ETL Cleansing Small Task

@vuyiswankosi

Greetings. I understand that the core of the task is building a self-contained pipeline for ETL. I will build a script with a command line interface that performs the whole process. It will use a .ini configuration file to simplify management of parameters. The design will be modular, storing different steps and filters of the process in separate single-function classes that are composed together in the main pipeline. This will allow easy rearranging and extension by simply adding a new class with its own transformation. I will then document the general design and how to setup and run it. --- I am available to begin immediately and work until completion. Contact me if you wish to continue. Thanks.

@AhmadXpert2410

Hi there, I can build a reliable, automated ETL pipeline to ingest your CSV and JSON files, clean the data, validate every record, and load only schema-compliant rows into your environment. With strong experience in Python (pandas/PySpark), data validation frameworks, and scheduled pipelines, I’ll implement duplicate checks, type enforcement, out-of-range flagging, and text normalization with fully reproducible scripts. The solution will include modular ETL functions, automated triggers (CLI or scheduler), clear logging, and a sample run showing before/after datasets so you can verify accuracy. Everything will be documented so you can deploy locally or in cloud setups easily, and I’ll highlight optional future improvements like aggregations or advanced joins. You’ll receive a production-ready ETL script, setup guide, and test output to ensure smooth execution on new file batches. Happy to review your schema rules and file samples first so I can deliver a clean, maintainable pipeline quickly. Regards, Ahmad

@AZTURK1

Hi there, I understand you need a reliable, automatable ETL to ingest CSV/JSON, perform strict cleansing and schema validation, and produce clean output ready for loading. I’m confident I can deliver a reproducible, documented pipeline using Python (pandas or PySpark) or a tool of your choice. - Deliverable 1: Well-documented ETL script/job (CSV + JSON ingestion, dedupe, type enforcement, text normalization) - Deliverable 2: Sample run with before-and-after records and logs showing flagged/removed rows - Deliverable 3: Setup & deployment instructions (CLI / scheduled job / cloud function) and a small test harness Skills: ✅ ETL ✅ PySpark / Python (pandas) ✅ Data cleansing workflow (dedupe, type casting, normalization) ✅ Scheduled job / CLI / cloud function deployment ✅ Validation & schema enforcement (reject/flag non-compliant rows) Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I can start immediately and deliver the first working version within the agreed timeline. Do you have an existing schema (CSV headers and JSON schema) or sample files I can use to build rules, and do you prefer pandas (local/small batches) or PySpark (for larger volumes)? Best regards,

@viettungma

Hi there, I can build a robust, automated ETL pipeline for your CSV and JSON files using Python and pandas. Here is exactly how I will deliver this: Ingestion & Cleansing: I will write a script to ingest your flat files, remove duplicates, enforce strict data types, normalize text fields, and flag any out-of-range values. Validation: I will set up validation checks to ensure only schema-compliant rows make it to your final output dataset. Deliverables: You will receive the clean Python script, a before-and-after sample run demonstrating the changes, and a clear README with setup instructions so you can easily trigger the job via CLI or schedule it. I have strong experience in data parsing, automation, and building reproducible scripts. I am ready to review a sample of your raw files and deliver the first working version within 48 hours. Best regards, Tung

@Hemantt841

I understand you're looking for a reliable ETL pipeline to handle CSV and JSON files, focusing on data cleansing and validation before loading into your environment. The need for thorough cleansing rules, such as removing duplicates and enforcing data types, is clear, along with

@w6ri

I can deliver a reproducible, automated ETL pipeline using Python (Pandas & Pydantic). This stack ensures strict schema validation and high-performance text normalization. I will provide a Dockerized solution to guarantee the script runs identically in your environment as it does in mine Just have few questions to ask What is the average size of the files per batch? Where should the data be loaded (e.g., a SQL database, S3 bucket, or a final clean CSV)? How often will this run (hourly, daily, or real-time)? If one row fails validation, should the entire job stop, or should we process the valid rows and log the failures?

