School of Hive - with KNIME's local Big Data environment (SQL for Big Data)
Demonstrates a collection of Hive functions using KNIME's local Big Data environment including creating table structures from scratch and from an existing file and working with partitions.
Partitions are an essential organizing principle of Big Data systems. They will make is easier to store and handel big data tables.
All examples are fully functional. You could switch out the local big data environment for your own one (Cloudera e.g.).
This example focusses on Hive-SQL script in executors. Similar effects could be achieved by using KNIME's DB nodes.
https://hub.knime.com/mlauber71/spaces/Public/latest/_db_sql_bigdata_hive_spark_meta_collection#externalresources
External resources
- Combine Big Data, Spark and H2O.ai Sparkling Water
- KNIME and Hive - load multiple CSV files at once via external table
- add fields to Hive table
- Create HIVE table and show the way it was created and describe the structure
- MAX_ROW_SIZE Query Option
- A meta collection of KNIME and databases (SQL, Big Data/Hive/Impala and Spark/PySpark)
- This workflow demonstrates several methods to import one or many CSV file into Hive
- Hive - LanguageManual UDF - Date Functions
- This workflow demonstrates several methods to import one or many CSV file into Hive
- L4-BD Introduction to Big Data with KNIME Analytics Platform
- (official) KNIME Big Data Extensions User Guide
- HUB: School of Hive - with KNIME's local Big Data environment (SQL for Big Data)
- MEDIUM Blog: KNIME, Databases and SQL
Used extensions & nodes
Created with KNIME Analytics Platform version 4.7.8
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