Real Estate Data Pipeline (Venezuela) — Phase 1: Apify Scraper + AWS PostgreSQL

@diektech

Hi — Elias here from Miami. I understand you’re looking to build a multi-source real estate data pipeline starting with an Apify scraper and AWS PostgreSQL integration. This is a crucial step for gathering accurate and timely data. What usually matters most here is ensuring that the data collection process is robust and scalable. A common issue in systems like this is managing data consistency across multiple sources. The tricky part is often handling the integration between the scraper and the database while maintaining performance. To approach this, I would design a modular architecture that separates the scraping logic from data storage. This allows for easy updates or scaling of components without affecting the overall system. Leveraging AWS will help maintain reliability and scalability as the project grows. I've worked on similar data pipelines where I implemented efficient scraping and data handling processes, ensuring stable integrations and future-proof designs. A few questions to better understand the scope: Q1 – What specific data points do you need to scrape from the real estate sources? Q2 – Are there particular user roles or permissions that need to be managed for accessing this data? Q3 – How do you envision the data being used after it's collected? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@techplusintl

Hi, ★★★ TypeScript Expert ★★★ 6+ Years of Experience ★★★ I can build the real estate data pipeline with the MercadoLibre Venezuela adapter, ensuring all specified features and architecture are implemented. This will include: - Developing the Apify Actor with router architecture. - Implementing the normalized schema for properties, agencies, agents, and price history. - Setting up change detection and incremental weekly runs. - Handling DataDome with residential proxy and search sharding for listings. - Conducting QA sign-off across all regions. - Providing a full deployment package and handoff documentation. I will follow a structured approach using AWS Lambda and PostgreSQL, ensuring all components are integrated smoothly and efficiently. Ready to start once you provide access to the Apify account and any additional details needed for the implementation. Thanks!

@DeveloperStation

Hi there, I’ve developed multiple web scrapers using Apify, Puppeteer, and Selenium, and I’m well-versed with AWS services like Lambda, EC2, RDS, and CloudFront. I can create a robust, production-ready solution that handles edge cases and scales seamlessly. With a decade of experience, I’ve worked with startups and large enterprises, leading teams and managing end-to-end product development. I’m equally comfortable as a team player or a proactive leader. Let’s schedule a quick 10-minute call to discuss your project in detail and see if I’m the right fit. I usually respond within 10 minutes. I’m eager to learn more about your exciting project. Best, Adil

@C0RETECHIES

Hi, I understand you need a pipeline where an Apify Actor scrapes MercadoLibre VE, bypassing DataDome with residential proxies. It will use sharded searches for 3 regions. A Lambda then processes the data, normalizes it, and upserts into PostgreSQL RDS, tracking price history and changes from weekly incremental runs. Technical approach: Apify Actor (Crawlee/TS) with a request router and integrated proxies. A Node.js Lambda processes payloads, runs validation, and executes batch upserts into a normalized PostgreSQL schema. We'll use a checksum of key fields for change detection. Core modules: The scraper actor manages sharded queries and proxy rotation. The ingestion Lambda handles data validation, normalization, and DB writes. A monitoring module logs run metrics and errors for each job. Implementation strategy: We'll begin by stabilizing the scraper against DataDome for one region. Next, we build the Lambda and RDS schema. After validating the end-to-end flow, we scale to all regions and package the system with IaC templates for handoff. Questions: 1. What is the expected listing overlap between the "Greater Caracas" search and more granular filters within it? 2. Should price_history also track currency changes if a listing switches from VEF to USD? 3. Beyond DataDome, are there other API rate limits to factor into our crawler's politeness strategy? Regards, Rohit

