AWS Glue MySQL-S3 ETL

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in MySQL, Amazon Web Services, Big Data Sales, Hadoop, Data Cleansing, ETL, PySpark, Terraform and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@a4logic

Hello, With our extensive experience in AWS Glue and Terraform, we can seamlessly integrate your MySQL backend with the raw tier of your data lake. Our team has successfully implemented and deployed numerous similar MySQL-to-Parquet pipelines using AWS Glue and Terraform, ensuring the highest quality and efficiency in every project. We construct all the necessary infrastructure resources including Job, Connection, IAM role, catalog tables, optional crawler efficiently through Terraform for easy replication of stack in any account. Our familiarity with bulk data processing using Spark and PySpark enhances our ability to perform data cleansing accurately. By writing Parquet files into the specified S3 prefix after connecting to MySQL through JDBC, we ensure the preservation of raw data at its core while optimizing it for easier downstream analytics. Additionally, we understand that tailored documentations are essential and we guarantee a comprehensible README or inline comments that elaborate variables, scalability options, as well as customization of cleansing rules. As a renowned service provider company based in Pakistan, we prioritize client satisfaction and go to great lengths to ensure successful project delivery. We are not only confident about meeting your acceptance criteria, but also about providing you with an exceptional experience during our collaboration. Choose us for your AWS Glue MySQL-S3 ETL project and let us turn Thanks!

@C0RETECHIES

Hi there, You need a fully automated ETL pipeline. A Glue PySpark job will connect to MySQL, apply cleansing logic, and write data as Parquet files to your S3 raw tier. The entire Glue stack (connection, job, roles) must be defined and deployed from a single, reusable Terraform module. Technical approach: I'll build a Terraform module defining the aws_glue_connection, aws_glue_job, and a least-privilege IAM role. The PySpark script will use dynamic frames to read from JDBC, perform cleansing transformations, and write partitioned Parquet files. Job parameters will be passed from Terraform for reusability. Core modules: The deliverable includes the Terraform module (Job, Connection, Role), the parameterized PySpark ETL script for cleansing and Parquet conversion, and a detailed README for extending the system to more tables. Relevant systems: We have direct experience using Terraform to manage data-intensive AWS stacks involving RDS (MySQL) and S3 storage, mirroring the core components of your required infrastructure, such as in our TDTY project. Implementation strategy: I'll begin with a single-table MVP to validate the terraform apply workflow and the ETL run. From there, I'll refactor the script and module to be generic, ensuring it's easy to add new tables and customize cleansing rules as documented in the README. Regards, Rohit

@sanarena

Hi, I'm Armin Nikdel. I can deliver the Terraform module and PySpark Glue job to move your MySQL data into S3 as Parquet, fully automated with a single terraform apply. I'll define the Glue Connection, IAM role with least privilege, the PySpark job, and an optional crawler to catalog the output. The script will use JDBC with parameterized queries for safe extraction, apply server-side cleansing logic, and write partitioned Parquet to your raw tier. I'll include a README covering variables, table extension, and cleansing customization. One question: do you have a specific list of tables and cleansing rules, or should I design a config-driven approach that reads table names and transformation rules from a JSON file in S3?

@krupnovdenis

Hello! We can build the Glue side as a fully Terraform-managed ETL solution for this task. 1. Which MySQL tables should be included in the first run? 2. What cleansing rules should be applied before writing Parquet? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@mayankinnovative

As an experienced developer with two decades of experience and a specialization in PHP-based development, I can bring a unique skill set to your project. My extensive knowledge of MySQL and Terraform will prove invaluable in creating the Glue side needed for your MySQL-S3 ETL pipeline. I've not only created similar pipelines using AWS Glue and Terraform in the past, but I've also excelled at it. Being proficient in creating Terraform plans which need no manual edits to run successfully, you can trust me to deliver clean, optimized, and maintainable code that speaks to your project's specific needs. Moreover, my commitment to providing long-term support means that I won't merely complete the task at hand -- I'll consider long-term scalability and stability. With me on board, you can expect not only a glitch-free pipeline but also a clear README or inline comments explaining variables and rules flexibility for future expansion. If what you need is a dependable developer who understands your requirements quickly, delivers reliable results, and can provide continued support as your business scales, then there's no doubt we are an excellent fit for this project. Let's discuss how I can bring proven expertise to drive the successful completion of your project

@Zeeshan0489

Hello, I will deliver a reproducible Terraform module that provisions all AWS Glue assets (Job, Connection, IAM role, catalog tables, optional crawler) to move data from MySQL into Parquet files in S3. The Glue PySpark job will connect via JDBC, perform data cleansing, and write Parquet to your specified S3 prefix. The module will be defined so a single terraform apply recreates the stack in any account. A test run will copy a representative table, demonstrate cleansing, and confirm Parquet lands in the target path. I will include a README detailing variable definitions, how to extend to more tables, and how to customize cleansing rules. The deliverable includes a wrapper script to run plan/apply and inline comments for maintainability. This approach supports Big Data Sales objectives by enabling scalable ETL from MySQL to the raw data lake with Data Cleansing integrated. Best regards,

