Data Engineer Courses and Certification Training

Overview



Data Engineer is a technology used to manage and analyze large amounts of data. It is used to create data pipelines, store and process data, and create data models. Data Engineers are responsible for designing, building, and maintaining data systems. Major vendors for this technology include Amazon Web Services, Microsoft Azure, Google Cloud Platform, and IBM Cloud. These vendors provide cloud-based solutions for data engineering, such as data warehouses, data lakes, and data pipelines. They also provide tools for data analysis, such as machine learning and artificial intelligence.

About Data Engineer Training and Certification Courses


Data Engineer training and Certification Courses provide the skills and knowledge needed to design, build, and maintain data systems. These courses cover topics such as data modeling, data warehousing, data integration, data security, and data visualization. They also provide hands-on experience with popular data engineering tools and technologies.
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Prerequisites for Data Engineer Certifications


• Knowledge of data engineering fundamentals
• Understanding of data warehousing concepts
• Proficiency in SQL and NoSQL databases
• Knowledge of data modeling and ETL processes
• Familiarity with cloud computing platforms
• Understanding of data security and privacy protocols
• Ability to work with big data technologies such as Hadoop, Spark, and Kafka

Who should take Data Engineer Certification Training Courses?


• Professionals who are looking to gain expertise in data engineering
• Experienced software engineers who want to transition to data engineering
• Data analysts who want to learn data engineering
• Data scientists who want to learn data engineering
• Business intelligence professionals who want to learn data engineering
• IT professionals who want to learn data engineering
• Students who want to learn data engineering
• Anyone who wants to learn data engineering

Skills Earned by Individuals after Completing Data Engineer Training and Certification Courses


1. Understanding of data engineering principles and best practices.
2. Ability to design, develop, and maintain data pipelines.
3. Knowledge of data warehousing and data modeling techniques.
4. Proficiency in programming languages such as Python, Java, and SQL.
5. Ability to use big data technologies such as Hadoop, Spark, and Kafka.
6. Understanding of cloud computing platforms such as AWS and Azure.
7. Knowledge of data security and privacy regulations.
8. Ability to create and maintain data lakes and data warehouses.
9. Proficiency in data visualization tools such as Tableau and Power BI.
10. Understanding of data governance and data quality management.

Reasons to Choose Koenig Solutions for Data Engineer Courses


Benefits of Choosing Koenig Solutions for Data Engineer Training:
• Certified Instructor: Koenig Solutions provides certified instructors to ensure quality training.
• Boost Your Career: Koenig Solutions offers customized training programs to help you boost your career.
• Customized Training Programs: Koenig Solutions offers customized training programs to meet your specific needs.
• Destination Training: Koenig Solutions provides destination training to help you learn in a comfortable environment.
• Affordable Pricing: Koenig Solutions offers affordable pricing to make training accessible to everyone.
• Top Training Institute: Koenig Solutions is a top training institute with a wide range of courses.
• Flexible Dates: Koenig Solutions offers flexible dates to accommodate your schedule.
• Instructor-Led Online Training: Koenig Solutions provides instructor-led online training to help you learn from the comfort of your home.
• Wide Range of Courses: Koenig Solutions offers a wide range of courses to meet your needs.
• Accredited Training: Koenig Solutions provides accredited training to ensure quality learning.

Benefits of Taking Data Engineer Certification Courses


• Improved job prospects: According to a survey by Burning Glass Technologies, Data Engineer roles have grown by over 40% in the last 5 years.
• Higher salaries: Data Engineers earn an average of $117,000 per year, according to Glassdoor.
• Increased knowledge: Data Engineer Certification Training provides a comprehensive understanding of data engineering concepts and technologies.
• Improved problem-solving skills: Data Engineers are able to identify and solve complex data-related problems.
• Enhanced career opportunities: Data Engineers are in high demand in many industries, including healthcare, finance, and technology.

FAQ's

Ans - No, the published fee includes all applicable taxes.

The Data Engineering on Microsoft Azure DP-203 Certification examination is for $165.

The Microsoft Certified: Azure Data Engineer Associate Certification examination is for $165.

As part of your Microsoft Data Engineer certification training, you will receive:
  • A copy of the Microsoft Data Engineer course material
  • Data Engineer Practice test questions and answers
  • Pre-reading materials to prepare for your Data Engineer training
  • Data Engineer certification course hands-on lab sessions
  • Data Engineer course completion certificate
 

The DP-201T01: Designing an Azure Data Solution Certification training course is for $1,550.

The DP-201T01: Designing an Azure Data Solution Certification exam is for $165.

The DP-200T01: Implementing an Azure Data Solution Certification examination is for $165.

The Data Engineering on Microsoft Azure DP-203 Certification exam tests the candidate’s knowledge on the following domains:
  • Design and implementation of data storage tools (40-45%)
  • Design and development of data processing measures (25-30%)
  • Design and implementation of data security measures (10-15%)
  • Monitoring and optimization of data storage as well as data processing (10-15%)
 

The DP-200T01: Implementing an Azure Data Solution Certification exam tests the proficiency of their candidates in the following domains:
  • Implementation of data storage solutions (40-45%)
  • Monitoring and optimization of data solutions (30-35%)
  • Management and development of data processing techniques (25-30%)