8000+
Certified Learners
15+
Years Avg. faculty experience
40+
Happy Clients
4.5/5.0
Average class rating
ABOUT DataOps Courses
In today’s data-driven world, organizations are rapidly adopting DataOps to optimize their data management and delivery processes. At Xopsschool, we offer a comprehensive range of DataOps courses designed to empower individuals and teams with the skills necessary to transform data workflows. Our expert instructors provide hands-on experience, ensuring you gain practical knowledge and the ability to apply what you learn in real-world environments.
Whether you're a data enthusiast looking to transition into DataOps or a professional seeking to deepen your expertise, our tailored curriculum ensures that you not only learn the theory behind DataOps but also master the tools and techniques that can drive efficiency, improve collaboration, and deliver high-quality data faster than ever before.
What is DataOps?
DataOps, short for Data Operations, is a collaborative data management practice that combines data engineering, data quality, and data governance. It focuses on automating and streamlining data workflows to deliver high-quality data in a faster, more agile manner. Drawing inspiration from Agile and DevOps methodologies, DataOps promotes continuous integration, testing, and monitoring to ensure that data pipelines run smoothly and efficiently. As the volume of data continues to grow, organizations must find ways to process and deliver data more efficiently. DataOps not only ensures the timely delivery of data but also empowers teams to collaborate effectively, adapt to change quickly, and continuously improve their processes. With the right knowledge and tools, DataOps can significantly reduce operational risks and increase the speed of data-driven decision-making.
The Importance of DataOps
In today’s rapidly evolving data landscape, businesses face the challenge of managing large-scale, complex data infrastructures. DataOps bridges the gap between data engineering, operations, and business needs, allowing organizations to adopt a more agile, automated, and collaborative approach to managing data pipelines. By enabling faster data delivery and improving the quality and reliability of data, DataOps allows businesses to scale their data operations efficiently while reducing downtime and errors. Furthermore, DataOps facilitates better collaboration between cross-functional teams, ensuring that data engineers, data scientists, and analysts are aligned on objectives. It also integrates data governance practices, ensuring that data is not only delivered faster but also in compliance with privacy regulations and internal policies. Adopting DataOps can result in improved operational efficiency, reduced costs, and a more robust data pipeline.
Courses Overview
Our DataOps Courses
At Xopsschool, we offer a diverse range of DataOps courses, catering to professionals at every stage of their career. Whether you're just starting out or looking to advance your skills, our courses are designed to meet the demands of modern data operations. Each course is crafted to provide you with both theoretical knowledge and hands-on experience, ensuring you can immediately apply what you learn to real-world challenges. Our courses cover essential DataOps practices such as continuous integration, automated testing, data pipeline orchestration, and data quality monitoring. From foundational concepts to advanced strategies, we ensure that you build a robust understanding of DataOps that can drive tangible results within your organization.
What You’ll Learn
Each of our DataOps courses is designed with a focus on practical application and real-world use cases. For beginners, our foundational courses introduce the core principles of DataOps, data pipelines, and essential tools. For intermediate learners, we delve deeper into data integration techniques, cloud-based data operations, and automation frameworks. Advanced learners will have the opportunity to explore topics like enterprise-level DataOps strategies, big data ecosystems, and the integration of AI/ML workflows within DataOps. Upon completing our courses, you will be equipped with the knowledge to implement DataOps practices in your organization, enhancing data quality, operational efficiency, and collaboration. You will also have the opportunity to earn a certification that demonstrates your expertise in DataOps.
Why Choose Xopsschool for DataOps?
Expert Instructors with Real-World Experience:- At Xopsschool, we are committed to providing high-quality, industry-relevant education. Our instructors are seasoned professionals with extensive experience in DataOps, data engineering, and cloud computing. They bring their knowledge of real-world challenges into the classroom, offering practical insights and solutions to common data management problems. Each instructor is also dedicated to mentoring and providing personalized support to help you succeed.