@onkarkulkarni8

Hi, I can help with your "AWS Glue MySQL-S3 ETL" project. I build clean, maintainable PHP backends — core PHP and Laravel/Symfony, MySQL schema design, and well-structured REST APIs. For work involving mysql, amazon web services, big data sales, hadoop, data cleansing, etl, pyspark, terraform, I pay close attention to validation, security, and readable code, delivering in small, testable milestones. You'll get clean commits and clear documentation. Can we discuss the specifics before I firm up the timeline? ⭐ 5.0/5 from a recent client: "Project was delivered before Time with Best professional Knowledge One could ever held. Thanks for the support" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@teodorgeorge

Hello, My experience covers Terraform based Glue pipelines from MySQL to S3, including JDBC reads, cleansing logic, and Parquet output. For this Amazon Web Services ETL, I can define the Glue Job, Connection, IAM role, catalog tables, and optional crawler so terraform apply recreates the stack cleanly. The PySpark script will copy a representative table, apply your cleansing rules, and write partitioned Parquet to the configured S3 prefix. I will keep the raw tier limited to conversion needs and add README notes for variables, more tables, Hadoop friendly layouts, and custom cleansing rules. Best regards, Teo

@kumarashish99

Hello, I can help you build a fully Terraform-managed AWS Glue ETL solution that connects to your existing MySQL database, performs configurable data cleansing using a PySpark Glue job, and writes optimized Parquet files to your specified S3 raw tier. I'll define all required Glue resources—including the Job, JDBC Connection, IAM Role, Data Catalog, and optional Crawler—in Terraform so the entire stack can be deployed with a single `terraform apply` in any AWS account. I have extensive experience building AWS data pipelines using Glue, S3, Terraform, IAM, and JDBC integrations. The solution will include clean, reusable Terraform modules, configurable variables, a well-documented PySpark ETL script, partitioning where appropriate, and a README explaining deployment, onboarding additional tables, and customizing cleansing logic. The final deliverable will be production-ready, reproducible, and easy to maintain. Thanks

@sahilcomputers39

Hi, With 16+ years of experience in AWS, DevOps, and Infrastructure as Code, I have built and automated scalable ETL pipelines using AWS Glue, Terraform, S3, RDS/MySQL, IAM, and PySpark. For your project, I can create a fully Terraform-managed AWS Glue solution that provisions the Glue Job, Connection, IAM Role, Data Catalog, and optional Crawler. The Glue job will securely connect to MySQL via JDBC, perform the required data cleansing, and write optimized Parquet files to the specified S3 location. I'll ensure the solution is modular, reusable, and deployable with a single terraform apply across environments. The deliverables will include clean Terraform modules, well-structured PySpark scripts, configurable variables, and comprehensive documentation explaining deployment, customization, and onboarding additional tables. I will also validate the pipeline with a successful end-to-end test and ensure the output is analytics-ready. I've delivered similar AWS automation and ETL solutions focused on reliability, scalability, and maintainability, and I'm confident I can provide a production-ready implementation. We can discuss the budget once we connect. Best regards, SaD

@TechizeBuilder

Dear Client, I read "AWS Glue MySQL-S3 ETL" carefully and understand you want a job portal that connects employers and candidates effectively. My hands-on experience with MySQL, Amazon Web Services aligns directly with what you need. I've built recruitment platforms with job posting, resume upload and search, filters, applications tracking and employer dashboards — fast search is what makes these portals win, and mine are fast. A few quick questions to get us started: 1. Will employers post jobs directly, or will admins manage listings? 2. How will it earn — featured jobs, resume database access, subscriptions? 3. Which region/industry are you targeting first? Thanks & Regards, Deepak

@toriquldev123

Hello There! I’m Md. Toriqul Islam, and I’m excited to partner with you. I can dive into your project immediately. I’m an experienced AWS Data Engineer & DevOps Developer with 10+ years of experience building infrastructure-as-code, ETL pipelines, and cloud data platforms using Terraform and AWS services. I understand you need a fully Terraform-managed AWS Glue pipeline that connects to your existing MySQL database, performs configurable data cleansing, and writes optimized Parquet files to S3. I’ll deliver reusable Terraform modules, Glue PySpark scripts, IAM configuration, Data Catalog resources, optional Crawlers, and clear documentation for future expansion. I’m skilled in Terraform, AWS Glue, PySpark, MySQL, S3, IAM, JDBC, Data Catalog, ETL Pipelines, and Infrastructure as Code. I’m ready to start immediately and would be happy to discuss your Glue architecture and project requirements. Looking forward to hearing from you. Best regards, Md. Toriqul Islam

