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Elevate your skills with our
comprehensive Data Engineering course.

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Objectives

  • This Data Engineering course aims to provide a comprehensive understanding of data engineering, covering data pipelines, ETL processes, data storage, and processing techniques.

  • Participants will learn data modeling, transformation, pipeline orchestration, integration, governance, and security. The course includes hands-on experience and a final project to apply learned skills in real-world scenarios.

Course Outcome

Ability to design, build, and maintain scalable data pipelines.
Proficiency in working with various data storage solutions and processing frameworks.
Competence in data modeling, transformation, and integration.
Understanding of data governance, security, and compliance.
Skill in visualizing and reporting insights from data.
Capability to work with real-world data sets and solve complex data engineering problems.
Readiness for roles such as Data Engineer, ETL Developer, Data Architect, etc.

Why Mazenet?


  • Expert Faculty

    Our Faculty comprises of 300+ SMEs with many years of experience. All our trainers possess a minimum of 8+ years of experience.

  • Proven Track Record

    We have served over 200+ global corporate clients, consistently maintaining a 99% success rate in meeting training objectives for 300+ technologies with quick turnaround time.

  • Blended Learning

    We provide course content over any platform that our clients prefer. You can choose an exclusive platform or a combination of ILT, VILT, and DLP.

  • Learning Paths

    The learning paths are very defined with clear benchmarks. Quantitative assessments at regular intervals measure the success of the learning program.

  • Case Study

    We have amassed over 10,000 case studies to support training delivery. Candidates will be trained to work on any real-time business vertical immediately after the training.

  • 24*7 Global Availability

    We are equipped to conduct training on any day, date or time. We have delivered training pan India, Singapore, North America, Hong Kong, Egypt and Australia.

Delivery Highlights

  • Customized Training Modules

    Training programs are highly flexible with module customizations to suit the requirements of the business units.

  • Certification

    The training can be supplemented with appropriate certifications that are recognized across the industry.

  • Multi-language Support

    Course content can be delivered in English, Spanish, Japanese, Korean or any other language upon request.

  • Personalized Training Reports

    Candidates are assessed individually at regular intervals and are provided unique learning suggestions to suit their learning calibre.

  • Industry-Oriented Training

    Industry-oriented training, completing which, candidates can be immediately deployed for billable projects.

  • Diverse Training Platforms

    Choose from Instructor-Led Training, Virtual Instructor-Led Training, Digital Learning Platform and Blended Training platforms

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Course Preview

  • Overview of Data Engineering and its Role in Data Science
  • Data Pipelines, ETL (Extract, Transform, Load), Data Warehousing

  • Data Storage
  • Relational Databases (SQL) and Database Design
  • NoSQL Databases (MongoDB, Cassandra, etc.)
  • Data Lakes and Data Warehouses
  • Cloud Storage Solutions (Amazon S3, Google Cloud Storage)

  • Data Processing
  • Introduction to Big Data and Distributed Computing
  • Batch Processing with Hadoop MapReduce
  • Stream Processing with Apache Kafka and Apache Flink
  • Real-time Data Processing

  • Data Modeling Concepts (ER Diagrams, Schema Design)
  • Introduction to Data Transformation Tools (Apache Spark, Apache Beam)
  • Data Quality and Data Cleaning Techniques
  • Introduction to Workflow Management (Apache Airflow)

  • Workflow Orchestration and Dependency Management
  • Monitoring and Error Handling in Data Pipelines
  • Managing and Scaling Data Infrastructure

  • Integrating Data from Multiple Sources
  • Data Extraction Techniques (Web Scraping, API Integration)
  • Data Integration Platforms (Talend, Informatica)

  • Data Governance Frameworks and Best Practices
  • Data Security and Compliance Regulations (GDPR, HIPAA, etc.)
  • Data Privacy and Anonymization Techniques

  • Introduction to Data Visualization Tools (Tableau, Power BI)
  • Building Dashboards and Reports
  • Data Exploration and Insights Discovery

  • Machine Learning Engineering for Data Pipelines
  • Data Engineering for IoT and Sensor Data
  • Building Scalable and Fault-Tolerant Systems

  • Planning and Implementing a Data Engineering Project
  • Working with Real-world Data Sets
  • Presenting Insights and Findings