Data engineering Aws Plus Azure Data Factory Integration Project

@kumarashish99

As an AWS-certified professional with extensive experience in deploying and managing cloud infrastructures, I bring a versatile skill set to the table that complements your requirement for robust data-integration workflows. My expertise in Azure, SQL, and continuous integration and deployment aligns perfectly with the needs of your project. I've worked on similar projects where I've set up pipelines to handle incremental loads and schema drift, which I believe are crucial aspects of your project. Lastly, let me add that customer satisfaction is paramount to me. So apart from delivering end-to-end Azure Data Factory pipelines including all supporting JSON / ARM templates or ARM-linked Git repo, you can also count on me for comprehensive documentation explaining pipeline logic and fail-recovery steps along with a detailed handover session to ensure your team operates the solution confidently. Let's combine my AWS and Azure expertise to take your data-integration workflow to the next level!

@vishal1145

Your ADF pipelines will fail in production if you don't handle schema drift from those SQL sources and blob files upfront. Without dynamic mapping and incremental watermarking, you'll either miss data or reprocess everything on every run, blowing your compute budget. Quick questions - what's your expected data volume per run and refresh cadence? And do you have existing monitoring infrastructure in Azure Monitor, or should I configure Log Analytics workspaces from scratch? Here's the architectural approach: - AZURE DATA FACTORY: Build parameter-driven pipelines with dynamic datasets that auto-detect schema changes and route failures to dead-letter storage before they break downstream analytics. - CI/CD + GIT: Deploy ARM templates through Azure DevOps pipelines with environment-specific parameter files so you can promote dev to prod without manual JSON edits. - INCREMENTAL LOADS + ALERTING: Implement watermark tables in SQL to track last-run timestamps, configure Azure Monitor alerts for pipeline failures, and set budget thresholds to prevent runaway costs during testing. I've built similar ADF solutions for 2 healthcare clients processing 500GB daily with zero data loss over 18 months of production runtime. Let's schedule a 20-minute architecture review call so I can map your exact source schemas before committing to a build timeline.

@iterativeTech

As someone with a passion for leveraging AI and cloud data engineering to drive ROI, your project matches my core areas of expertise. Alongside fluency in AWS and Azure, I've been deploying efficient ETL/ELT pipelines for years, mapping data flows, handling schema drifts, and implementing CI/CD deployment through Git integration- exactly what this project calls for. An important factor that sets me apart is the real-world results my solutions generate, something I know you value as well. You can trust my deep technical expertise to architect end-to-end Azure Data Factories pipelines. But where I truly excel is in creating parameterized datasets and implementing transformation processes that support easy reuse and enable smarter downstream analytics. Another key feature added by my work is baked-in operational efficiency with an eye on cost control from the start. Monitoring, alerting, and basic cost-control settings are automatically included in every ADF workflow I create to ensure smooth production-ready solutions that align with business objectives. Given our shared focus on outcomes rather than just implementation, I am confident we can build a solution that will transform your data into measurable business outcomes while keeping an eye out for ROI optimizations.

@Jennycreation

As a seasoned data engineer with more than 17 years of comprehensive experience in development, I have a wealth of skills to not just meet, but exceed your expectations for this complex task. While I may not have direct project involvement with Azure Data Factory (ADF), this doesn't diminish my ability to deliver top-notch results for you. Here's why, based on my profile and experience: My profound understanding of SQL databases aligns with your data engineering objectives. This proficiency ensures that I'm equipped to seamlessly integrate all your source systems, including the SQL databases and flat files in blob storage, into efficient pipelines that effectively ingest, map, and store your data for downstream analytics. Moreover, my mastery in crafting parameter-driven datasets and implementing transformative data flow mappings will be invaluable assets to the project. Though I lack Azure-specific project experience like incremental loads or CI/CD through Git integration on ADF pipelines, my keenness for learning rapidly equips me not just with theoretical knowledge but also with practical implementation capacity. My proficiency with Azure generally and my exposure to other cloud platforms would further facilitate my speedy adaptation to ADF specifics.

