Forecasting Pipeline & React Integration

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Real-Time Forecasting Backend for Your React UI ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for someone to connect your React + TanStack Start UI to a real-time forecasting backend. Look no further; Zohaib is here to help you! My team has successfully completed over 50 similar projects related to data processing and machine learning. I will create a robust system to handle your CSV feed, ensuring it's ready for future data formats without any changes to your business logic. ➡️ Why Me? I can easily build your forecasting backend as I have 5 years of experience in data engineering and machine learning. My skills include data ingestion, storage solutions, and machine learning model training. Additionally, I have a strong grip on technologies like Airflow, Redis, and DuckDB, ensuring a smooth and efficient workflow for your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you the quality of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Data Ingestion ✅ Machine Learning ✅ Python Programming ✅ Airflow Orchestration ✅ DuckDB/Polars ✅ LightGBM ✅ Data Storage Solutions ✅ Redis Caching ✅ TypeScript ✅ Terraform ✅ End-to-End Testing ✅ API Development Waiting for your response! Best Regards, Zohaib

@Mahihassi

I'm Sardar Hasnain and I believe I'm the right fit for your project. My specialization in building scalable backend systems, combined with my strong Python skills and experience in ML makes me equipped to tackle your need for a real-time forecasting backend. Having worked on multiple AI and Cloud-based projects, I create solutions that effectively integrate AI models, cloud infrastructure, and user-friendly dashboards - exactly what you require. Moreover, I have hands-on experience with data ingestion and processing, which is crucial when dealing with the daily POS feed of 100 stores and 10k SKUs per store. I am well-versed in designing scalable backend architectures and have successfully built REST APIs and microservices as well - expertise you need to INTEGRATE the pipeline with your existing storefront.

@Microlent

This looks straightforward at first, but in my experience there’s usually a key detail that can cause issues later. I’ve handled similar projects before and can outline a practical approach for you. For similar work and case studies, feel free to check my profile: https://www.freelancer.com/u/Microlent Let me know if you I'd like me to walk you through the plan. – Rajesh Rolen

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Herculesz

Hi I have hands-on experience building forecasting pipelines where single-node DuckDB/Polars workflows are designed cleanly enough to move into Spark/EMR without rewriting the business logic. The main technical risk here is keeping ingestion, feature engineering, model training, forecast serving, and UI requests separated properly while still supporting real-time responses for 100 stores and high SKU volume. I can structure the backend with adapter-based feed ingestion, partitioned Parquet storage in S3, DuckDB/Polars local query layers, Redis caching, and Airflow DAGs for store/SKU-based orchestration. For forecasting, I can implement LightGBM for demand patterns with stronger feature signals and Croston/TSB methods for intermittent demand, with typed Python modules and pytest coverage. On the frontend side, I can wire TanStack Start createServerFn adapters so the React UI can request forecasts through a clean TypeScript API layer. I will also make sure the end-to-end path is fully tested with Playwright, Vitest, and pytest so ingestion, prediction, retrieval, and UI display are validated together. The architecture will include Terraform/IAM definitions and a clear Spark migration guide showing which execution layers change and which business logic stays untouched. Thanks, Hercules

@kamran2012

I can help with this, I will deliver the full forecasting pipeline — CSV ingestion with a pluggable format layer, Parquet-on-S3 storage queried via DuckDB/Polars, LightGBM + Croston/TSB model training orchestrated through Airflow, and TypeScript server adapters wired into your TanStack Start frontend via createServerFn. One architecture detail worth nailing early: I will isolate all compute behind a thin executor interface so the Spark migration is purely a backend swap — no DAG rewrites, no API changes. The migration guide will map each DuckDB/Polars call to its PySpark equivalent with partition strategy notes for 100-store fan-out. Questions: 1) What is the current POS feed frequency and average file size per store? 2) Are Terraform state and CI already managed somewhere, or will I set those up as well? Looking forward to talking through the details. Kamran

