DataOps Certified Professional

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15+Data Tools

DataOps Certified Professional Training

The DataOps Certified Professional certification is designed for data engineers, analytics engineers, and data operations professionals who want to master the principles and practices of DataOps — bridging the gap between data engineering, analytics, and operations teams. This certification covers data pipeline design and automation, orchestration with Apache Airflow and Prefect, data transformation with dbt, and data quality enforcement using Great Expectations and Soda. Participants build the skills to implement end-to-end DataOps workflows that deliver reliable, observable, and high-quality data at scale.

What is DataOps Certified Professional?

The DataOps Certified Professional (DCP) certification validates an individual's ability to apply DataOps principles across the full data lifecycle — from ingestion and transformation to delivery and monitoring. The program covers both the cultural and technical dimensions of DataOps: collaborative practices between data engineering, analytics, and operations teams; automated testing and CI/CD for data pipelines; lineage tracking with OpenLineage and Monte Carlo; and streaming pipelines with Apache Kafka and Apache Flink. Designed for data-focused professionals working in modern cloud-native and hybrid environments, this certification is recognized as a mark of excellence in data reliability engineering.

Course Feature

  • Comprehensive Curriculum: Covers DataOps fundamentals, pipeline automation, orchestration, transformation, data quality, lineage, and streaming — all with real-world applications.
  • Hands-On Labs: Practical labs with Apache Airflow, Prefect, dbt, Great Expectations, Soda, OpenLineage, Apache Kafka, and Apache Flink in cloud environments.
  • Expert-Led Training: Instructors with deep data engineering and DataOps experience deliver both theoretical grounding and practical operational insights.
  • Live Project Work: End-to-end DataOps pipeline projects — from ingestion and orchestration to quality enforcement, lineage tracking, and observability dashboards.
  • Case Studies: Real-world DataOps implementations from fintech, e-commerce, and healthcare demonstrating measurable improvements in data reliability and delivery speed.
  • Certification Exam Preparation: Mock exams and practice scenarios to prepare for the DataOps Certified Professional examination with confidence.
  • Flexible Learning Options: Online and in-person formats to accommodate different schedules and learning styles.
  • Community Access: A professional network of DataOps practitioners for ongoing support, knowledge sharing, and career growth.

Training Objectives

  • Master DataOps Principles: Understand DataOps culture, collaboration patterns, and the full data lifecycle from ingestion to consumption.
  • Pipeline Design & Automation: Build modular, testable, and version-controlled data pipelines with CI/CD integration for automated deployment.
  • Orchestration Mastery: Configure and manage complex workflow orchestration using Apache Airflow DAGs and Prefect flows with error handling and retries.
  • Data Transformation with dbt: Implement modular SQL transformations, tests, documentation, and incremental models using dbt Core and dbt Cloud.
  • Data Quality Enforcement: Write and run automated data quality checks using Great Expectations suites and Soda checks integrated into CI/CD pipelines.
  • Data Lineage & Observability: Instrument pipelines with OpenLineage metadata and use Monte Carlo for end-to-end data observability and anomaly detection.
  • Streaming Pipelines: Build real-time data pipelines with Apache Kafka for event streaming and Apache Flink for stateful stream processing.
  • Cloud DataOps: Deploy and manage DataOps workloads on AWS (Glue, MWAA), GCP (Composer, Dataflow), and Azure (Data Factory, Synapse).
  • Team Collaboration: Apply DataOps collaboration practices between data engineering, analytics, and operations teams using version control and shared tooling.
  • Exam Readiness: Complete structured mock exams and scenario-based exercises to pass the DataOps Certified Professional exam.
Target Audience

This program is designed for data engineers, analytics engineers, data platform engineers, and data operations leads who want to formalize their DataOps expertise. It also benefits data architects, BI engineers, and DevOps professionals transitioning into data platform roles. Anyone seeking to improve data reliability, accelerate pipeline delivery, and foster collaboration across data teams will find this certification essential. Prior experience with SQL, Python, and basic data pipeline concepts is recommended.

