise row counts and clear error management is guaranteed. Together, let's leverage the best of AWS services like Glue, EMR, Lambda, and S3 to build an exceptional solution!

@marcelof59

Hi, Thank you for your job posting. I carefully reviewed your project requirements and I’m very interested in working with you because this project strongly matches my skills and experience. I am confident that I can deliver high-quality results within your expected timeline and budget. I always focus on clear communication, clean work, and reliable delivery to ensure client satisfaction. My experience includes: * Building scalable, production-grade ETL pipelines on AWS using PySpark, Glue, EMR, Lambda, and S3, with a focus on automation, modular code, and fault tolerance * Designing infrastructure-as-code templates with CloudFormation and Terraform for automated provisioning of data ingestion, processing, and storage resources I have successfully worked on similar projects and understand the importance of accuracy, efficiency, and long-term maintainability. I would be happy to discuss your project in more detail and start as soon as possible. Looking forward to your kind response. Best Regards, Marcel

@serviceaxisgroup

Hi, I can help with an end-to-end AWS PySpark ETL pipeline that auto-detects CSV/JSON/Parquet, standardises schema, and loads analytics-ready data to S3 or Redshift with reliable partitioned, compressed outputs. I’ll start by setting up parameter-driven Glue/EMR jobs with modular Spark code for extraction from databases, APIs, and file systems, plus CloudWatch logging and clear retry logic. To reduce risk, I’ll build with unit tests, deterministic schema inference, and run end-to-end on your sample data early for row-count reconciliation and failure alerts. Which target destination do you prefer (S3 or Redshift), and should APIs require pagination handling? I can share an effort estimate and a rollout plan for deployment and scheduling.

@gavenl

I can build this as a production AWS PySpark ETL pipeline not just a script. My approach would be confirm the three source types and target store create parameter-driven Spark jobs with schema inference for CSV/JSON/Parquet land raw and curated data in partitioned S3 paths then load to Redshift or the analytics target you choose. I would include validation checks for source vs target row counts retry/error handling CloudWatch-visible logs and a README with runbook/config examples. For AWS infrastructure I can use Glue/EMR/Lambda/S3 and provide CloudFormation or Terraform templates depending on your preference. I would start with a small sample-data run then harden the pipeline for scheduling and monitoring.

@Appworldz

This is the right way to think about ETL on AWS, not as a one-off script, but as a production-grade data platform component. I’ve built PySpark/Glue pipelines handling mixed-format ingestion (CSV/JSON/Parquet), schema evolution, API/database extraction, partitioned S3 lakes, and Redshift loading with CloudWatch observability, retry orchestration, and IaC-driven deployment. I’d approach this with modular Glue jobs + reusable transformation layers, parameterized configs, centralized logging, and Terraform for reproducibility. Depending on source complexity and orchestration depth, this is realistically a 1–3 week build. Your budget/rate is aligned with senior AWS data engineering work. Happy to also include CI/CD and data quality validation if you want the pipeline truly production-ready long term.

@ngnsoftwareworld

‼️A1 Perfect Development, On-Time Delivery & Long-Term Support.‼️ Hello, I’m a Senior Full Stack & Data Engineer with strong experience building production-grade ETL/data engineering solutions on AWS using PySpark, Glue, EMR, Lambda, S3, Redshift, Terraform, and CloudWatch. Your requirement matches the type of scalable pipelines my team and I regularly deliver for analytics and reporting platforms. My Proposed Approach • Build a modular PySpark ETL framework with reusable extract/transform/load layers • Ingest data from APIs, databases, and file systems into S3 staging • Automatic schema inference + format detection for CSV / JSON / Parquet • Partitioned & compressed outputs (Parquet/Snappy preferred) for analytics efficiency • Implement Glue Jobs / EMR Spark processing depending on scale & cost optimization • Add retry logic, centralized logging, CloudWatch alerts, and parameter-driven execution • Infrastructure managed entirely through Terraform (or CloudFormation if preferred) AWS Stack AWS Glue / PySpark S3 Data Lake Redshift or S3 analytics target Lambda for orchestration/helper tasks CloudWatch monitoring & alerts IAM secure role-based access Terraform IaC Deliverables ✔ Production-ready ETL pipeline ✔ Fully documented PySpark codebase ✔ Terraform templates ✔ Logging, testing & monitoring setup ✔ Deployment walkthrough + support