@koolvishruth

With over 7 years of experience in developing scalable software and AI solutions, I am excited to offer my skills for your project. My expertise in Artificial Intelligence (AI) and Machine Learning (ML) is perfectly aligned with your need for forecasting backend capabilities. I have hands-on experience setting up a ML pipeline of similar scale and complexity, ensuring it is production-ready while maintaining flexibility for future growth. Additionally, my proficiency in leading edge technologies such as DuckDB/Polars and LightGBM aligns perfectly with your data storage and retrieval requirements, and my capacity to deliver compartmentalized code that easily translates into PySpark on EMR Serverless sets me apart from other candidates. My passion for automation is a valuable asset in furthering the efficiency of your project. Let's connect

@corpmember29

Hello, I have 10+ years of experience in scalable ML pipelines, forecasting systems, data engineering, and full-stack development with Python, TypeScript, React, Airflow, AWS, DuckDB/Polars, Redis, and Spark ecosystems. I have worked on production-grade forecasting and analytics platforms handling large-scale SKU/store segmentation with clean migration paths from single-node architectures to distributed Spark environments. I can structure the pipeline exactly as required — modular CSV ingestion with future-ready adapters for JSON/Parquet, partitioned Parquet storage on S3, Redis caching, Airflow orchestration, and real-time forecast serving integrated directly with your existing React + TanStack Start UI using createServerFn. The solution will include fully tested ingestion, ML prediction flows, storage/retrieval validation, and complete UI-to-API end-to-end testing using Playwright, Vitest, and pytest with CI integration. I will also provide Terraform infrastructure, IAM policies, typed documentation, deployment-ready DAGs, and a concise Spark migration guide for EMR Serverless scaling. I will provide complete source code, clean architecture, and 2 years of free ongoing support after delivery. We will follow Agile methodology with regular updates and assistance from development to deployment. I eagerly await your positive response. Thanks

@devendrathakur12

I've built almost this exact pipeline recently — a single-node DuckDB/Polars and LightGBM forecasting stack with intermittent-demand handling, orchestrated by Airflow and served into a TanStack Start frontend. Two questions. For the 10k SKUs per store, how sparse is demand at the store-SKU level, since a high share of zero-sales days is what decides where Croston/TSB beats LightGBM and how we segment models? And do forecasts need to be precomputed nightly and cached, or genuinely computed on-demand per request, since at 1M store-SKU pairs precompute-plus-Redis will hold the real-time UX far better than live inference? Suggestion: I'll keep the compute engine behind a thin interface (read, transform, train) so the Spark migration is a swap of the execution layer, not a rewrite of business logic. I'll also precompute forecasts in the Airflow DAG and serve them from Redis through createServerFn, so the storefront gets sub-second responses and we avoid hammering DuckDB on every round-trip. Plan: I'll build CSV ingestion into partitioned Parquet on S3 with the format abstracted first. Then the LightGBM and Croston training and serving package with tests. Finally, Airflow DAGs, TypeScript adapters, Terraform, and the green CI suite end-to-end. Happy to share the Spark migration approach from the last build on a quick call. Best, Dev S.

@topcreator100

Hi there, I’ve read your Forecasting Pipeline and React integration project and I’m confident I can wire 100 stores and 10k SKUs per store to a real-time forecasting backend. I’ll design an ingestion layer that handles CSV now and swap to JSON/Parquet later without touching business logic, store raw and features in partitioned Parquet in S3, and use DuckDB/Polars with Redis caching for fast access. The solution will train and serve forecasts with LightGBM and Croston/TSB, orchestrated by Airflow, and exposed via a createServerFn for real-time React consumption, with a clean single-node pipeline ready for a future Spark transition. I’m interested, have hands-on experience with similar pipelines, and will deliver a CI-tested Python package, Airflow DAGs, TS adapters, and a migration guide;

@Muhammadzeesha59

Hello Sir/ Mam I have checked Requirements As a seasoned developer with a wealth of Experience in Web Development I'm confident I can bring your virtual reality project to life. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thank you !