Comprehensive, Hands-On Learning Experience:- We believe in the power of hands-on learning. Our courses are designed with interactive labs, case studies, and projects that allow you to work with real datasets and use industry-standard tools. This practical approach ensures that you not only understand the theoretical aspects of DataOps but also gain the technical skills to apply your knowledge in any organizational setting. Whether you're learning how to build data pipelines or automate quality checks, our courses offer real-world scenarios to challenge and expand your capabilities.
Instructor-led, Live & Interactive Sessions
DURATION |
MODE |
PRICE |
ENROLL NOW |
|---|---|---|---|
60 Hrs |
Self learning using Video |
9,999/- |
|
60 Hrs |
Live & Interactive in Online Batch |
24,999/- |
|
60 Hrs |
One to One Live & Interactive in Online |
59,999/- |
|
2 - 3 Days (Approx) |
Corporate (Online/Classroom) |
Contact US |
Calendar |
AGENDA OF THE DATAOPS COURSES Download Curriculum
- What is DataOps?
- Importance and Benefits of DataOps in modern organizations.
- Key principles and practices of DataOps.
- The intersection of DataOps, Agile, and DevOps.
- Understanding Data Pipelines: Definition and components.
- Types of data pipelines: Batch vs. real-time.
- Tools for data pipeline orchestration (e.g., Apache Airflow, Prefect).
- Introduction to data integration and ETL (Extract, Transform, Load).
- Automating data workflows and pipeline processes.
- Monitoring tools and practices to ensure data quality.
- Introduction to CI/CD (Continuous Integration/Continuous Deployment) for data.
- Using monitoring tools (e.g., Prometheus, Grafana).
- Introduction to Data Governance: Definition, policies, and practices.
- Data security and compliance (GDPR, CCPA).
- Managing data access control and permissions.
- Tools for ensuring data privacy and security.
- Designing and optimizing data pipelines for scalability.
- Pipeline architecture: Single-purpose vs. multipurpose pipelines.
- Data integration with cloud platforms (AWS, Google Cloud, Azure).
- Leveraging tools like Apache Kafka for real-time data streaming.
- Implementing CI/CD for data pipelines.
- Automation tools and frameworks (e.g., Jenkins, GitLab CI, CircleCI).
- Setting up automated testing for data pipelines (unit tests, integration tests).
- Managing deployment environments (development, staging, production).
- Ensuring data quality in DataOps workflows.
- Techniques for data validation and error handling.
- Leveraging tools like Great Expectations and dbt (data build tool) for data testing.
- Establishing data quality gates and thresholds.
- Using cloud platforms for DataOps: AWS, GCP, and Azure.
- Building and managing cloud-based data pipelines.
- Cloud-native DataOps tools and services (e.g., AWS Data Pipeline, Google Cloud Dataflow).
- Integrating cloud-based storage and data lakes.
- Integrating DataOps with ML/AI pipelines.
- Automating data preprocessing and feature engineering.
- Version control for data and models (e.g., DVC - Data Version Control).
- Best practices for model deployment and monitoring.
- Techniques for building real-time data pipelines.
- Tools for real-time data streaming (Apache Kafka, Apache Flink, AWS Kinesis).
- Monitoring and troubleshooting in real-time data systems.
- Case studies on real-time data processing in DataOps environments.
- Defining enterprise-level DataOps frameworks.
- Principles of DataOps at scale.
- Managing complex data pipelines and distributed data systems.
- Tools for managing multi-cloud environments.
- Integrating DataOps with big data platforms (e.g., Hadoop, Spark, Presto).
- Working with large-scale data lakes and data warehouses.
- Optimizing big data pipelines for performance and efficiency.
- Case study: Large-scale data pipeline management.
- Implementing continuous delivery for data products.
- Managing versioning and updates in real-time data systems.
- Ensuring continuous integration and continuous testing for data.
- Using feature flags and blue-green deployments in DataOps.
