Data Engineering: Pipelines, ETL, Hadoop
This course provides a comprehensive guide to mastering data engineering, where you'll learn to build robust data pipelines, delve into ETL (Extract, Transform, Load) processes, and handle large datasets using Hadoop.
Overview
This course includes:
- 2 hour of on-demand video
- Certificate of completion
- Direct access/chat with the instructor
- 100% self-paced online
This course provides a comprehensive guide to mastering data engineering, where you'll learn to build robust data pipelines, delve into ETL (Extract, Transform, Load) processes, and handle large datasets using Hadoop. You will gain expertise in extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or big data platforms. With hands-on experience in Hadoop, the industry-standard framework for handling massive datasets, you’ll learn to manage and process massive datasets efficiently. Whether you're a beginner or an experienced professional, this course equips you with the skills to design, implement, and manage data pipelines, making you a valuable asset in any data-focused organization.
Skills You Will Gain
Learning Outcomes (At the end of this program you will be able to)
- Analyze the architecture and components of data pipelines to understand their impact on data flow and processing efficiency.
- Implement robust ETL processes, for scalability and maintainability.
- Analyze big data challenges and introduce Hadoop ecosystem tools (HDFS, MapReduce, Hive, Pig, and Spark) for data processing tasks.
Prerequisites
This course is ideal for aspiring data engineers, software developers interested in data processing, and IT professionals looking to expand their expertise into data engineering. It is also suitable for business analysts and other professionals who seek a foundational understanding of data handling technologies to improve decision-making capabilities and enhance their roles in data-driven environments. Whether you are just starting your journey in data engineering or looking to strengthen your existing skills, this course will provide the knowledge and tools you need to succeed.
Who Should Attend
To get the most out of this course, you should have a basic understanding of programming concepts and some familiarity with database systems. A foundational knowledge of Python programming and SQL will be helpful, as will an understanding of relational database systems. No prior experience with Hadoop is required, but a keen interest in big data and data analytics will greatly enhance your learning experience.
Curriculum
Instructors
Frequently Asked Questions
How much do the courses at Starweaver cost?
We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.
Do you offer any certifications upon completion of a course at Starweaver?
Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.
Does Starweaver offer any free courses or trials?
No, we don't offer any free courses, but we do offer 5-day trial only on our subscriptions-based plans.
Are Starweaver's courses designed for beginners or advanced students?
Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.
What payment options are available for Starweaver courses?
We accept various payment methods such as major credit cards, PayPal, wire transfer, and company purchase orders. For more information related to payments contact customer support.
Do you offer refunds?
Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.
