L4-DE: Data Engineering in KNIME Analytics Platform

Duration: Hours

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    Training Mode: Online

    Description

    Introduction of Data Engineering in KNIME Analytics:

    The L4-DE: Data Engineering in KNIME Analytics Platform certification is designed for professionals eager to enhance their expertise in data engineering practices using KNIME. This course covers crucial concepts such as data integration, data transformation, and ETL (Extract, Transform, Load) processes, enabling participants to design and manage robust data pipelines. Through hands-on training, candidates will learn how to efficiently ingest, preprocess, and transform large datasets, ensuring that data is ready for analysis and reporting.

    The certification emphasizes best practices for building scalable and maintainable workflows, equipping professionals with the skills to handle complex data environments. Participants will explore techniques for connecting to various data sources, managing data quality, and implementing data governance. By mastering these competencies, candidates will be well-prepared to support their organizations in deriving actionable insights from high-quality, well-engineered data. This certification is particularly valuable for data engineers, analysts, and anyone involved in data management and processing within the KNIME ecosystem.

    Cost: $150

    Prerequisites: L1, L2, & L3 Certification
    Recommended Preparation Courses: L2-DE, L3-DE, and L4-DE
    65 minute multiple-choice exam: 45 questions

    1. Basic Knowledge of KNIME:
      Familiarity with the KNIME Analytics Platform interface and the ability to create and manipulate simple workflows.
    2. Understanding of Data Analytics Concepts:
      A foundational understanding of data analytics principles, including data manipulation and visualization techniques.
    3. Experience with Databases:
      Basic knowledge of databases, SQL, and data storage concepts, as well as the ability to query and manage relational data.
    4. Data Integration Skills:
      Familiarity with data integration methods and tools, including understanding ETL (Extract, Transform, Load) processes.
    5. Analytical Skills:
      Strong analytical thinking and problem-solving skills to design and implement efficient data workflows effectively

    Topics Covered:

    • Aspects of data engineering
    • Data types
    • Data sources
    • Data pipelines
    • Data storage
    • Data governance
    • Big data connectivity and processing with Apache Spark
    • Best practices for data engineering

    Conclusion:
    This certification equips professionals with the essential skills needed to design and implement effective data engineering solutions using KNIME. By mastering data integration and transformation techniques, candidates will improve their ability to prepare data for analysis, ensuring high-quality insights. Achieving this certification demonstrates a commitment to advancing data engineering capabilities in a rapidly evolving data landscape.

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