- Building scalable automation systems for data workflows.
- Techniques for maintaining automation consistency across multiple teams.
- Automating governance and security controls.
- Leveraging Kubernetes and containerization for scalable data operations.
- Best practices for combining DataOps, DevOps, and MLOps.
- Continuous integration for machine learning and data pipelines.
- Managing model deployment and monitoring in DataOps workflows.
- Case study: End-to-end integration of DataOps and MLOps.
- Advanced techniques for managing and ensuring data quality at scale.
- Building robust data quality checks and workflows.
- Automating data quality reporting and analytics.
- Tools and frameworks for data profiling and validation (e.g., Deequ, Talend).
- Leveraging DataOps for large-scale AI/ML models.
- Real-time data analytics and monitoring with DataOps.
- Techniques for optimizing data workflows for AI/ML applications.
- Scaling real-time analytics systems using DataOps tools.
- Real-time performance tuning for data pipelines.
- Techniques for optimizing and troubleshooting pipeline performance.
- Advanced monitoring strategies using observability tools (e.g., Prometheus, Grafana, Datadog).
- Creating dashboards for end-to-end monitoring of DataOps workflows.
- Ensuring regulatory compliance in DataOps.
- Managing and automating security checks for data.
- Data encryption and access controls in pipelines.
- Tools for audit trails and compliance reporting.
- Leveraging cloud-native tools (e.g., AWS Lambda, Google Dataflow).
- Cloud-native orchestration and automation tools.
- Building serverless data pipelines for scalability.
PROJECT
As part of this projects, we would help student to have first hand experience of real time software project development planning, coding, deployment, setup and monitoring in production from scratch to end. We would also help students to visualize a real development environment, testing environment and production environments. The goal of this project. Project technology would be Java, Pythong and DOTNET and based on microservices concept..
INTERVIEW
As part of this, You would given complete interview preparation support until you clear a interview and get onboarded with organziation including demo inteview and guidance. More than 50 sets of Interview KIT would be given including various project scenario of the projects.
OUR COURSE IN COMPARISON
| FEATURES | XOPSSCHOOL | OTHERS |
|---|---|---|
| Faculty Profile Check | ||
| Lifetime Technical Support | ||
| Lifetime LMS access | ||
| Top 25 Tools | ||
| Interviews Kit | ||
| Training Notes | ||
| Step by Step Web Based Tutorials | ||
| Training Slides | ||
| Training + Additional Videos |
- Data-Driven Applications: Many applications now rely on large datasets. DataOps helps engineers manage and automate these data workflows effectively.
- Collaboration: Promotes collaboration between software engineers, data engineers, and data scientists for smoother development cycles.
- Automation: DataOps practices automate repetitive tasks, reducing manual work and speeding up the development process.
- Data Quality: Ensures high-quality, consistent data throughout the development process, which is crucial for reliable software.
- Faster Integration: Allows for faster integration of data into applications using continuous integration and deployment (CI/CD) principles.
- Practical Skills: Gain hands-on experience with real-world data tools and technologies to build and manage efficient data pipelines.
- Streamline Data Workflows: Learn how to automate and optimize data workflows, improving both speed and reliability.
- Collaborative Approach: Understand how DataOps enhances collaboration between software engineers, data engineers, and analysts.
- Improve Data Quality: Master techniques for ensuring data consistency, validation, and quality throughout the pipeline.
- Cloud and Scalability: Learn how to implement DataOps in cloud environments and scale data operations as your organization grows.
- Real-Time Data Processing: Develop the skills to build and manage real-time data pipelines, crucial for modern data-driven applications.
- Agile Integration: Use agile principles to continuously integrate and deliver high-quality data in fast-paced development cycles.
- Basic Programming Knowledge: Familiarity with programming languages like Python, Java, or SQL.
- Understanding of Data Structures: Basic knowledge of how data is structured, stored, and processed.