@CM66

Hi, I’m interested in taking on this Azure Data Factory project. I have experience building ADF based data integration solutions with SQL sources, Azure Blob Storage, parameterized pipelines, incremental loading and production monitoring. For your setup, I would first design the overall architecture and then build the solution in a reusable way rather than creating seperate pipelines for every source. I can handle the linked services, parameter-driven datasets, copy activities, mapping data flows where transformations are required, and the curated landing layer. My approach would include: Building reusable, parameterized ADF pipelines for SQL and Blob sources. Implementing incremental loads using watermark/last modified logic where applicable. Handling schema changes and schema drift where required. Using metadata-driven pipeline patterns to reduce duplicate work. Creating mapping data flows for transformations that cannot be handled efficiently through copy activities. Setting up triggers and schedules based on the source requirements. Adding proper error handling, retries and failure/recovery logic. Configuring monitoring and alerts so failed pipelines can be identified quickly. Applying basic cost-control practices to avoid unnecessary data-flow/runtime usage. Setting up Git integration and CI/CD deployment using ARM templates or the appropriate ADF deployment approach. Keeping naming, parameters and pipeline structure clean so your team can maintain it later.

@alexkvat

With 15+ years of experience in enterprise data solutions and SQL, I can deliver a well-architected data integration workflow in Azure Data Factory. My background includes building ETL pipelines, designing data transformation logic, and implementing production-ready data systems with monitoring and alerting. For this project, I will implement: 1. Pipeline Architecture: Design modular, parameter-driven pipelines with linked services for your SQL databases and blob storage sources. Each pipeline will follow reusable patterns with configurable datasets. 2. Data Flows: Build mapping data flows for transformations—handling schema mapping, data cleansing, and business logic to land data in your curated analytics layer. 3. Incremental Loading: Implement watermark-based incremental ingestion to process only changed records, reducing runtime and costs. 4. Monitoring & Alerting: Configure ADF diagnostic settings, pipeline alerts for failures, and cost management through trigger scheduling and integration runtime optimization.

@ronakkataria1

A few things about ADF that decide whether this build stays reliable, worth settling before you pick anyone. ADF Copy activity has no upsert. It inserts or it overwrites, nothing in between. The moment you need to merge changed rows into a target you are into a stored procedure or a mapping data flow, and a data flow spins up a Spark cluster that bills by the minute. So the design question is not how to copy, it is how you match and merge, and that is where the cost and the reruns live. Second, your SQL sources. If any of them sit on-prem or behind a private endpoint, the default AutoResolve integration runtime cannot reach them at all. That needs a self-hosted IR on a box inside the network. Fine, but it is a setup step to agree now, not discover in week two. On reusable, the pattern that lasts is metadata driven. One control table lists sources, targets and watermarks, and a single parameterised pipeline loops it. Add a source later by inserting a row, not cloning a pipeline. It also makes reruns idempotent, since each load knows its own watermark and can pick up where it failed. Which SQL sources are cloud versus on-prem, and is any of this incremental or full reload each run? Ronak

@vtriexc

I can build your Azure Data Factory solution end-to-end with a production-ready, reusable architecture focused on reliability, scalability, and maintainability. With 4+ years of software development experience and hands-on experience with Azure, SQL, ETL, APIs, and data-driven applications, I can design the ADF workflows around your actual source systems rather than creating a one-off pipeline. My approach will include: • Parameter-driven pipelines and datasets for maximum reusability • SQL database and Blob Storage ingestion • Incremental/watermark-based loads to avoid unnecessary processing • Mapping Data Flows for transformations, joins, cleansing, and schema handling • Schema drift support where required • Proper error handling, retries, logging, and failure recovery • Monitoring and alerting for production operations • Cost-conscious integration/runtime configuration • Git-based ADF development with CI/CD and ARM templates • Clear documentation covering architecture, triggers, dependencies, and recovery procedures • Final handover walkthrough for your team I’ll work in milestones: architecture/design sign-off → pipeline development → testing/optimization → production readiness → handover. I focus on clean, maintainable implementations so your team can easily extend the pipelines when new sources or requirements are added. I’m ready to start with your existing Azure environment and source details.