@Neelumsoft

With my extensive experience in full-stack development and a track record of successful projects, I believe I am the right individual for this job. From website development to hybrid application design, I have honed my skills over the years to help businesses automate complex processes and enhance their online presence. A key component of my expertise lies in integrating APIs into various applications - a skill that aligns directly with the task at hand: plugging a TypeScript server adapter into your TanStack Start frontend. Additionally, I understand the importance of long-term scalability and adaptability. My ability to assess a project from both short-term and long-term perspectives enables me to deliver a solution that is production-ready today while being easily lifted to PySpark on EMR Serverless when needed. This translates into time and cost-savings for you as changes can be made without tampering with business logic. My aim is to leverage my technical knowledge and problem-solving ability to fulfill all your project requirements while providing excellent customer service throughout. Given my relevant skills and achievements as authenticated by Freelancer.com directory's Top 3% standing, hiring me for this venture would be an asset to your team!

@maxscender

I can help you. Here's how I'd approach this. Your SOW is well-structured, but the real risk isn't building the pipeline — it's the Spark-readiness constraints in Section 7. Most developers will nail the single-node work and leave you with a rewrite when you hit 250 stores. I'd enforce those rules as automated lint checks in CI, not just code review conventions. Specifically: a custom pylint rule that flags `iterrows`/row-wise `apply`, a test that asserts every Parquet write is partitioned, and a contract test confirming feature functions accept both Polars and PySpark DataFrames. For the serving layer, your 50ms p95 on Redis is straightforward, but the 400ms Parquet fallback needs care — DuckDB over S3 with predicate pushdown on `region=X/date=Y` partitions gets you there, but only if partition sizes stay under ~128MB and you pre-warm the DuckDB catalog on Airflow DAG completion rather than on first request. One thing your SOW doesn't address: schema evolution. When you add promo lift or weather features in Phase 3, the Zod/Pydantic single-source-of-truth setup needs a versioning strategy now, or you'll break the contract tests on the first new column. I'd set up a schema registry pattern from day one — trivial to add, painful to retrofit.

@Sohail748

Hi there, Before implementation, I can provide a free pipeline architecture review covering ingestion, storage layers, forecasting flow, caching, Airflow orchestration, and the future Spark migration path so the build starts with clean boundaries. Your scope fits a backend-heavy ML forecasting pipeline. I can structure the system with Python using Polars/DuckDB for single-node processing, partitioned Parquet on S3, Redis caching, and LightGBM + intermittent-demand models such as Croston/TSB for forecast serving. The ingestion layer can be abstracted so CSV works first while JSON/Parquet can be added later without rewriting business logic. I would also wire the forecasting backend into your existing React/TanStack Start UI through TypeScript server adapters, with end-to-end coverage across ingestion, prediction, storage retrieval, and UI-to-API round trips using pytest, Vitest, and Playwright. As an added value, I’ll include a concise Spark/EMR migration guide showing which modules stay stable and which compute adapters change when moving from single-node processing to distributed execution. Regards, Sohail Jamil

@a4logic

Hello, I appreciate your consideration for my team at Our Software to take on this forecasting pipeline and React integration project. We have extensive experience in handling web development projects using the latest technologies, including JSON - a key aspect of this project's scope. By leveraging our skills in data processing, storage, Airflow implementation, and API development, I am confident we can successfully build the entire backend pipeline for you. Our ability to work with various data formats such as CSV, JSON, and Parquet without affecting the business logic fulfills one of your project requirements perfectly. We prioritize scalability and flexibility in our solutions, which is evident in our expertise with DuckDB/Polars for data querying and Redis caching. You can trust us to not only train and serve forecasts using LightGBM and Croston/TSB but also ensure Airflow orchestrates these processes efficiently. Delivering high-quality results is always our top priority; we undertake comprehensive end-to-end testing with various frameworks like Playwright, Vitest, and Pytest to confirm the stability of all aspects of the pipeline. And while we certainly have hands-on experience with single-node ML pipelines like yours, we're equally adept in scaling them using tools like PySpark on EMR Serverless when required. You can be confident that choosing us would mean getting production-ready code that will gracefully facilitate futu Thanks!