- Experience with Databases: Familiarity with relational and non-relational databases (e.g., MySQL, MongoDB).
- Familiarity with Software Development: Understanding of software development life cycles (SDLC) and version control systems (e.g., Git).
- Basic Cloud Knowledge (Optional): Familiarity with cloud platforms like AWS, Google Cloud, or Azure is helpful but not mandatory.
- Data Processing Concepts: A basic understanding of data processing concepts like ETL (Extract, Transform, Load) or batch vs. real-time processing.
- Basic DevOps Knowledge (Optional): Understanding of DevOps principles such as CI/CD, automation, and monitoring.
UPCOMING EVENTS - OTHER CERTIFICATION COURSES
October 2020
October 2020
October 2020
October 2020
October 2020
Need Assistance
Feel Free To Contact Us -
+91 84094 92687
(INDIA)
+1 (469) 756-6329
(USA)
DATAOPS COURSES CERTIFICATIONS
What are the benefits of DataOps Courses certifications?
Certifications always play an important role in any profession. It is considered as one of the best way to determine one’s credibility and worth in the professional career. The same thing is applicable in DataOps, if you are a certified DataOps professional then you will get certain benefits:
- DataOps Courses certification assure recruiters that the DataOps professional whom we are going to hire have the skills, knowledge, and competency to perform the responsibilities what expected from them after hiring.
- DataOps as a technology is dominating the job market. DataOps engineer ranks #2 on Glassdoor's best jobs rankings. The role of DataOps certified professional has seen a 200% jump in postings on indeed" according to SD times report
- This certified course helps anyone who aspires to make a career as a DataOps professional.
- DataOps Certified Engineer is one of the most highly paid job roles in the world. Pay scale even for junior level DataOps practitioner is quite high.
- According to indeed and other job portals, 80% of the companies pay more than $90000 starting salary to a DataOps Certified Professionals. Also, 35% pay at least $115000 and 17% more than $125000
- Obviously there are several factors which affects salary for like geography, skills, company, it varies accordingly. In Cities like Bangalore/Hyderabad DataOps Certified Professional can expect - INR 3,25,672 - 19,42,394 which is not at all a bad package.
- DataOps is going to stay here for long time. Therefore, organizations and as well as professionals need to change as they have no choice but to evolve. If they will evolve then demands of certified professionals will definitely increase and the sooner you do it, the sooner you will be in a leading position.
View more
FREQUENTLY ASKED QUESTIONS
1. What is DataOps?
DataOps is a set of practices, principles, and tools aimed at automating and streamlining the data pipeline lifecycle. It integrates Agile, DevOps, and CI/CD to improve the speed, quality, and collaboration across teams handling data.
2. Who should take the DataOps course?
This course is ideal for data engineers, software developers, DevOps professionals, IT teams, and anyone involved in data management, analytics, or machine learning. It's suitable for both beginners and advanced learners looking to deepen their knowledge of modern data operations.
3. What skills will I gain from the course?
By the end of the course, you will:
- Design and automate data pipelines for both batch and real-time processing.
- Implement CI/CD and version control for data workflows.
- Ensure data quality and monitoring.
- Use popular DataOps tools such as Apache Airflow, Apache Kafka, and cloud-based platforms like AWS and Azure.
4. Do I need any prior experience to join this course?
Basic knowledge of programming (Python, SQL) and understanding of data concepts (ETL) will be helpful but not mandatory. The course is structured to guide beginners through foundational concepts before advancing to complex topics.
5. How long is the DataOps course?
The course duration varies by level:
- Beginner Level: 4-6 weeks.
- Intermediate/Advanced Levels: 6-8 weeks. The course is self-paced with regular milestones to help you stay on track.
6. Can I take the course if I’m new to cloud platforms?
Yes, the course will introduce basic cloud concepts. While prior cloud knowledge (AWS, Google Cloud, Azure) is helpful, you will learn to work with cloud platforms and cloud-native data services as part of the course.