@shadabkhan92

As highly-seasoned AI experts, my team and I would be an ideal match for your AWS Glue MySQL-S3 ETL project. We have a robust background in building production infrastructure that combines agentic AI with practical functionality. Our inclusion of Odoo ERP, IoT hardware, and full-stack web and mobile engineering sets us apart in our ability to implement AI systems that work seamlessly inside existing workflows. In line with your specific requirements, we’re well-versed in AWS services and capable of maneuvering through their infrastructure with ease. In addition to having prior experience crafting ETL pipelines on AWS Glue, employing Terraform to script the entire Glue stack is also one of our specialties. You can trust that a single terraform apply will recreate the entire ecosystem successfully in any account. Moreover, we are adept at leveraging PySpark for the cleansing and conversion tasks you've outlined. Our impeccable record of executing similar projects assures you an efficient MySQL-to-Parquet pipeline implementation. We create thorough README or inline comments that users find helpful in grasping project variables, how to extend to more tables, cleansing rules customization. To summarize, if you want more than just AI prototypes but an AI-laced practical system that actually ships and performs as expected, then we are your best bet!

@MIndlabsAi6

Hey there! I'm really pumped about this opportunity! I recently led a project with similar challenges and nailed it. Drawing from my experience in MySQL, Amazon Web Services, Big Data Sales, Hadoop, Data Cleansing, ETL, PySpark, Terraform, I’m ready to dive into your project. Please initiate a chat for further discussion. Cheers, Vishal Maharaj

@vishal1145

Your Terraform already handles networking, but without a proper Glue catalog strategy your raw tier will become a mess of orphaned Parquet files that analysts cannot query efficiently. If you do not define partition keys and schema evolution rules now, you will rewrite this pipeline in six months. Quick questions - are you planning incremental loads with watermark columns, or full table snapshots on each run? And do you need the crawler to auto-detect schema changes, or will you version the catalog tables manually? Here is the architectural approach: - TERRAFORM: Build reusable modules for aws_glue_job, aws_glue_connection, aws_glue_catalog_table with dynamic partitioning variables so you can onboard new tables without duplicating code. - PYSPARK: Write a parameterized Glue script that reads JDBC via dynamic frames, applies your cleansing transformations, then writes partitioned Parquet using relationalize() to handle nested structures without flattening loss. - ETL ORCHESTRATION: Configure job bookmarks to track processed rows and prevent duplicate writes, plus CloudWatch alarms on DPU consumption so runaway queries do not burn your budget. I have built three similar MySQL-to-S3 pipelines for fintech clients processing 50M+ rows daily, all fully IaC with zero manual console clicks. Let's schedule a 15-minute call to walk through your cleansing logic before I template the Terraform.

@iimtpradeepdash

I write Terraform modules for ETL Glue jobs like this on most data lake builds. The critical piece is mapping Glue resources and IAM roles so the stack is always reproducible, no manual edits. My last project with this setup: built a MySQL-to-S3 Parquet pipeline, PySpark script as the job, catalog tables auto-created and partitioned, crawlers optional by flag. Code documented to allow new tables/datasets just by editing a variable. Most recent case was for an analytics client moving from RDS to a raw zone lake on AWS. You’d get one Terraform module including Glue Job, Connection (JDBC string/creds as variables), IAM, optional crawler, everything parameterized. PySpark for cleansing, dynamic frames for table generalization, partitioned Parquet as output. Any custom data cleansing logic you want beyond null-trim/date normalization? Can start this week—just confirm the Terraform version you use. Pradeep

@badarmadni927

Hi — Badar Madni here. You're building a data pipeline that moves data from a MySQL backend into a data lake using AWS Glue, with the infrastructure defined in Terraform. This involves not just building the Glue job, but also ensuring it connects to MySQL via JDBC and performs the required data cleansing, which can be complex due to the need for efficient data processing and handling potential errors. My approach would focus on creating a modular Terraform configuration that provisions all necessary Glue resources, including the job, connection, and IAM role, while also implementing the data cleansing logic in PySpark to produce Parquet files. A few questions to better understand the scope: Q1 – What is the expected volume of data to be processed by the Glue job? Q2 – Are there any specific data cleansing rules that need to be applied? Q3 – How should the Parquet files be partitioned for efficient downstream analytics? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you. Badar Madni

@devendrathakur12

Hi, I recently built a data pipeline where Terraform provisioned the complete infrastructure while AWS Glue transformed relational data into optimized Parquet files for analytics. The infrastructure was fully reproducible and easy to extend. I can deliver your Glue Job, JDBC connection, IAM roles, catalog resources, optional crawler, and PySpark script with clean Terraform modules and documentation. The code will be modular so adding more tables later is straightforward. One question: should the Glue job support incremental loads using timestamps or IDs, or is the first version expected to perform full table loads only? Looking forward to making your terraform apply do all the heavy lifting. Best regards, Dev S.