@Hubready

Hello!, I am a Florida-based senior software engineer with extensive experience in building and scaling production-grade software. I carefully read your project description regarding the Forecasting Pipeline and React integration and believe I can help you achieve your goals. With over 15 years in the field, my expertise includes Python, data processing, and API development, which aligns perfectly with your requirements. I have successfully integrated similar systems that involve real-time data processing and user-friendly interfaces. Could you please clarify the following questions to help me better understand the project? 1. What specific data sources do you need to integrate into the pipeline? 2. Are there particular performance benchmarks or KPIs you're aiming to achieve? In the past, I’ve worked on projects like a data analytics dashboard for a retail platform and a machine learning model for sales forecasting. I suggest breaking the project into phases: first, defining the data flow and requirements, then API development, and finally, integration with your existing React setup. This structured approach will ensure a seamless implementation. I am committed to detail and delivering results that fit your vision. Let’s connect to discuss how I can contribute to your project! -James

@dfordesigners

Hi, I have extensive experience in building and scaling ML pipelines, specifically transitioning from single-node to Spark environments. I have successfully integrated real-time forecasting backends with React UIs, utilizing technologies like LightGBM, Croston/TSB, Airflow, and Redis. My approach ensures seamless data ingestion, processing, and storage, with a focus on maintainability and scalability. I can deliver the required Terraform definitions, Python packages, Airflow DAGs, server adapters, and testing suites, meeting all outlined requirements. Let's discuss how I can bring your Forecasting Pipeline & React Integration project to life.

@mogsten

Hi, I am interested in your project because I have hands-on experience building scalable ML forecasting pipelines that integrate Python-based feature engineering, LightGBM models, and production data orchestration using Airflow, along with cloud infrastructure automation via Terraform. I will implement a clean, modular architecture that ingests your POS CSV feeds and structures the pipeline so it can later switch to JSON or Parquet without affecting core business logic, ensuring long-term maintainability. My approach includes storing raw and processed data in partitioned S3 Parquet format, enabling fast local querying via DuckDB and Polars, with Redis caching for real-time API performance. I will design and deploy forecasting models using LightGBM combined with Croston and TSB methods, fully orchestrated through parameterized Airflow DAGs for store and SKU-level scalability. The React + TanStack Start frontend will be connected through typed TypeScript server adapters using createServerFn, enabling real-time forecast retrieval with full end-to-end test coverage using Playwright, Vitest, and pytest in CI. I will also deliver Terraform modules, IAM policies, and a clear Spark migration guide to ensure seamless future scaling to EMR Serverless. Let’s connect so I can review your current setup and start building the pipeline immediately. Alexander

@SmartCodeDev

Hello, I have hands on experience building scalable ML forecasting pipelines with Python, Airflow, DuckDB/Polars, Redis caching, AWS infrastructure, and migration paths from single node processing to Spark based distributed systems. I can build your forecasting backend with clean ingestion abstractions for CSV/JSON/Parquet, partitioned Parquet storage on S3, LightGBM and Croston/TSB forecasting pipelines, and real time forecast serving integrated directly into your TanStack Start frontend through typed TypeScript adapters. The architecture will be production ready while remaining Spark migration friendly, with clear separation between ingestion, feature engineering, training, inference, orchestration, and storage layers to support future EMR Serverless scaling. I can also deliver Terraform infrastructure, Airflow DAGs, CI integrated Playwright/Vitest/pytest coverage, and a concise Spark migration guide covering compute, storage, orchestration, and execution layer changes.