7. What is the format of the course?
The course is a combination of video lectures, hands-on labs, interactive assignments, and live sessions. It includes practical exercises using real-world tools and scenarios, allowing you to apply your knowledge.
8. What tools and technologies are covered in the course?
The course covers industry-standard DataOps tools and technologies, including:
- Apache Airflow for pipeline orchestration.
- Apache Kafka for real-time data streaming.
- dbt for data transformation.
- AWS, Azure, and Google Cloud for cloud-based data operations.
- Jenkins, GitLab CI for CI/CD automation.
9. Will I receive a certification upon completion?
Yes, you will receive a DataOps Certification after successfully completing the course, demonstrating your expertise in DataOps and its application in real-world scenarios.
10. How is the course structured?
The course is broken down into multiple modules, each focusing on a different aspect of DataOps, such as data pipeline orchestration, automation, monitoring, cloud integration, and security. Each module includes lectures, hands-on exercises, and real-world case studies.
11. What if I miss a live session?
All live sessions are recorded and available for later viewing. You can catch up on any missed content and still engage with course materials at your own pace.
12. How much time will I need to dedicate each week?
On average, students should expect to dedicate 4-6 hours per week for the course, including watching lectures, completing exercises, and engaging in discussions. This may vary depending on your experience level and learning pace.
13. What happens if I don’t understand a concept or need help during the course?
You will have access to instructors via email and forums. There are also weekly office hours where you can ask questions, discuss challenges, and get personalized help.
14. Is this course applicable to all industries?
Yes, DataOps principles are applicable across all industries that rely on data, including finance, healthcare, e-commerce, and technology. The skills gained can be used to optimize data management in any data-driven organization.
15. How will DataOps benefit my career?
DataOps is an in-demand skill as more companies focus on managing their data pipelines efficiently. By mastering DataOps, you will be better equipped for roles in data engineering, software development, DevOps, and machine learning, enhancing your career prospects in a rapidly growing field.
16. Can I get a refund if I am not satisfied with the course?
Yes, we offer a 30-day money-back guarantee. If you feel the course doesn't meet your expectations within the first 30 days, you can request a full refund, no questions asked.
REVIEWS
Abhinav Gupta, Pune
(5.0)The training was very useful and interactive. Rajesh helped develop the confidence of all.
Indrayani, India
(5.0)Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.
Ravi Daur , Noida
(5.0)Good training session about basic DataOps concepts. Working session were also good, howeverproper query resolution was sometimes missed, maybe due to time constraint.
Sumit Kulkarni, Software Engineer
(5.0)Very well organized training, helped a lot to understand the DataOps concept and detailed related to various tools.Very helpful.
Vinayakumar, Project Manager, Bangalore
(5.0)Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.
Abhinav Gupta, Pune
(5.0)The training with Xopsschool was a good experience. Rajesh was very helping and clear with concepts. The only suggestion is to improve the course content.
Google Ratings
Videos Reviews
Facebook Ratings
RELATED COURSE
RELATED BLOGS
OUR GALLERY
- Bangalore :-
Xopsschool Training Venue (Vervenest Technologies Private Limited) 3478J HAL 2ND Stage, Chirush Mansion, 2nd & 3rd Floors, 13th Main Road, HAL 2nd Stage,Indiranagar, Bengaluru, Karnataka 560008 - Hyderabad :-
DevOpsSchol Training Venue(Palmeto Solutions) 8th floor, Vaishnavi Cynosure,Telecom Nagar, Gachibowli, Telangana -500032 Land Mark : Reliance Digital Building, Next to Gachibowli Flyover.
TRENDING COURSES
- Master in DevOps Engineering (MDE)
- Master in Azure DevOps
- Master in Machine Learning
- Master in Big Data Hadoop
- Master in Artificial Intelligence
- Master in AppDynamics
- Master in Python Programming
- Master in Cloud Computing
- Master in Datadog
- Master in GitLab