Training Methodology
  • Instructor-led live sessions covering DataOps theory, tooling, and real-world implementation patterns
  • Hands-on labs configuring Airflow DAGs, Prefect flows, and dbt models in cloud sandbox environments
  • Data quality lab: writing Great Expectations suites and Soda checks integrated with CI pipelines
  • Self-paced video tutorials and downloadable lab guides for all tools covered
  • Mock exams and practice tests simulating the certification examination format
  • Capstone project: build a complete DataOps pipeline with orchestration, transformation, quality checks, and lineage tracking
  • Peer collaboration forums and live Q&A with instructors
Training Materials
  • Detailed course slides and eBooks covering the full DataOps curriculum across all 8 modules
  • Airflow DAG templates, Prefect flow examples, and dbt project starter code
  • Great Expectations and Soda check configuration guides with annotated examples
  • OpenLineage integration walkthroughs and Monte Carlo observability setup guides
  • Kafka and Flink pipeline reference architectures and implementation guides
  • Mock exams and practice scenario bank aligned to the certification exam objectives
  • Case studies from retail, fintech, and healthcare DataOps implementations
  • Cloud platform deployment guides for AWS, GCP, and Azure DataOps stacks
  • Interactive online labs with pre-provisioned cloud environments for all exercises

Agenda of DataOps Certified Professional

  • What is DataOps? Principles, Benefits, and Industry Adoption
  • DataOps vs. DevOps vs. MLOps: Similarities and Key Distinctions
  • Breaking Down Silos: Collaboration Between Data Engineering, Analytics, and Operations
  • DataOps Maturity Model and Organizational Assessment Frameworks
  • Hands-On: Mapping a DataOps Workflow for a Real-World Business Use Case

  • Data Pipeline Architecture: Batch, Micro-Batch, and Streaming Patterns
  • Modular Pipeline Design: Idempotency, Testability, and Reusability
  • Version Control for Data Pipelines: Git Workflows and Branching Strategies
  • CI/CD for Data Pipelines: Automated Testing and Deployment with GitHub Actions
  • Hands-On: Building and Version-Controlling a Modular, Testable Data Pipeline

  • Apache Airflow Architecture: DAGs, Operators, Sensors, and Hooks
  • Advanced DAG Patterns: Dynamic Tasks, TaskFlow API, and XComs
  • Prefect Flows, Tasks, and Deployments: Modern Python-Native Orchestration
  • Error Handling, Retries, SLAs, and Alerting in Orchestration Systems
  • Hands-On: Building a Production-Grade DAG in Airflow and an Equivalent Flow in Prefect

  • dbt Core vs. dbt Cloud: Architecture, CLI, and Workflow Differences
  • Modular SQL with dbt Models, Sources, Seeds, and Snapshots
  • dbt Tests: Generic and Singular Tests for Data Validation
  • Incremental Models, Materializations, and Query Performance Optimization
  • Hands-On: Building a Full dbt Project with Documentation, Tests, and Incremental Models

  • Data Quality Dimensions: Completeness, Accuracy, Consistency, and Timeliness
  • Great Expectations: Expectation Suites, Checkpoints, and Data Docs
  • Soda Checks: Writing SodaCL Checks and Integrating with Pipelines
  • CI/CD Integration: Failing Pipelines on Data Quality Gate Violations
  • Hands-On: Implementing a Full Data Quality Layer with Great Expectations and Soda

  • Data Lineage: Why It Matters for Data Trust, Compliance, and Debugging
  • OpenLineage Standard: Emitting and Consuming Lineage Events from Pipelines
  • Marquez: Open-Source Lineage Metadata Server Setup and Configuration
  • Monte Carlo Data Observability: Anomaly Detection, Freshness, and Volume Monitoring
  • Hands-On: Instrumenting an Airflow Pipeline with OpenLineage and Viewing Lineage in Marquez