@slavkom1

Hi, This Phase 1 is clear: Apify Actor, AWS Lambda handoff, and PostgreSQL storage for MercadoLibre Venezuela. The DataDome/proxy part is probably the fussy bit here, especially with weekly incremental runs. I’ve built JavaScript and PostgreSQL pipelines where scraping, change detection, and clean run logs mattered more than fancy code. I’d keep the work practical: - set up the Crawlee router and MercadoLibre adapter - map a normalized Database Design for properties, agencies, agents, prices, and logs - package the Amazon Web Services deployment so your in-house developer can apply it safely - add import tests, QA checks, and notes for Elasticsearch-ready search fields if needed later I can start right away and stay inside the weekly tracker limit with daily Slack updates. For the handoff, would your AWS developer prefer Terraform, CloudFormation, or a lighter deployment script package for the Lambda and RDS setup? Regards, Slavko

@ixorawebmob

Hello, I’ve gone through your project details and this is something I can definitely help you with. I have 10+ years of experience in mobile and web app development, working with Flutter, Android, iOS, React, Node.js, and APIs. I focus on clean architecture, scalable code, and clear communication to ensure the project runs smoothly from start to finish. I will first review your requirements, suggest the best technical approach, and then proceed with development while keeping you updated at every stage. Here is my portfolio: https://www.freelancer.in/u/ixorawebmob I’m interested in your project and would love to understand more details to ensure the best approach. Could you clarify: 1. Do you need this for mobile, web, or both? 2. Do you already have UI/UX designs or should we create them? 3. Will there be any third-party API or payment gateway integration? 4. What is your expected timeline for completion? 5. Are there any reference apps or websites you like? What specific features would you like to prioritize in the real estate data pipeline? Let’s discuss over chat! Regards, Arpit Jaiswal

@Eliteinno

Hello, Is the AWS setup for Lambda and RDS already configured? Do you have any specific testing tools in mind for the automated import tests? Excited to work on this pipeline! I'll ensure seamless integration and address change detection as a key challenge by implementing robust monitoring strategies. I'll share past examples in chat. Once we review the details, we can discuss the timeline and costs on a quick call. Looking forward to hearing from you and kicking off this project. Just let me know when you're ready to chat!

@BarryCrouch

Hi, thanks for the detailed Phase 1 brief. I understand you need a robust real estate data pipeline for MercadoLibre Venezuela, built as an Apify Actor with Crawlee/TypeScript and a router-based architecture, then connected through AWS Lambda to PostgreSQL/RDS with normalized tables, change tracking, weekly incremental runs, and strong QA across Caracas, Táchira, and Anzoátegui. I’ve delivered similar data extraction and pipeline systems where reliability, anti-bot handling, and clean handoff documentation were critical, including proxy-based scraping, structured storage, and deployment packages for in-house teams. I’m comfortable working within your hourly cap, tracking time carefully, and providing clear daily progress updates in Slack. I can also prepare the IaC and documentation so your AWS developer can deploy without friction. Do you already have preferred search shard rules for the ~64k Caracas listings, or would you like me to propose the initial partitioning strategy? Would you like the import test suite to focus first on schema validation, deduplication, or change detection? Happy to discuss the project further and get started immediately.

@craigd43

I couldn't be more excited about the opportunity to tackle Phase 1 of your real estate data pipeline project, and I believe my skill set aligns perfectly with your needs. With vast expertise in JavaScript and Node.js, I craft robust scripts and applications that not only scrape valuable data but integrate seamlessly into diverse systems like AWS, a crucial requirement for your project. Moreover, my proficiency in web scraping has empowered me to build significant experience with platforms such as Apify - an essential component in your pipeline build. With a meticulous attention to detail, I’ve created successful crawlers for numerous data sources, making me ideally suited to handle the MercadoLibre Venezuela adapter you've outlined. My approach is rooted in delivering results; I commit to quality code delivered promptly and strive to exceed client expectations. This dedication is why my clients see me as their go-to developer for projects demanding reliability, complexity management, and completion on time, precisely like yours. Let's get started on your real estate journey with intricate databases, smoothly running scheduled tasks and accurate information at every update.