@mohitsps

Drawing from my decade-long, end-to-end technical experience as a Technical Lead, I confidently assert that I am well-positioned to excel in constructing a fault-tolerant AWS PySpark ETL pipeline for your project. My Python expertise will be an asset in leveraging AWS native services such as Glue, EMR, Lambda, and S3 to extract and load data from disparate sources. This skill set extends to auto-detecting file formats (CSV, JSON, Parquet), inferring schemas, and achieving efficient column-wise writes. Moreover, I am well-versed in implementing robust engineering practices like modular Spark code structures for optimization of PySpark scripts or Glue jobs. I can guarantee parameter-driven jobs, unit tests, extensive logging for real-time analytics purposes, well-configured alerts and error-handling protocols. You can rely on my demonstrated ability to bring multidimensional projects to successful conclusion: the three deliverables outlined resonate with my prior work across backend development (e.g. Django), scalable APIs and high-load system design. Notably skilled in CI/CD strategies including CloudFormation or Terraform, my infrastructure-as-code services will be vital in efficiently setting up the required AWS resources. Finally, I understand the importance of a smooth transition post-development; I am committed to providing you with comprehensive documentation (README) for deployment and scheduling-alongside guide-oriented delivery and handover session.

@AtharvaITS01

Your AWS ETL requirements align well with our cloud integration and backend engineering experience. We can deliver a modular pipeline using Glue/PySpark, S3, Lambda, CloudWatch, and Terraform with automated ingestion, schema handling, partitioned outputs, monitoring, retries, and deployment automation. We would structure the solution around reusable Spark modules, configurable job parameters, and production-grade observability. Estimated delivery is approximately 5–10 weeks depending on transformation complexity, source connectivity, and Redshift optimization scope. Best, Anand

@laibatahir396

Being in the industry as a DevOps engineer for several years, I have built a strong foundation and expertise in Python, AWS, and data engineering which make me an ideal candidate for your PySpark ETL pipeline project. My extensive knowledge of AWS components like Glue, EMR, Lambda, and S3 provide me with the necessary skills to design a cloud-native ETL pipeline with maximum efficiency. In addition to being proficient in PySpark coding and developing parameter-driven jobs, I also prioritize modern engineering practices to ensure reliability and maintainability. My experience with infrastructure-as-code templates such as CloudFormation or Terraform will undoubtedly help set up all the required AWS resources seamlessly. I have always been committed to delivering high-quality solutions tailored specifically to my client's needs and expectations. Understanding the importance of end-to-end transparency, I'll make sure to provide you with detailed documentations such as README with execution steps, config examples, troubleshooting notes apart from hosting an interactive handover session. Best regards Laiba

@vijaydogra03

Hello Client, I can help you build a production-ready cloud-native ETL pipeline on AWS using PySpark, Glue/EMR, Lambda, S3, and Redshift. I have experience developing scalable ETL workflows that ingest data from databases, APIs, and file systems (CSV, JSON, Parquet) with schema inference, automated transformations, partitioning, compression, logging, retry handling, and CloudWatch monitoring. Deliverables include: • PySpark/Glue ETL jobs • Terraform or CloudFormation setup • Modular and parameterized code • Logging, alerting, and unit testing • Deployment documentation and handover support The pipeline will be fully deployable in your AWS account with end-to-end testing using your sample data. I’d be happy to discuss the architecture, timeline, and best AWS services for your workload. Best regards, Vijay

@karthikresonite

Hello, Greetings from Resonite Technologies! We have strong expertise in AWS-based data engineering, PySpark ETL pipelines, and production-grade cloud architectures. We can build a scalable, fault-tolerant ETL solution that ingests data from databases, APIs, and file systems into an analytics-ready AWS environment. Our proposed solution includes: ✔ PySpark/Glue-based ETL workflows ✔ Automatic schema inference & format detection (CSV/JSON/Parquet) ✔ S3/Redshift optimized loading with partitioning & compression ✔ Parameter-driven modular pipeline architecture ✔ Logging, monitoring, retry & alerting mechanisms ✔ CloudWatch integration for observability ✔ Infrastructure-as-Code using Terraform or CloudFormation Recommended AWS Stack: • AWS Glue / EMR • S3 + Redshift • Lambda for orchestration/triggers • CloudWatch for logs & alerts • IAM-secured architecture Deliverables: • PySpark scripts / Glue jobs • Terraform/CloudFormation templates • README & troubleshooting guide • Deployment walkthrough & handover session We focus on production reliability, scalability, maintainability, and cost-efficient AWS architecture design. Best Regards, Resonite Technologies

@awscertified007

There are 9 days of TOC present. Do you want all items in the TOC to be covered? We can discus and I can start on aws glue immediately