  • Streaming DataOps: When Batch Processing is Not Enough
  • Apache Kafka Architecture: Brokers, Topics, Partitions, Consumers, and Producers
  • Apache Flink: Stateful Stream Processing, Windows, and Watermarks
  • Kafka Connect and Schema Registry for Reliable Data Integration
  • Hands-On: Building a Real-Time Streaming Pipeline from Kafka to a Cloud Data Warehouse

  • AWS DataOps Stack: Glue, MWAA (Managed Airflow), Redshift, and Lake Formation
  • GCP DataOps Stack: Cloud Composer, Dataflow, BigQuery, and Dataplex
  • Azure DataOps Stack: Azure Data Factory, Synapse Analytics, and Microsoft Purview
  • Capstone Project Review and Architecture Discussion with Instructor Feedback
  • Certification Exam Tips, Mock Exam Practice, and Final Q&A Session

PROJECT

Participants work on 3 real-time capstone projects: (1) building an orchestrated batch pipeline with Airflow, dbt, and Great Expectations including CI/CD integration; (2) instrumenting a full pipeline with OpenLineage and setting up Monte Carlo observability alerts; (3) designing a streaming analytics pipeline with Kafka and Flink that feeds a real-time dashboard. All projects are scoped to production-realistic scenarios covering data engineering, analytics, and operations collaboration.

INTERVIEW

As part of this program, you will receive a complete DataOps interview preparation kit — built from 200+ years of combined industry experience and insights from thousands of DevOpsSupport learners worldwide. The kit covers data pipeline design questions, dbt and Airflow configuration scenarios, data quality interview questions, and system design exercises for roles including Data Engineer, Analytics Engineer, and Data Platform Lead.

Our Course in Comparison

FeaturesDevOpsSupportOthers
DataOps-Specific Curriculum (Pipeline to Lineage)
Hands-On Labs with Airflow, dbt & Great Expectations
Data Lineage & Observability Coverage
Streaming Pipeline Module (Kafka + Flink)
Lifetime LMS Access
15+ Data Tools Coverage
Interview Kit (Q&A)
Training Notes & Lab Guides
Cloud Platform Labs (AWS / GCP / Azure)
Capstone Projects (3 Real-World Scenarios)

Frequently Asked Questions

The DataOps Certified Professional certification validates expertise in data pipeline automation, orchestration, data quality engineering, data lineage, and the cultural practices that enable fast and reliable data delivery across engineering, analytics, and operations teams.

Ideal for data engineers, analytics engineers, data platform engineers, BI developers, and DevOps professionals who work with data pipelines and want to formalize their skills with a recognized credential. Prior SQL and Python experience is recommended.

The course covers Apache Airflow, Prefect, dbt Core and Cloud, Great Expectations, Soda, OpenLineage, Marquez, Monte Carlo, Apache Kafka, Apache Flink, and cloud-native DataOps services on AWS, GCP, and Azure — over 15 tools in total.

The program spans 60 hours of structured training, typically delivered at 4 hours per day over 15 days. Flexible scheduling options are available for self-paced learners who need more time to work through the material.

Participants should have working knowledge of SQL, basic Python programming, and familiarity with data warehouse or data lake concepts. Experience with any pipeline tool or cloud data service is beneficial but not required to enroll.

The certification exam includes multiple-choice questions, scenario-based questions, and practical exercises covering all 8 modules. Candidates are assessed on their ability to design, implement, and troubleshoot DataOps workflows using the tools covered in the program.

The certification is valid for 3 years. Recertification keeps your credential current with evolving DataOps practices, tooling updates, and cloud platform changes. A recertification exam is available at a reduced cost.

This certification supports roles including Data Engineer, Analytics Engineer, Data Platform Engineer, DataOps Lead, Data Reliability Engineer, and Senior Data Infrastructure Engineer. It is also valuable for data architects designing modern data stack architectures.

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