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Data engineering
Best practices Data engineer Education ELT Spark Big data Orchestration Will they blend
  1. Workflow
    Interactive Workflow Metadata exploration
    KNIMEServer Web application Workflow summary
    +2
    This workflow shows a simple guided analytics application for exploring the metadata of a workflow. The example uses the Analyze …
    knime > Examples > 09_Enterprise > 03_Metadata_Mapping > Interactive_Workflow_Metadata_Exploration
    3
    knime
  2. Workflow
    Database Jam Session
    Database Credentials MySQL
    +8
    This workflow jams together data from not one, not two, but six databases! They are: MySQL, MongoDB, MS SQL Server, MariaDB, Orac…
    knime > Examples > 01_Data_Access > 02_Databases > 08_Database_Jam_Session
    3
    knime
  3. Workflow
    Try & Catch for Google Books API
    Onboarding Data engineering
    Use this workflow to wrap your GET request to Google Books API into a Try & Catch
    lada > Public > Examples > Try & Catch for Google Books API
    1
    lada
  4. Workflow
    Microsoft Sharepoint meets Google Cloud Storage
    Google Cloud Microsoft Sharepoint
    +6
    This workflow accesses data on Google Cloud Storage and on Microsoft Sharepoint, blends the data, and formats the data into a tab…
    haoran > Public > 01_Data_Access > 06_ZIP_and_Remote_Files > 08_Microsoft_Sharepoint_meets_Google_Cloud_Storage
    0
    haoran
  5. Workflow
    Database Jam Session
    Database Credentials MySQL
    +8
    This workflow jams together data from not one, not two, but six databases! They are: MySQL, MongoDB, MS SQL Server, MariaDB, Orac…
    haoran > Public > 01_Data_Access > 02_Databases > 08_Database_Jam_Session
    0
    haoran
  6. 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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_4_Orchestration > 04.0_Reset_DB&Big_Data_Environment
    0
    hayasaka
  7. 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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.2_Missing_value_imputation_on_Spark
    0
    hayasaka
  8. 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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.1_In-database&Spark_processing
    0
    hayasaka
  9. 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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.0_Setup_Local_Big_Data_Environment
    0
    hayasaka
  10. Workflow
    01.2_Extract_S3_data&Blend_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. …
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_1_ETL_Processing_I > 01.2_Extract_S3_data&Blend
    0
    hayasaka
  11. 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. …
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_2_ETL_Processing_II > 02.2_Database_update
    0
    hayasaka
  12. Workflow
    MDF meets Apache Kafka
    MDF Kafka Apache
    +8
    The MDF Reader node extracts sensor data about Motor and Turbine speed from a MDF file. The Kafka Connector and Kafka Consumer no…
    haoran > Public > 01_Data_Access > 10_Other > 01_MDF_meets_Apache_Kafka
    0
    haoran
  13. 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. …
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > solutions > Session_2_ETL_Processing_II > 02.2_Database_update
    0
    hayasaka
  14. 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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > solutions > Session_3_ELT_on_Big_Data > 03.1_In-database&Spark_processing
    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…
    knime > Education > Courses > 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
    04.3 Orchestration_solution
    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 > solutions > Session_4_Orchestration > 04.3_Orchestration
    0
    hayasaka
  17. Workflow
    03.4_Writing_from_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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > solutions > Session_3_ELT_on_Big_Data > 03.4_Writing_from_Spark
    0
    hayasaka
  18. 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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.4_Writing_from_Spark
    0
    hayasaka
  19. 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. …
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > solutions > Session_1_ETL_Processing_I > 01.2_Extract_S3_data&Blend
    0
    hayasaka
  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…
    knime > Education > Courses > L4-DE Best Practices for Data Engineering > exercises > Session_3_ELT_on_Big_Data > 03.3_Aggregation_on_Spark
    0
    hayasaka