You will get Build a Data Quality Monitoring System for Production Pipelines
Project details
You will get a production-ready data quality monitoring system designed to improve reliability and trust across your data pipelines.
This project focuses on detecting data issues early by applying automated quality checks, monitoring metrics, and alerts, reducing downstream errors and operational risk.
The solution is designed to work with modern data stacks and multiple databases, and can be adapted to both batch and streaming pipelines.
I focus on building clear, maintainable systems that provide visibility into data health and help teams make better, more reliable decisions based on their data.
This project focuses on detecting data issues early by applying automated quality checks, monitoring metrics, and alerts, reducing downstream errors and operational risk.
The solution is designed to work with modern data stacks and multiple databases, and can be adapted to both batch and streaming pipelines.
I focus on building clear, maintainable systems that provide visibility into data health and help teams make better, more reliable decisions based on their data.
Database Type
MySQL, MS SQL, Oracle, PostgreSQL, MongoDB, Azure Cosmos DBWhat's included
| Service Tiers |
Starter
$250
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 9 days |
Number of Revisions | 1 | 2 | 3 |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $200
Additional Revision
+$100
Additional data source integration
(+ 2 Days)
+$200
Custom alerting and reporting
(+ 2 Days)
+$300Frequently asked questions
About Emanuel
Data Engineering
Laguna Blanca, Argentina - 6:19 am local time
Steps for completing your project
After purchasing the project, send requirements so Emanuel can start the project.
Delivery time starts when Emanuel receives requirements from you.
Emanuel works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and understand the data pipelines
I review the provided requirements, data sources, and current pipeline context.
Design data quality rules and monitoring approach
I define the data quality checks, metrics, and monitoring strategy based on your needs.