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  1. Workflow
    02.2 Database update_exercise
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_2_ETL_Processing_II > 02.2_Database_update
    0
    hayasaka
  2. Workflow
    04.1_ETL_Customers_exercise
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_4_Orchestration > 04.1_ETL_Customers
    0
    hayasaka
  3. Workflow
    02.1_Data_Anonymization_exercise
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_2_ETL_Processing_II > 02.1_Data_Anonymization
    0
    hayasaka
  4. Workflow
    01.1_Extract_WebService_data_solution
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > solutions > Session_1_ETL_Processing_I > 01.1_Extract_WebService_data
    0
    hayasaka
  5. Workflow
    03.1_In-database&Spark_processing_exercise
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.1_In-database&Spark_processing
    0
    hayasaka
  6. Workflow
    03.4_Writing_from_Spark_exercise
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.4_Writing_from_Spark
    0
    hayasaka
  7. Workflow
    01.2_Extract_S3_data&Blend_solution
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > solutions > Session_1_ETL_Processing_I > 01.2_Extract_S3_data&Blend
    0
    hayasaka
  8. Workflow
    03.2_Missing_value_imputation_on_Spark_exercise
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.2_Missing_value_imputation_on_Spark
    0
    hayasaka
  9. Workflow
    04.0_Reset_DB&Big_Data_Environment
    Education Data engineering Data engineer
    +3
    This workflow resets the database by overwriting the customers and statistics tables and sets up a local big data environment and…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_4_Orchestration > 04.0_Reset_DB&Big_Data_Environment
    0
    hayasaka
  10. Workflow
    03.1_In-database&Spark_processing_solution
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > solutions > Session_3_ELT_on_Big_Data > 03.1_In-database&Spark_processing
    0
    hayasaka
  11. Workflow
    02.2 Database update_solution
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > solutions > Session_2_ETL_Processing_II > 02.2_Database_update
    0
    hayasaka
  12. Workflow
    01.1_Extract_WebService_data_exercise
    Best practices Data engineer Data engineering
    +1
    The company has two branches: one in the United States and another one in Europe. New US customer data comes from a web service. …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_1_ETL_Processing_I > 01.1_Extract_WebService_data
    0
    hayasaka
  13. Workflow
    03.0_Setup_Local_Big_Data_Environment
    Education Data engineering Data engineer
    +3
    This workflow sets up a local big data environment for the next exercise. It creates a local big data environment and loads the u…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.0_Setup_Local_Big_Data_Environment
    0
    hayasaka
  14. Workflow
    03.3_Aggregation_on_Spark_exercise
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.3_Aggregation_on_Spark
    0
    hayasaka
  15. Workflow
    03.2_Missing_value_imputation_on_Spark_solution
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > solutions > Session_3_ELT_on_Big_Data > 03.2_Missing_value_imputation_on_Spark
    0
    hayasaka
  16. Workflow
    03.3_Aggregation_on_Spark_solution
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > solutions > Session_3_ELT_on_Big_Data > 03.3_Aggregation_on_Spark
    0
    hayasaka
  17. Workflow
    04.3 Orchestration_exercise
    Best practices Data engineer Data engineering
    +4
    Once the workflow the ETL on Customers data is executed successfully, i.e., customer data are accessed and anonymized, and the da…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_4_Orchestration > 04.3_Orchestration
    0
    hayasaka
  18. Workflow
    04.2_ELT_Usage
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DE Best Practices for Data Engineering > exercises > Session_4_Orchestration > 04.2_ELT_Usage
    0
    hayasaka
  19. Workflow
    Parse Contract Values Testflow
    Best practices Data engineer Data engineering
    +7
    For the company, it is important that contract values are always parsed correctly, even after the changes in the workflow. Theref…
    lada > Public > Testing Framework > testflows > report > 01_Parse_Contract_Values_Testflow
    0
    lada
  20. Workflow
    03.3_Aggregation_on_Spark_exercise
    Education Data engineering Data engineer
    +3
    The company tracks the usage of the website and stores the information about each session. - Various data are collected, e.g., se…
    chemgirl36 > Public Space > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.3_Aggregation_on_Spark
    0
    chemgirl36