@asitkumarmishra2

With experience spanning over 8 years designing and delivering secure enterprise applications, I'm confident that I'm the ideal candidate for your robust data integration project. My technical stack-spanning Azure, .NET Core, Angular and more-aligns perfectly with your project requirements. Notably, my expertise in Full Stack Development and API-driven architecture will prove indispensable for your Azure Data factory project as it necessitates strong understanding of CI/CD automation, a skill I've honed over the years. To ensure your data-integration workflow is set up to withstand the test of time while remaining scalable and cost-effective, monitoring, alerting, and basic cost-control settings will be baked into the solution from inception; a practice I'm well-versed in. Over my career span, I've also consistently prioritized documentation and detailed knowledge transfer. You can expect an easily accessible resource file encompassing JSON/ARM templates or ARM-linked Git repositories for version control. Further, I'll ensure you're proficiently groomed on the system's operationvyubecause of their centerpiece role in the success of any digital solution. I have substantial experience working with Data Azure Factory (ADF), specifically with incremental loads which makes me equipped to handle any schema drift handling challenges your integration might encounter.

@warrick1718

This project immediately caught my attention because it is exactly the type of work I do best. Your focus on creating "reliable, reusable pipelines" in Azure Data Factory aligns perfectly with my experience in data integration. I have a strong background in designing end-to-end workflows, including setting up linked services and crafting parameter-driven datasets. I also understand the importance of monitoring, alerting, and cost-control settings to ensure a production-ready solution. While I am new to freelancer, I have tons of experience and have done other projects off site, working with SQL databases, incremental loads, and CI/CD deployment through Git integration. If this sounds like what you're looking for I'd love to hear more about your project. Regards, Warrick Van Eeden

@dwivedipiyush841

Dear Client, I am interested in working on your Azure Data Factory (ADF) data integration project. I have experience designing and developing data integration solutions using SQL databases, flat files, Blob Storage, ETL/ELT pipelines, and production support. My expertise includes: • End-to-end ADF pipeline development • Linked Services, Datasets and Integration Runtimes • Reusable, parameter-driven pipelines and datasets • SQL Server/SQL database and Azure Blob integrations • Mapping Data Flows and data transformations • Incremental/delta loads using watermark/change tracking • Schema drift handling and data validation • Error handling, retries and failure recovery • Pipeline scheduling, monitoring and alerting • Performance and cost optimization • Git integration and CI/CD using ARM templates • Deployment across Dev, UAT and Production My approach will cover architecture/design, ADF development, testing, production readiness, CI/CD and documentation. I will build reusable and maintainable pipelines rather than one-time solutions. I would be happy to understand your source systems, data volumes, refresh frequency and target architecture and then finalize the milestones for design, development, testing and acceptance. I am available to start and can work closely with your team throughout the project. Best Regards, Piyush Azure Data Factory | Data Integration | SQL | ETL/ELT +91-9693516919

@alluankit01

Hi, I can build your Azure Data Factory integration workflow with reusable pipelines, linked services, parameterized datasets, transformations, monitoring, alerting, and Git-based deployment support. My approach will be to first review your SQL sources, blob files, target curated layer, refresh frequency, and transformation rules. Then I’ll design ADF pipelines that are reliable, reusable, and ready for downstream analytics. I’m comfortable with: * Azure Data Factory * SQL and blob storage ingestion * Parameterized pipelines * Linked services and datasets * Mapping data flows * Incremental loads * Schema drift handling * Monitoring and alerts * Git integration * ARM/JSON templates * CI/CD support Deliverables: * End-to-end ADF pipelines * Reusable parameterization * Source-to-target mapping logic * Curated data landing flow * Trigger schedules * Failure handling and recovery notes * Monitoring and alert setup * ARM/JSON or Git repo outputs * Pipeline documentation * Handover walkthrough I’ll focus on a production-ready ADF solution that is easy to monitor, maintain, and extend as more SQL or flat-file sources are added. Best regards Ankit