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Advanced - Discover the KNIME Possibilities

<p>KNIME - "Discover the possibilities" (for advanced users) - further ideas and suggestions for working with KNIME</p>

KNIME und Python (KNIME kann auch Java und R unter der Haube)

KNIME und SQL / Datenbanken

KNIME und Big Data, Spark und Hive

KNIME and advanced graphics in R and Python, including interactive data apps

https://medium.com/p/841df87b5563

KNIME Data Apps - interactive handling and display of data

https://forum.knime.com/t/interactive-dashboard-where-bar-chart-and-table-consume-same-filter/57227/2?u=mlauber71

KNIME Data Connect: DACH 2022 - Advanced Visualizations with Python and KNIME

You can rewatch the talks on YouTube: https://youtu.be/mG2SZiKG9zo?t=2108

BOOK - Codeless Deep Learning with KNIME
Build, train, and execute various deep neural network architectures using KNIME.
https://www.knime.com/codeless-deep-learning-book


BOOK - Codeless Time Series Analysis with KNIME
Build, train, and execute various deep neural network architectures using KNIME.
https://www.knime.com/codeless-time-series-analysis-with-knime

KNIME - "Discover the possibilities" (for advanced users) - further ideas and suggestions for working with KNIME

Meta collection about KNIME and Text analytics and Text mining https://hub.knime.com/mlauber71/spaces/Public/latest/_text_analytics_and_knime_meta_collection?u=mlauber71 KNIME - adress deduplication https://yam-united.telekom.com/workspaces/knime/apps/wiki/wiki/list/view/40433162-e4cf-4b3b-bc9d-f77f3e4282f0 address deduplication, string similarity and fingerprinting (a collection) https://hub.knime.com/mlauber71/spaces/Public/latest/_adress_dedupe_string_similarity?u=mlauber71

KNIME and Harvard University collaborate on advanced geospatial data analysis

A Hands-On Tutorial: Geospatial Analytics with KNIME

https://www.knime.com/blog/tutorial-geospatial-analytics-no-code

Examples: https://hub.knime.com/-/spaces/-/latest/~ieq2yfgeQUshNTi-/

Joint project with Harvard’s CGA will advance spatial data science

https://www.knime.com/blog/harvard-cga-collaboration

Python Code von der AI schreiben lassen

KNIME, ChatGPT and Python
https://medium.com/p/c05709dd3bf5

KNIME, Databases and SQL
https://medium.com/p/273e27c9702a

School of Hive - with KNIME's local Big Data environment (SQL for Big Data)

https://hub.knime.com/s/1q-JwD0cwmuEkWpy

KNIME Snippets (5) — Python Overview

https://medium.com/p/aa4f3a55a768

locate and create /data/ folder with absolute paths
Collect Local Metadata
Aggregation at the occupation level
DB GroupBy
load datainto hivetable
DB Loader
Target_numeric numeric value for calculations
DB Query
1st cretae (empty)hive table
DB Table Structure Creator
Target_SumRename the column
DB Column Renamer
remove H2 table DROP TABLE IF EXISTS "PUBLIC"."census_income";
DB SQL Executor
createnew_table
DB Table Structure Creator
Sort data
DB Sorter
REFRESH #table#
Spark SQL Query
Binary Classification Inspector
clean up
Destroy Spark Context
Target_percentCalculate % zu Target
DB Query
Load data into Spark
Hive to Spark
default.census_income
DB Table Deleter
=> deletes the whole local big data folder/big_data decide in the configuration if you want sub-folders or parent foldersif you encouter any problems, closeKNIME and delete all data from the folder/big_data/
local big data context create
TEST 30
Spark Predictor (Classification)
TRAINING
Spark Gradient Boosted Trees Learner
transfer to tablecensus_income
DB Writer
H2 local SQL database in memory disappears after closing the program can also be stored locally
H2 Connector
create tablecensus_income
DB Table Structure Creator
train.table
Table Reader
Spark Statistics
TRAINING 70
Spark Missing Value
A DB connection in the middle of the workflow
DB Connection Extractor
TRAINING 70
Category to Number
TEST 30
Spark Missing Value (Apply)
empty entry
DB Query
70/30 zufällige Aufteilung
Table Partitioner
Missing Values einbauen
String Manipulation (Multi Column)
70/30 Aufteilung
Spark Partitioning
Everything in one large SQL statement the exact syntax with quotation marks, etc., may vary slightly depending on the SQL database
DB Query Reader
TEST 30
Spark to Table
TEST 30
Missing Value (Apply)
TEST 30
Spark Transformations Applier
TRAINING 70
Spark Category to Number
Python Predictor
Python Script
TEST 30
Category to Number (Apply)
Bring results from the database into KNIME
DB Reader
Python Learner output the tree settings as knime table
Python Script
TRAINING 70
Missing Value
DB Table Selector
random_forest.pkl
Python Script
random_forest.pkl
Python Script
Binary Classification Inspector
Bring results from the database into KNIME
DB Reader
PUBLIC.result_occupation_analysisWrite results to the database
DB Writer (DB Data)

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: A9699459
Created at: 2022-10-11
On NodePit since: 2024-10-28
Last update: 2026-09-19
Created with KNIME version: v5.12.0

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