@FizzaNadeemK

Hi, I can take this Phase 1 build from architecture to production-ready pipeline with clean separation between ingestion, transformation, and AWS deployment so your in-house team can safely run final infra rollout. I’ve worked on large-scale e-commerce and real estate data pipelines with queue-based scraping, incremental sync logic, and PostgreSQL normalization patterns similar to Apify + Crawlee + AWS RDS architectures. I’ll structure the MercadoLibre Venezuela adapter with resilient crawling, change detection, and clean schema for properties, agents, and price history. I’ll deliver a production Apify Actor (TypeScript) with Lambda-ready routing, RDS schema, proxy handling, and full QA coverage across Caracas, Táchira, and Anzoátegui. Do you want the initial dataset optimized for fastest coverage or highest detail per listing first? Best Regards, Fizza Nadeem K

@quan9824

Hi BrainX Technologies, This hourly engagement is for Phase 1 of the approved proposal, “Real Estate Data Pipeline (Phase 1)” dated 2 June 2026. The scope includes building the base real estate data pipeline plus the MercadoLibre Venezuela adapter as an Apify Actor using Crawlee/TypeScript, with router architecture, AWS Lambda handoff, and PostgreSQL/RDS storage. The system should include the normalized schema for properties, agencies, agents, price history, run logs, change detection, incremental weekly runs, DataDome handling with residential proxy, search sharding for Greater Caracas, Táchira, and Anzoátegui, QA sign-off across all three regions, automated import tests, and a complete deployment package with handoff documentation for our in-house AWS developer. Agreed terms: Rate: USD $30/hour Billing: weekly via Freelancer hourly tracker Weekly limit: approximately 17 hours/week maximum Time tracking: Clockify plus daily updates in our shared Slack channel No upfront payment Work reviewed and approved weekly Any hours beyond the agreed weekly maximum require prior written approval Apify, proxy, and AWS costs will run on our accounts No production AWS credentials will be shared Final deployment will be completed by our in-house AWS developer using your IaC package Please confirm acceptance of the scope and terms so we can begin immediately upon award.

@emilesd617

Hi I am a full stack data engineer with 8 years of rich experience and a strong background in large scale web scraping and cloud data pipelines. I am familiar with Apify, Crawlee, TypeScript, AWS Lambda, PostgreSQL, and Node.js. For this project, I think the most important part is building a reliable pipeline that collects data accurately and scales well for future sources. I can develop the Apify Actor, implement incremental scraping and change detection, normalize the data model, integrate with AWS and PostgreSQL, and provide clean documentation for a smooth handover. I'm an individual freelancer and can work on any time zone you want. Please feel free to contact me at the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile

@XRProConnect

Hi there, This project instantly caught my eye, so I had to reach out. I see you looking for a real estate data pipeline in Venezuela, integrating Apify Scraper with AWS PostgreSQL for MercadoLibre data. I have a strong track record of helping businesses optimize their data pipelines for better insights. I can boost your project's performance through robust architecture and efficient data handling. Feel free to request samples of my successful projects. Based on what you mentioned, here is how we would approach the project: - Create a robust Apify Actor for MercadoLibre data extraction - Implement a scalable AWS Lambda function for data processing - Set up a secure PostgreSQL database for storage and retrieval Rest assured, I prioritize clear communication and a user-focused approach to ensure seamless project delivery. Let's create a high-performing real estate data pipeline together. Best Regards, XRProConnect

@steveng96

Hello, I am available now. I have read your project description carefully and I understand what you want. 300% Confidence!!! I have 7+ years of experience in JavaScript, PostgreSQL, Amazon Web Services, Elasticsearch, Node.js. I have completed similar projects. Please contact me. Best regards, Steven

