In the modern data landscape, mastering DataOps: Automation in Data Engineering is a crucial discipline for transforming the way data is managed, processed, and delivered, enabling organizations to achieve unparalleled speed, quality, and reliability in their analytics and machine learning initiatives. This methodology, which applies the principles of Agile, DevOps, and Lean manufacturing to the entire data lifecycle, is essential for breaking down traditional silos between data teams and automating repetitive tasks, thereby reducing errors and accelerating time-to-insight. This comprehensive training course is meticulously designed to equip data engineers, data architects, and DevOps professionals with the advanced knowledge and practical strategies required to implement a robust DataOps framework, from continuous integration and delivery for data pipelines to automated testing, monitoring, and governance. Without a solid understanding of DataOps: Automation in Data Engineering, organizations risk manual bottlenecks, inconsistent data quality, and slow-moving analytics projects that fail to keep pace with business demands, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Engineers and Architects
• DevOps and Site Reliability Engineers
• Data Scientists and Analysts
• Technical Leaders and Managers
• Software Engineers with a focus on data systems
• Cloud Architects
• Anyone responsible for managing or improving data workflows.

Objectives

• Understand the core principles and cultural shift of DataOps.
• Learn how to apply Agile and DevOps practices to data engineering.
• Acquire skills in using version control for data code and configuration.
• Comprehend techniques for building and automating CI/CD pipelines for data.
• Explore strategies for implementing automated testing and data validation.
• Understand the importance of infrastructure as code (IaC) for data platforms.
• Gain insights into orchestrating and automating complex data workflows.
• Develop a practical understanding of data observability and monitoring.
• Master the use of key tools and technologies in a DataOps ecosystem.
• Acquire skills in applying best practices for data governance and security.
• Learn to manage and collaborate on data projects effectively.
• Comprehend techniques for building reproducible and reliable environments.
• Explore strategies for ensuring data quality and lineage throughout the pipeline.
• Understand the importance of a unified metadata and data catalog.
• Develop the ability to lead and implement a successful DataOps transformation.

Course Content

Module 1: Introduction to DataOps Fundamentals

• What is DataOps and its core principles?
• The origins of DataOps: Agile, DevOps, and Lean manufacturing.
• The DataOps Manifesto and its values.
• The business case for DataOps: speed, quality, and collaboration.
• Comparing DataOps to traditional data management approaches.

Module 2: Version Control for Data and Code

• The importance of versioning data engineering code.
• Using Git for managing pipeline scripts and infrastructure code.
• Branching strategies for team collaboration and feature development.
• CI/CD triggers and pull requests for data pipelines.
• Versioning data models and schema changes.

Module 3: Automated Testing for Data Pipelines

• The necessity of testing in data engineering.
• Types of tests: unit, integration, and data-specific tests.
• Implementing data validation and quality checks.
• Using tools like Great Expectations for data assertions.
• Building a test-driven development (TDD) culture for data.

Module 4: Continuous Integration (CI) for Data

• What is Continuous Integration in a data context?
• Building a CI pipeline with tools like Jenkins, GitLab CI, or GitHub Actions.
• Automating code builds, testing, and dependency management.
• Running tests on every code commit.
• The role of containerization in CI for data pipelines.

Module 5: Continuous Delivery (CD) for Data

• What is Continuous Delivery for data?
• Automating the deployment of pipelines to different environments.
• Staging and production environment management.
• Release management strategies for data workflows.
• The importance of rollbacks and recovery plans.

Module 6: Infrastructure as Code (IaC) for Data Platforms

• What is IaC and its benefits for data engineering?
• Using tools like Terraform or Pulumi to manage cloud resources.
• Automating the provisioning of databases, data lakes, and compute clusters.
• Defining environments (dev, test, prod) with code.
• Managing infrastructure changes safely and repeatably.

Module 7: Workflow Orchestration and Automation

• The role of an orchestrator in a DataOps framework.
• Introduction to Apache Airflow and Dagster.
• Designing and scheduling complex data pipelines as DAGs.
• Parameterizing and reusing pipeline components.
• Monitoring and managing pipeline runs in a production environment.

Module 8: Data Observability and Monitoring

• The difference between monitoring and observability in data.
• Key data metrics to monitor: freshness, volume, and quality.
• Implementing logging and tracing for pipeline visibility.
• Setting up dashboards and alerts with tools like Prometheus and Grafana.
• Root cause analysis for pipeline failures.

Module 9: Metadata Management and Data Catalogs

• The importance of a unified data catalog for DataOps.
• Automating the collection of metadata and data lineage.
• Tools for metadata management (e.g., Apache Atlas, Amundsen).
• Enabling self-service data discovery for analysts and scientists.
• Integrating the catalog with other DataOps tools.

Module 10: DataOps Tools and Technology Stack

• The modern DataOps ecosystem.
• A deep dive into popular tools for each stage of the pipeline.
• Building an end-to-end DataOps stack with open-source and cloud-native tools.
• The role of platforms like Databricks and Snowflake in DataOps.
• Evaluating and selecting the right tools for a DataOps initiative.

Module 11: Data Governance in a DataOps Context

• Shifting from rigid governance to agile governance.
• Automating compliance and security checks.
• Managing access control and permissions with code.
• Implementing data masking and anonymization.
• Ensuring data privacy and regulatory compliance.

Module 12: Collaboration and Team Dynamics

• The DataOps culture: a shared mindset of collaboration and continuous improvement.
• Breaking down silos between data engineers, data scientists, and business teams.
• Agile methodologies for data projects: sprints, backlogs, and stand-ups.
• Effective communication strategies for data teams.
• Fostering a blameless post-mortem culture.

Module 13: Building Reproducible Environments

• The challenge of inconsistent environments.
• Using containers (Docker) to package dependencies and code.
• Managing environment configurations with ConfigMaps and secrets.
• Best practices for dependency management (e.g., requirements.txt).
• The importance of a single source of truth for all environment settings.

Module 14: DataOps Case Studies and Best Practices

• Analyzing successful DataOps implementations from various industries.
• Common anti-patterns and pitfalls to avoid.
• Strategies for scaling DataOps across the organization.
• The role of a dedicated DataOps team or engineer.
• The future of DataOps and its evolution.

Module 15: Practical Workshop: Implementing a DataOps Pipeline

• Participants work in teams to build a complete DataOps pipeline from scratch.
• Exercise: containerize a data transformation job and version control the code.
• Set up a CI pipeline to test the job automatically.
• Configure an orchestrator to run the pipeline on a schedule.
• Implement data quality checks and a monitoring dashboard.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.