Big Data Engineer Expert

$590.00

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The Engineer of Big Data Professional (IBDP) certification validates the skills and knowledge required to create, design and maintain tools and software to analyze and process large data sets in enterprise environments. This certification ensures that big data engineers have the technical competencies required to develop scalable, reliable and secure solutions for large-scale data processing.

Category:

WHY BECOME CERTIFIED?

  • Obtaining this certification ensures that professionals are up-to-date with the latest technologies, tools and methodologies for managing and analyzing large volumes of data, which is crucial in an increasingly data-driven world.
  • Engineers certified in Big Data can design and maintain efficient and scalable data systems, which increases organizational productivity by enabling faster and more accurate data-driven decisions.
  • A certification in Big Data can open up new opportunities for employment and career growth, as companies actively seek qualified professionals to manage and extract value from their data.

WHO IS IT INTENDED FOR?

  • Data Analysts
  • Software Developers
  • Database administrators
  • IT infrastructure specialists
  • IT Consultants
  • IT project leaders
  • Information Security Professionals
  • Any professional working with large volumes of data or wishing to acquire advanced knowledge and skills in Big Data, regardless of their current level of experience in the field.

COMPETENCIES TO BE CERTIFIED

  • Experience with multicloud computing.
  • Proficiency in data visualization.
  • Knowledge in machine learning and AI applied to big data.
  • Familiarity with NoSQL databases.
  • Proficiency in programming languages such as Python, Ruby, Apache Spark and Rust.
  • Understanding of automation and scripting for data processing.
  • Knowledge of enterprise architecture in big data environments.

ABOUT THE EXAM

Domains of Examination

Big Data Fundamentals:

  • Big data basics.
  • Large-scale data processing architectures.
  • Principles of multicloud computing.

Tools and technologies:

  • Use of data visualization tools.
  • Implementation of machine learning and AI solutions in big data environments.
  • Proficiency in NoSQL databases.

Software development for Big Data:

  • Programming in Python, Ruby and other relevant languages.
  • Implementation of data processing with Apache Spark.
  • Development of scalable and secure solutions.

Automation and Optimization:

  • Automation of data processes.
  • Optimization strategies for big data processing.
  • Resource management in big data environments.

Duration: 3 hours
Examination format: theoretical and practical. Multiple choice questions, short answer questions and practical case studies.
Validity: two (2) years.
Passing score: 70%.

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