Azure Data Pipeline Development

@koolvishruth

Hello Sir, I am certified in Azure. I have 7 years of experience in Data Pipeline & Automation.I can work on this project.I would like to connect through chat to know more

@ashinnd84

Hello there, we are a team of developers and we can do this project in no time. Thanks Ashish Kumar.

@rajc6h6

You need robust data pipelines extracted directly from your databases—I build these exact ETL workflows. I use SQL and Python daily to design efficient extraction pipelines that guarantee data integrity. I also founded an analytics platform (Synlitics) that reliably syncs complex data from multiple sources, so I understand strict database management. I can map your database schemas and deploy the first automated extraction pipeline within 72 hours. What specific SQL databases are your records currently sitting in?

@RKhakhula

Hi There, I am a Senior Data Analyst with extensive experience in database engineering, ETL pipeline development, and data infrastructure optimization. Throughout my career, I have successfully designed and maintained robust data pipelines to extract, transform, and load information seamlessly across complex enterprise systems. In my recent roles, I have implemented a comprehensive transaction archive infrastructure, designing tables, procedures, and functions using advanced SQL. I have built functional ETL tools using Python to extract data from various payment operators and financial streams, unifying disjointed structures for secure ERP injection. My background in Economic Cybernetics and Data Science equips me to safeguard data quality and integrity at every layer of the pipeline. I am fully prepared to collaborate closely with you on detailed project proposals to build efficient, scalable data pipelines tailored to your operational needs. Let's contact to discuss details. Solution Vector Roman Khakhula

@jagratip3

Hello, I can help you design and implement robust, scalable data pipelines focused on reliable extraction, transformation, and loading from database systems. I have strong experience in SQL, data modeling, and building ETL/ELT workflows that ensure data quality, consistency, and performance. I can work with relational databases, optimize queries, and structure pipelines that are maintainable and easy to extend as data grows. I also focus on data integrity checks, error handling, logging, and monitoring so the pipelines remain stable in production environments. I can collaborate on defining the architecture and refine the solution based on your project requirements. Deliverables will include well-structured pipelines, clean documentation, and a maintainable setup aligned with best practices. Available to start immediately. Best regards

@rakhshanz3

As an experienced and qualified data engineer, I am an ideal fit for the Azure Data Pipeline Development project. My extensive knowledge in Azure data technologies such as Azure Data Factory and Databricks, coupled with a profound understanding of SQL and database management, positions me to efficiently design, implement, and extract data from various databases for your pipelines. With my track record of ensuring data quality and integrity throughout my career, you can trust that your project is in capable hands. Having spent nearly two decades within the tech industry - which includes serving as the Chief Technology Officer of an AI startup - I've garnered a deep appreciation for the importance of robust data pipelines. Some examples of my past work include AI-powered voice and chat agents for customer support, end-to-end automation systems driven by machine learning algorithms, and full-stack SaaS applications with scalable dashboards. This experience gives me an edge in understanding the broader strategic objectives your pipelines must serve, not just their technical implementation. Lastly, my problem-solving skills have consistently proven themselves while building custom AI tools for document processing, data extraction and recommendation systems – skills pivotal to handle any challenges that may arise during the implementation of your project.

@Hrolgar

Incremental loads that look clean in dev tend to produce duplicates on the first pipeline retry in prod. Watermark logic needs a dedicated control table tracking last-processed offsets, not just MAX(modified_date) queried from the source at runtime. For this I'd use a metadata-driven ADF pattern: one parameterized pipeline template reading source config from a control table, incremental extraction with idempotent upsert into staging, transformation via Data Flow or Synapse Spark depending on volume, and a star-schema landing layer in dedicated SQL pool or Azure SQL. Every run writes execution metadata back to a logging table so reruns are safe and failures have a clean audit trail. Alerting on pipeline failures wired to Azure Monitor before handover. What you get: deployed ADF pipelines with incremental and full-load toggle, staging and warehouse schema, monitoring alerts, and a short doc covering the control table design and how to extend it for new sources. Single milestone, 31,875 INR, delivered in 3 days. One thing that'd sharpen the scope before I start: what are the source systems, on-prem SQL, other Azure services, flat files, or a mix?