DataOps Course: Master Automation, Quality & Collaboration in Data
Become a skilled DataOps Engineer with BinnBash Academy's comprehensive course. Master automation, CI/CD for data, data quality, monitoring, and collaboration tools. Drive efficiency, reliability, and governance across the entire data lifecycle!
Optimize Your Data Operations!Who Should Enroll in this DataOps Course?
This course is ideal for professionals looking to streamline and optimize data delivery and governance:
- Data Engineers, ETL Developers, and Data Architects.
- DevOps Engineers interested in data pipeline automation and reliability.
- Data Scientists and Analysts seeking to improve data quality and deployment processes.
- QA Engineers specializing in data testing and validation.
- Anyone involved in the data lifecycle looking to implement agile and automated practices.
- IT Managers and Leads aiming to enhance data team efficiency and data product delivery.
DataOps Course Prerequisites
- Strong understanding of data pipelines and data warehousing concepts.
- Proficiency in SQL and at least one scripting language (e.g., Python).
- Familiarity with version control systems (Git).
- Basic knowledge of cloud platforms (AWS, Azure, GCP) is beneficial.
- Understanding of CI/CD principles is a plus.
- Analytical mindset and attention to detail for data quality.
Key DataOps Tools & Technologies Covered
Hands-on practice implementing automation, quality checks, and collaborative practices in data pipelines.
DataOps: Comprehensive Syllabus & Practical Contents
Module 1: Introduction to DataOps & Principles
- What is DataOps? Philosophy, Culture, and Practices.
- The DataOps Lifecycle: Plan, Build, Integrate, Deploy, Operate, Monitor.
- Benefits of DataOps: Speed, Quality, Collaboration, Governance.
- Key Pillars of DataOps: Automation, Monitoring, Testing, Collaboration.
- Case Studies of DataOps Implementations.
- Lab: Analyze existing data workflows to identify DataOps opportunities.
Tools & Concepts:
- DataOps Principles, Data Lifecycle.
Expected Outcomes:
- Understand DataOps fundamentals.
- Identify DataOps benefits.
- Analyze data workflows.
Module 2: Data Versioning & Environment Management
- Importance of Data Versioning in DataOps.
- Using DVC (Data Version Control) for Data and Model Versioning.
- Managing Data Environments (Development, Staging, Production).
- Infrastructure as Code (IaC) for Data Infrastructure (Terraform basics).
- Containerization for Data Applications (Docker basics).
- Lab: Implement DVC for a data project, set up a basic Docker environment.
Tools & Concepts:
- DVC, Docker, Terraform.
Expected Outcomes:
- Version data effectively.
- Manage data environments.
- Understand IaC for data.
Module 3: CI/CD for Data Pipelines
- Continuous Integration (CI) for Data Code.
- Continuous Delivery/Deployment (CD) for Data Pipelines.
- Automated Testing for Data (Unit, Integration, Data Validation).
- Building CI/CD Pipelines with Jenkins, GitLab CI, or GitHub Actions.
- Code Quality, Linting, and Static Analysis for Data Code.
- Lab: Build and automate CI/CD pipelines for data transformations.
Tools & Concepts:
- CI/CD, Jenkins/GitLab CI/GitHub Actions.
Expected Outcomes:
- Automate data code integration.
- Implement data pipeline deployments.
- Ensure data code quality.
Module 4: Data Quality & Testing Automation
- Importance of Data Quality in DataOps.
- Data Quality Dimensions (Accuracy, Completeness, Consistency, etc.).
- Automated Data Testing with Great Expectations / Deequ.
- Building Data Validation Rules and Expectations.
- Data Reconciliation & Anomaly Detection.
- Data Quality Dashboards & Reporting.
- Lab: Implement automated data quality checks and build data quality reports.
Tools & Concepts:
- Great Expectations/Deequ, Data Quality.
Expected Outcomes:
- Ensure high data quality.
- Automate data validation.
- Monitor data health.
Module 5: Data Pipeline Orchestration & Monitoring
- Introduction to Workflow Orchestration (Apache Airflow).
- Building DAGs (Directed Acyclic Graphs) in Airflow.
- Scheduling, Dependencies, and Retries in Airflow.
- Monitoring Data Pipelines (Prometheus, Grafana).
- Alerting & Incident Management for Data Pipelines.
- Data Lineage & Metadata Management.
- Lab: Orchestrate complex data pipelines with Airflow, set up monitoring.
Tools & Concepts:
- Apache Airflow, Prometheus, Grafana.
Expected Outcomes:
- Orchestrate data workflows.
- Monitor pipeline performance.
- Manage data lineage.
Module 6: DataOps on Cloud & Data Governance
- DataOps on AWS (Glue, Step Functions, DataBrew, Lake Formation).
- DataOps on Azure (Data Factory, Purview, Synapse Analytics).
- DataOps on GCP (Cloud Composer, Data Catalog, Data Fusion).
- Data Governance, Security, and Compliance in DataOps.
- Building a Professional DataOps Portfolio.
- Career Guidance: Resume Building, LinkedIn Optimization, Mock Interviews for DataOps roles.
- Final Project: Implement an end-to-end DataOps pipeline on a chosen cloud platform.