@clarencer20

Hi , Good morning! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Pandas, Data Cleansing, Python, JSON, Pentaho, PySpark and ETL. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Thanks for giving opportunity

@gsinfotechopcp4

Hello! I am excited about the opportunity to work on your project involving the development of a reliable ETL pipeline for ingesting, cleaning, and validating CSV and JSON files on a regular schedule. The core tasks include reading multiple flat-file formats, applying data-cleansing rules, and running validation checks to ensure only clean, schema-compliant rows are loaded into your environment. I am proficient in Python (pandas, PySpark), Talend, and other ETL tools, and I am committed to delivering a reproducible solution that can be triggered automatically. My focus will be on providing a well-documented ETL script or job configuration, a sample run for demonstration, and setup instructions for seamless deployment in your environment. Regards, gsinfotechopcp4

@PyMLExpert1

With my expertise in Python and invaluable experience with ETL pipelines, I assure you of a reliable and highly efficient solution to your CSV and JSON data integration, cleaning, and validation tasks. By employing the power of tools like pandas and PySpark among others, I will ensure that every step of the pipeline is executed flawlessly. My proficiency in managing complex algorithms adds another layer of quality to ensure data accuracy and consistency. Creating well-documented scripts is something I take very seriously as it allows for effective future maintenance. I understand the importance of reproducibility and automatic triggers, so be assured that your pipeline will run seamlessly without manual intervention. Moreover, l will provide comprehensive instructions empowering you to deploy the solution effortlessly on any environment you choose. To exhibit my capabilities while addressing your needs accurately, I propose furnishing you with a complete end-to-end sample run providing a clear view of transformation applied to the data before and after. I'm dedicated to your project's success and I'm ready to start immediately. Let's work together to create an ETL pipeline that empowers your business for the long term.

@yueshenglin

[Input Folder / S3] ↓ File Ingestion Layer ↓ Standardization Layer ↓ Cleaning Layer ↓ Validation Layer ↓ Load Layer ↓ Audit Log + Rejected Records

@mindmagic1

नमस्ते, मैं समझता हूँ कि आपको एक भरोसेमंद ETL पाइपलाइन की ज़रूरत है जो रेगुलर CSV और JSON फ़ाइलें लेती है, डेटा को अच्छी तरह से साफ़ और वैलिडेट करती है, और आपके एनवायरनमेंट में सिर्फ़ स्कीमा-कम्प्लायंट रिकॉर्ड लोड करती है — पूरी तरह से ऑटोमेटेड और रिप्रोड्यूसिबल। मेरा तरीका यह होगा कि इसे Python में pandas (या अगर वॉल्यूम को स्केलिंग की ज़रूरत हो तो PySpark) का इस्तेमाल करके बनाया जाए। पाइपलाइन एक साफ़ स्ट्रक्चर को फ़ॉलो करेगी: इनजेक्शन → स्कीमा एनफोर्समेंट → क्लीनिंग (डीडुप्लीकेशन, टाइप कास्टिंग, रेंज चेक, टेक्स्ट नॉर्मलाइज़ेशन) → वैलिडेशन लेयर → क्लीन आउटपुट + स्ट्रक्चर्ड एरर लॉग। इनवैलिड रो को सटीक फेलियर कारणों से आइसोलेट किया जाएगा ताकि कुछ भी साइलेंटली ब्रेक न हो। सॉल्यूशन को CLI के ज़रिए ट्रिगर किया जा सकता है और शेड्यूल किया जा सकता है (क्रॉन, टास्क शेड्यूलर, या आपके एनवायरनमेंट के आधार पर क्लाउड फ़ंक्शन)। मैं अच्छी तरह से डॉक्युमेंटेड कोड, डेटासेट से पहले/बाद में दिखाने वाला एक सैंपल रन, और स्टेप-बाय-स्टेप सेटअप इंस्ट्रक्शन दूँगा ताकि आप इसे बिना किसी दिक्कत के लोकली या क्लाउड में डिप्लॉय कर सकें। अगर आप उम्मीद के मुताबिक फ़ाइल साइज़ और टारगेट स्टोरेज (DB, वेयरहाउस, फ़्लैट आउटपुट) शेयर करते हैं, तो मैं पहले वर्किंग वर्शन के लिए स्टैक और टाइमलाइन कन्फ़र्म कर सकता हूँ। शुभकामनाएँ,

