Advanced Data Center Management and Infrastructure

"Mastering the Heart of IT: Advanced Data Center Management and Infrastructure"
Free tutorial
Rating: 4.6 out of 5 (40 ratings)
1,351 students
51min of on-demand video
English [Auto]

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  • Basic understanding of business concepts: It is recommended to have a foundational knowledge of business principles and terminology. Familiarity with concepts such as business models, competitive analysis, and financial statements will be beneficial.


In today's digital age, data centers are the backbone of modern businesses, ensuring the efficient storage, processing, and management of vast amounts of data. The Advanced Data Center Management and Infrastructure course is designed to equip professionals with the advanced knowledge and skills required to excel in the field of data center management.

Course Objectives:

  1. Optimize Data Center Efficiency: Learn advanced strategies and best practices to maximize the efficiency of data center operations, reduce energy consumption, and lower operational costs.

  2. Scalability and Capacity Planning: Explore techniques for effectively planning and managing data center growth, including capacity planning, resource allocation, and scaling strategies.

  3. Advanced Virtualization: Dive deep into virtualization technologies and their role in data center management, including containerization, software-defined networking (SDN), and virtualized storage solutions.

  4. Security and Compliance: Understand advanced security measures and compliance requirements specific to data centers, focusing on safeguarding sensitive data and mitigating risks.

  5. Disaster Recovery and Business Continuity: Develop comprehensive disaster recovery and business continuity plans to ensure data center resilience in the face of unexpected events.

  6. Automation and Orchestration: Learn about automation tools and orchestration frameworks to streamline routine tasks, enhance operational efficiency, and reduce human error.

  7. Cloud Integration: Explore strategies for integrating on-premises data centers with cloud services, enabling hybrid cloud environments, and optimizing cloud resource management.

  8. Monitoring and Analytics: Gain expertise in advanced monitoring and analytics tools to proactively identify and resolve issues, optimize performance, and make data-driven decisions.

  9. Hardware Infrastructure Management: Delve into the management of physical infrastructure components such as servers, storage systems, networking equipment, and cooling systems.

  10. Green Data Center Practices: Learn about environmentally sustainable practices for data centers, including green energy solutions and eco-friendly design principles.

  11. Case Studies and Real-World Applications: Analyze real-world data center scenarios and case studies to apply theoretical knowledge to practical situations.

  12. Industry Trends and Emerging Technologies: Stay up-to-date with the latest industry trends, emerging technologies, and innovations in data center management.

Who this course is for:

  • System Administrators
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Cloud Infrastructure Engineers


Educator || 1M+ Students Trained
Akhil Vydyula
  • 3.9 Instructor Rating
  • 221 Reviews
  • 16,152 Students
  • 31 Courses

Hello, I'm Akhil, an Associate Consultant at Atos India with a focus on the Advisory Consulting practice, specializing in Data and Analytics. My professional journey has led me through various facets of data analysis and modeling, particularly in the BFSI sector, where I've had the privilege of overseeing the full lifecycle of development and execution.

My skill set encompasses a wide range of data-related tasks, including data wrangling, feature engineering, algorithm development, model training, and implementation. I thrive on leveraging data mining techniques such as statistical analysis, hypothesis testing, regression analysis, as well as both unsupervised and supervised machine learning processes to extract meaningful insights and drive data-informed decisions. I'm particularly passionate about risk identification through decision models, and I've honed my expertise in Machine Learning Algorithms, Data/Text Mining techniques, and Data Visualization to effectively address these challenges.

Currently, I'm immersed in an exciting Amazon cloud project that involves end-to-end development of ETL processing. In this role, I craft ETL processing code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, execute scripts using EMR services, and load consolidated data into Postgres SQL (RDS/Redshift) on a full, incremental, and live basis. To streamline this process, I've automated it by creating jobs in Step functions, which trigger EMR instances to run scripts in a specific order and send notifications upon execution status changes. The scheduling of these Step functions is achieved through event bridge rules.

Additionally, I've worked extensively with AWS Glue, using it to replicate source data from on-premises systems to raw-layer S3 buckets via AWS DMS services. One of my key strengths lies in my ability to understand the nuances of data and apply the right transformations to convert data from multiple tables into key-value pairs. Furthermore, I've optimized the performance of stored procedures in Postgres SQL to execute second-level transformations by efficiently joining multiple tables and loading the data into final tables.

I'm passionate about harnessing the power of data to drive actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, feel free to connect with me. Let's explore the endless possibilities that data analytics has to offer!

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