Tools & Concepts:
- Cloud DataOps Services, Data Governance.
- Portfolio Building, Career Prep.
Expected Outcomes:
- Implement cloud DataOps.
- Ensure data governance.
- Secure a DataOps Engineer job.
This course provides hands-on expertise to make you a proficient and job-ready DataOps Engineer!
DataOps Engineer Roles and Responsibilities in Real-Time Scenarios & Live Projects
Gain hands-on experience by working on live projects, understanding the real-time responsibilities of a DataOps Engineer in leading global companies. Our curriculum is designed to align with industry best practices for agile and reliable data delivery.
Data Pipeline Automation
Design, build, and maintain automated CI/CD pipelines for data ingestion, transformation, and delivery, ensuring faster and more reliable data releases, as done at LinkedIn.
Automated Data Quality & Testing
Implement automated data quality checks and testing frameworks (e.g., Great Expectations) to ensure data accuracy, completeness, and consistency throughout the data lifecycle, similar to work at Walmart.
Workflow Orchestration & Scheduling
Utilize tools like Apache Airflow to orchestrate complex data workflows, manage dependencies, and ensure timely execution of data pipelines, common at Airbnb.
Data Pipeline Monitoring & Alerting
Set up comprehensive monitoring and alerting systems (Prometheus, Grafana) to track data pipeline performance, identify anomalies, and ensure data reliability in real-time.
Cloud Data Platform Operations
Manage and optimize data infrastructure on cloud platforms (AWS, Azure, GCP) using Infrastructure as Code (IaC) principles to ensure scalability and cost-efficiency.
Collaboration & Communication
Foster collaboration between data engineers, data scientists, and business stakeholders, ensuring seamless data flow and effective communication across data teams.
Data Governance & Security
Implement and enforce data governance policies, security measures, and compliance standards across data pipelines and data platforms, ensuring data integrity and regulatory adherence.
Continuous Improvement & Optimization
Continuously evaluate and optimize data processes, tools, and infrastructure to improve efficiency, reduce operational overhead, and enhance data product delivery speed.
Our Alumni Works Here!
Arjun Kapoor
DataOps Engineer
Sakshi Singh
Data Reliability Eng.
Rohan Sharma
Data Platform Ops
Priya Desai
Data Quality Eng.
Vikram Yadav
Associate DataOps
Kavita Rao
Data Pipeline Ops
Manish Kumar
DataOps Intern
Divya Mehta
Data Governance Spec.
Siddharth Jain
Data Automation Eng.
Neha Sharma
DataOps Consultant
Arjun Kapoor
DataOps Engineer
Sakshi Singh
Data Reliability Eng.
Rohan Sharma
Data Platform Ops
Priya Desai
Data Quality Eng.
Vikram Yadav
Associate DataOps
Kavita Rao
Data Pipeline Ops
Manish Kumar
DataOps Intern
Divya Mehta
Data Governance Spec.
Siddharth Jain
Data Automation Eng.
Neha Sharma
DataOps Consultant
What Our DataOps Students Say
"This DataOps course is a game-changer! I learned how to automate data pipelines and ensure data quality end-to-end."
"Implementing CI/CD for data with GitHub Actions was incredibly practical. My team's data delivery speed has significantly improved."
"Great Expectations transformed how we approach data quality. This course made complex testing easy to understand and apply."
"BinnBash Academy's focus on real-time monitoring with Airflow, Prometheus, and Grafana is exactly what's needed in the industry."
"The instructors are highly knowledgeable and provided excellent guidance on building robust and reliable data operations."
"I highly recommend this course for any data professional looking to optimize their data workflows and ensure data integrity."
"From data versioning to cloud DataOps, every module was hands-on and directly applicable to real-world scenarios."
"The emphasis on data governance and collaboration was crucial. It's not just about tools, but about effective team practices."
"Learning to automate and monitor data pipelines has significantly improved my team's efficiency and data reliability."
"The practical approach to learning, combined with industry-relevant tools, made this course stand out from others."
"This DataOps course is a game-changer! I learned how to automate data pipelines and ensure data quality end-to-end."
"Implementing CI/CD for data with GitHub Actions was incredibly practical. My team's data delivery speed has significantly improved."
"Great Expectations transformed how we approach data quality. This course made complex testing easy to understand and apply."
"BinnBash Academy's focus on real-time monitoring with Airflow, Prometheus, and Grafana is exactly what's needed in the industry."
"The instructors are highly knowledgeable and provided excellent guidance on building robust and reliable data operations."
"I highly recommend this course for any data professional looking to optimize their data workflows and ensure data integrity."
"From data versioning to cloud DataOps, every module was hands-on and directly applicable to real-world scenarios."
"The emphasis on data governance and collaboration was crucial. It's not just about tools, but about effective team practices."
"Learning to automate and monitor data pipelines has significantly improved my team's efficiency and data reliability."
"The practical approach to learning, combined with industry-relevant tools, made this course stand out from others."
DataOps Engineer Job Roles After This Course
DataOps Engineer
Data Reliability Engineer
Cloud DataOps Engineer
Data Quality Engineer
Data Platform Engineer (Ops)
Data Automation Specialist
DataOps Consultant
Data Governance Analyst (Ops)