@amanh96

Subject: Professional ETL Pipeline for Automated Data Cleansing & Validation Hi there, I have reviewed your requirements for a reliable ETL pipeline to ingest, clean, and validate flat files. I specialize in building automated data workflows using Python and Pandas, and I can deliver a solution that ensures only high-quality, schema-compliant data enters your environment. My Technical Approach: Automated Ingestion: I will develop a script to automatically read multiple CSV and JSON formats from your source directory. Thorough Data Cleansing: I will implement logic to remove duplicates, enforce strict data types, normalize text fields, and flag out-of-range values as requested. Validation Layer: I will include a "gatekeeper" step that runs validation checks so that only clean, schema-compliant rows proceed to the final load. Reproducible Automation: The final solution will be a well-documented script that can be triggered via CLI or a scheduled job (Cron/Task Scheduler). Deliverables I will provide: Documented ETL Script: A clean, modular Python script using Pandas. Validation Report: A sample run demonstrating "before-and-after" records to prove cleansing accuracy. Deployment Guide: Clear instructions for local or cloud setup. Estimated Timeline: I can have the first working version ready for your review within 24–48 hours. I am ready to start immediately. Could you please share a sample of the data or the specific schema you need to be enforced? Best regards, Aman Kumar

@Lidiya299

I already build modular ETL pipelines in Python. Your task matches my current workflow: - ingest CSV/JSON - data cleaning and normalization - schema validation - reproducible pipeline with clear structure and documentation. I can deliver a clean ETL pipeline that is easy to run locally or automate later via scheduler. Happy to share a sample structure and timeline if needed.

@matinh7

Hey! I noticed there's a lack of details in the project description, but I'm ready to jump in and tackle whatever challenges it presents. With over a couple of years working in similar projects, I can quickly adapt and help you out. If we can chat through your needs a bit, I'd be happy to use my usual toolbox for project management and communication tools—maybe something like Trello or Slack if that works for you. I'm pricing this lower than usual since I'm new to this platform and looking to gather some initial reviews. Just let me know what you’re aiming for. You'd get updates regularly (like code snapshots and setup guidance), and it’d be great if we could touch base after the first deliverable. Here are a few quick questions: 1. What specific goals do you have? 2. Are there any deadlines I should be aware of? 3. Do you have preferred tools already in mind? Looking forward to your reply! Best, Matin.

@purityp9

"Hi! As a new freelancer in data cleansing, I bring: - *Eagerness to learn* and adapt to your project needs - *Attention to detail* and a passion for clean data - *Strong communication* to ensure clear expectations - *Competitive rates* as a starting freelancer I'm excited to prove my skills and deliver quality work! "

@ashishk9801

Hi, I can build a reliable, reproducible ETL pipeline to ingest your CSV/JSON files, apply strict cleansing and validation rules, and load only schema-compliant records into your environment. I am already experienced in performing this type of data cleansing as I do it on a daily basis.

@Himanshuv76

Hello, I can build a reliable and automated ETL pipeline to process your scheduled CSV and JSON files efficiently. Using Python (pandas or PySpark, depending on volume), I will design a reproducible workflow that ingests multiple flat-file formats, applies robust data cleansing rules, and validates each record before loading. The pipeline will remove duplicates, enforce strict data types, normalize text fields, and flag or isolate out-of-range or invalid values. Only schema-compliant and fully validated records will proceed to the final load step. The solution can be triggered via CLI, scheduled job (cron), or deployed as a cloud function based on your environment preference. Deliverables will include a fully documented ETL script, a sample run showing before-and-after data comparison, and clear setup instructions for local or cloud deployment.

@PurarateyTech

Hello! I am an experienced Python Developer with 3 years of expertise in building scalable data pipelines and backend systems. I specialize in transforming messy, multi-format raw data into high-quality, schema-compliant datasets. My Proposed Stack: I recommend a Python-based pipeline using Pandas for high-performance manipulation and Pydantic for rigorous data validation. This ensures the solution is lightweight, easily containerized, and can be triggered via CLI, Cron, or Cloud Functions (AWS Lambda/GCP). My Professional Approach: Ingestion: Automated handling of CSV and JSON files with dynamic schema mapping. Deep Cleansing: Custom logic for deduplication, whitespace normalization, and type enforcement. Strict Validation: Every record is checked against a defined schema; invalid rows are logged/quarantined rather than crashing the script. Automation: I will provide a setup that is ready for scheduled execution (Local or Cloud). Deliverables: Modular ETL Script: Well-documented, clean Python code. Validation Reports: A "Summary of Run" showing records processed vs. flagged. Deployment Guide: Step-by-step instructions for your specific environment. Timeline: I can deliver a first working version, including the sample run, within 3–4 days. Questions: What is the approximate volume of data (file size/row count) per batch? I am ready to start immediately and ensure your data remains clean and reliable. Best regards, Rahul Kumar Python & AI Engineer

@dhirajbagav

Will do it have good knowledge in Etl tools it is very easy we will do it bhjjkkkkkkkkvvcfftyyhbbvvbbb