@mubeenm5026

As a top-notch software development company, we at Web Crest boast a team that's versatile and equipped to handle projects of any size and complexity; exactly what your Real Estate Data Pipeline demands. Leveraging my proficiency in Node.js, we have established an extensive track record of scraping and processing large-scale data, making our DNA a perfect fit for Phase 1. Over the years, I've not only honed my crawler skills but have worked meticulously on router architecture-based solutions (such as AWS Lambda) and PostgreSQL (RDS), which tick all the boxes of this project. Apart from technical competence, our business acumen sets us apart. We understand your requirements to build a normalized schema with change detection, price history, agencies, agents along with DataDome handling. My role would not be limited to just building the pipeline but also running comprehensive tests, ensuring quality assurance across three regions - Greater Caracas listings plus Tachira and Anzoategui. Lastly, my partnership with your in-house AWS developer assures painless handoff using your IaC packages - minimizing overheads while assuring delivery.

@loxtic

Hello, how are you doing? I have considerable experience building data pipelines and adapters, including TypeScript-based actors and AWS deployments, with a track record of incremental loads and robust QA. I’ve worked on multi-region data ingestion, normalization, and change detection, plus automated tests and deployment packages. I can jump in and align with your weekly rhythm and reporting in Slack. Let me know further if interested.

@TanvirProttoy

Hello, Coordinating a multi-source real estate data pipeline across multiple regions with incremental updates and proxy handling is a complex challenge where mismanaged scraping, sharding, or schema design can create delays or data inconsistencies. Ensuring accurate weekly runs while navigating DataDome protections and handling 64k+ listings requires careful orchestration. Building the pipeline as an Apify Actor with a router architecture feeding into AWS Lambda and PostgreSQL allows full automation with scalable monitoring, change detection, and QA verification. A normalized schema for properties, agencies, agents, and price history plus automated import tests ensures data integrity, while prior experience with residential proxies and search sharding guarantees coverage without overloading endpoints. I can provide a detailed review of your existing setup and outline any optimizations, develop a working Phase 1 Apify Actor proof of concept, or perform a controlled test run on one region to validate the full pipeline before full deployment. Best regards

@bohdan38

❤️❤️❤️❤️❤️Hi there, Your Phase 1 pipeline requires robust scraping and reliable incremental ingestion across Venezuela; primary failure points are DataDome anti-bot fingerprinting and search-shard coverage leading to missed or duplicate listings. I'll deliver an Apify Actor (Crawlee/TypeScript) with router patterns, residential proxy rotation and headless-fingerprint strategies to mitigate DataDome, checksum-based change detection, weekly incremental Lambda runs, and a normalized RDS schema. Automated import tests and region QA will be included, plus an IaC package for your in-house deploy. I implemented a MercadoLibre adapter with Crawlee and Terraform for RDS/Lambda, cutting duplicate rates by ~90% and enabling seamless handoffs. I also suggest synthetic run monitoring and proxy-health alerts to catch regressions early. Best regards, - Bohdan

@fahad508

Hello, This Phase 1 scope is essentially a production-grade scraping + ETL system, and the main risks are not in “building the actor,” but in long-term stability: bot protection (DataDome), geo coverage consistency, incremental diff logic, and clean schema evolution into PostgreSQL without data drift. I would approach this as a modular Apify Crawlee TypeScript Actor with a router-based architecture per region/source, strict normalization layer before persistence, and event-safe AWS Lambda ingestion into RDS using idempotent upserts. The focus would be on reliable incremental runs, not just initial extraction. Experience building Apify/Crawlee-based scraping systems, AWS Lambda ETL pipelines, and PostgreSQL data models with change detection and large-scale proxy-managed crawling. 1. For “change detection,” should we treat price/history updates as full snapshots per run or delta-based field updates with versioned rows in PostgreSQL? 2. Is the AWS Lambda layer strictly ingestion-only, or should it also handle transformation/business logic, or will that remain inside the Apify Actor? 3. What is the expected retry strategy for DataDome failures—aggressive proxy rotation per request, or controlled backoff with partial regional completeness? Best regards, Fahad