Data Observability & Quality Engineering using Monte Carlo & Great Expectations
Ensure reliable, trustworthy data pipelines with modern tools for data monitoring, testing, and validation using Monte Carlo and Great Expectations.
Course Duration: 10 Hours
Preview Data Observability & Quality Engineering using Monte Carlo & Great Expectations course
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Data Observability & Quality Engineering using Monte Carlo & Great Expectations is a self-paced, industry-aligned course built for data professionals who want to ensure the integrity, reliability, and availability of data across their organization. This course focuses on two of the most powerful platforms in the data reliability landscape: Monte Carlo, a leading data observability tool, and Great Expectations, the most widely used open-source library for data validation and testing.
In today’s data-centric world, broken pipelines, missing values, unexpected schema changes, or freshness issues can severely impact analytics, machine learning, and decision-making processes. Traditional data quality checks are no longer sufficient to detect these complex and distributed failures. Data Observability introduces a proactive approach—similar to software observability—enabling teams to monitor the health of their data, detect incidents in real-time, and take corrective action swiftly.
This course introduces the core principles of Data Observability and Data Quality Engineering, such as metadata monitoring, anomaly detection, data lineage, testing, and SLAs. You will learn how to implement Monte Carlo’s automated observability suite for alerting and root cause analysis, and how to write validation tests using Great Expectations that are embedded directly into your ETL pipelines.
How to Use This Course
To make the most out of this course:
- Follow the structure – The course is divided into modules covering foundational concepts, tool-specific deep-dives, hands-on labs, and project implementation.
- Hands-on practice – Don’t just watch tutorials—interact with datasets, build expectations, set up monitoring tools, and simulate data incidents.
- Integrate into your workflow – Learn how to embed Great Expectations into your Airflow, dbt, or Spark pipelines and how to connect Monte Carlo with Snowflake or BigQuery.
- Capstone project – Apply all your learning by building a complete observability and testing solution for a real-world data pipeline scenario.
Whether you’re a data engineer responsible for production pipelines or an analyst trying to ensure report accuracy, this course will equip you with practical skills to safeguard your data workflows.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the principles of Data Observability and Data Quality Engineering.
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Implement end-to-end data validation with Great Expectations.
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Configure Monte Carlo for automatic anomaly detection and incident alerting.
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Monitor data freshness, volume, and schema with observability dashboards.
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Build, test, and document expectations for structured and semi-structured data.
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Integrate data quality checks into existing ETL pipelines (Airflow, dbt, etc.).
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Leverage data lineage for root cause analysis of data incidents.
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Simulate and resolve real-world data pipeline failures.
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Automate SLAs and error notifications for high-trust data delivery.
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Create a data reliability layer for analytics and machine learning workflows.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Data Observability and Data Quality
- What is Data Observability?
- Why Data Quality Matters
- Industry Use Cases
Module 2: Great Expectations Fundamentals
- Installation & Configuration
- Creating Data Contexts
- Writing Expectations
- Validating Batches
Module 3: Advanced Great Expectations
- Custom Expectations
- Expectation Suites and Profiling
- Integrating with Airflow and dbt
- Managing Validation Reports
Module 4: Monte Carlo Essentials
- Platform Overview & Setup
- Metadata Monitoring (Freshness, Volume, Schema)
- Configuring Rules & Alerts
- Incident Management
Module 5: Root Cause Analysis & Lineage with Monte Carlo
- Column-level Lineage
- Alert Routing & Slack Integration
- Case Studies on Incident Resolution
Module 6: Hands-On Pipeline Integration
- Simulating Data Failures
- Data Drift & Anomaly Detection
- Writing CI/CD Validation Tests
Module 7: Data SLAs & Governance
- Defining and Enforcing SLAs
- Data Contracts and Ownership
- Reporting and Compliance Tracking
Module 8: Capstone Project
- Design and build a production-quality data pipeline with built-in observability and quality checks using both tools.
Module 9: Final Assessment & Review
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Mock Interviews
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Resume Tips for Data Engineering and Reliability Roles
Certification Back to Top
Upon successful completion of the course, participants will receive a Certificate of Completion from Uplatz validating their expertise in Data Observability and Quality Engineering. This certification demonstrates your ability to work with modern data reliability tools like Monte Carlo and Great Expectations and highlights your commitment to delivering accurate, trustworthy, and reliable data.
The certificate can be added to your LinkedIn profile or resume and used to showcase your readiness for roles in data engineering, data quality assurance, and analytics operations. Employers increasingly seek professionals who can proactively manage and monitor data integrity—this certification sets you apart as a candidate who understands both technical tools and data lifecycle reliability best practices.
Career & Jobs Back to Top
As data ecosystems become more complex and business decisions depend on real-time analytics, the demand for data reliability engineers, observability specialists, and data quality engineers has grown significantly. Professionals skilled in tools like Monte Carlo and Great Expectations are highly sought after by enterprises across industries such as fintech, e-commerce, healthcare, logistics, and SaaS.
After completing this course, learners can pursue roles such as:
- Data Quality Engineer
- Data Observability Specialist
- Data Reliability Engineer
- ETL/Data Pipeline Engineer
- Analytics Engineer
- Data Governance Analyst
These roles are often well-compensated, with average annual salaries ranging from $85,000 to $135,000 globally, depending on experience and location. The ability to ensure data trust and reliability is now considered a core part of the modern data stack, and mastering it gives you a major advantage in both job applications and promotions.
The course also provides exposure to production-ready tools and practices, helping professionals transition into more strategic roles like Data Engineering Leads or Data Platform Owners who manage data infrastructure, compliance, and platform reliability at scale.
Interview Questions Back to Top
- What is data observability?
Data observability is the ability to monitor, understand, and troubleshoot data pipelines and datasets using automated tools that track freshness, volume, schema changes, lineage, and anomalies. - How does Monte Carlo help in data observability?
Monte Carlo automatically monitors metadata from data warehouses, detects anomalies in pipelines, and sends alerts when data freshness, volume, or schema changes deviate from expected behavior. - What is Great Expectations used for?
It is an open-source Python-based tool that helps create, test, and validate data quality rules ("expectations") within your data pipelines. - What are expectations in Great Expectations?
Expectations are assertions about data, such as “values in column X should not be null” or “column Y should match a regex format.” They ensure datasets meet defined standards. - What’s the difference between data quality and data observability?
Data quality is about enforcing correctness (validation), while observability is about detecting and understanding when and why something goes wrong (monitoring and root cause analysis). - Can Monte Carlo and Great Expectations be used together?
Yes. Great Expectations enforces validation, while Monte Carlo adds real-time monitoring and incident detection—together providing full reliability coverage. - What are common data quality metrics tracked in Monte Carlo?
Freshness, volume, schema, distribution (data drift), and lineage. - What is data lineage and why is it important?
Data lineage tracks the flow of data across sources, transformations, and destinations. It helps in root cause analysis when data incidents occur. - How do you test a pipeline using Great Expectations?
By embedding expectation suites into the pipeline that are validated during each run, ensuring input and output data conform to required rules. - What are SLAs and how are they enforced in data pipelines?
Service Level Agreements (SLAs) define the expected performance of data systems (like delivery time or completeness) and can be enforced using tools like Monte Carlo alerts or Great Expectations tests.
Course Quiz Back to Top
FAQs
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Q1. What are the payment options?
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A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience.
Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!
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While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.
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