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Download KNIME 5.3.1 Date released: 27 Aug 2024 (7 months ago) Download KNIME 4.7.8 Date released: 03 Jan 2024 (one year ago) Download KNIME 4.7.7 Date released: 17 Sep 2023 (one year ago) Download KNIME 4.7.6 Date released: 19 Aug 2023 (one year ago) Download KNIME 4.7.5 Date released: 09 Jul 2023 (one year ago) Download KNIME 4.7.4 Date released: 21 Jun 2023 (one year ago) Download KNIME 4.7.3 Date released: 30 May 2023 (one year ago) Download KNIME 4.7.2 Date released: 03 May 2023 (one year ago) Download KNIME 4.7.1 Date released: 10 Feb 2023 (2 years ago) Download KNIME 4.7.0 Date released: 06 Jan 2023 (2 years ago) Download KNIME 4.5.2 Date released: 28 Mar 2022 (3 years ago) Download KNIME 4.5.1 Date released: 22 Jan 2022 (3 years ago) Download KNIME 4.5.0 Date released: 07 Dec 2021 (3 years ago) Download KNIME 4.4.2 Date released: 26 Oct 2021 (3 years ago) Download KNIME 4.4.1 Date released: 30 Aug 2021 (4 years ago) Download KNIME 4.4.0 Date released: 01 Jul 2021 (4 years ago) Download KNIME 4.3.3 Date released: 02 Jun 2021 (4 years ago) Download KNIME 4.3.2 Date released: 09 Mar 2021 (4 years ago) Download KNIME 4.3.1 Date released: 01 Feb 2021 (4 years ago) Download KNIME 4.3.0 Date released: 08 Dec 2020 (4 years ago)

This course builds on the [L1-AP] Data Literacy with KNIME Analytics Platform - Basics by introducing advanced concepts for building and automating workflows with KNIME Analytics Platform Version 5. This course covers topics for controlling node settings and automating workflow execution. You will learn concepts such as flow variables, loops, switches, and how to catch errors. In addition, you will learn how to handle date and time data, how to create advanced dashboards, and how to process data within a database. Moreover, this course introduces additional tools for reporting. You will learn how to style and update Excel spreadsheets using the Continental Nodes. Moreover, you will learn how to generate reports using the KNIME Reporting extension.This is an instructor-led course consisting of five, 75 minutes online sessions run by our data scientists. Each session has an exercise for you to complete at home, and we will go through the solution at the start of the following session. The course concludes with a 15-30 minutes wrap up session.Session 1: Flow Variables & Components Session 2: Workflow Control and InvocationSession 3: Date&Time, Databases, REST Services, Python & R IntegrationSession 4: Excel Styling, KNIME Reporting ExtensionSession 5: Review of the Last Exercises and Q&AFAQ

IntroductionKNIME Analytics Platform is open source software for creating data scienceapplications and services. Intuitive, open, and continuously integrating newdevelopments, KNIME makes understanding data and designing data scienceworkflows and reusable components accessible to everyone.With KNIME Analytics Platform, you can create visual workflows with anintuitive, drag and drop style graphical interface, without the need forcoding.In this quickstart guide we’ll take you through the KNIME Workbench and show youhow you can build your first workflow. Most of your questions will probablyarise as soon as you start with a real project. In this situation, you’ll find alot of answers in the KNIME Workbench Guide,and in the E-Learning Course on our website.But don’t get stuck in the guides. Feel free to contact us and the widecommunity of KNIME Analytics Platform users, too, at theKNIME Forum. Another way of getting answersto your data science questions is to explore the nodes and workflows available on theKNIME Hub. We are happy to help you there!Start KNIME Analytics PlatformIf you haven’t yet installed KNIME Analytics Platform, you can do that on thisdownload page. For a step by step introduction,follow thisInstallation Guide.Start KNIME Analytics Platform and when the KNIME Analytics Platform Launcherwindow appears, define the KNIME workspace here as shown in Figure 1.Figure 1. KNIME Analytics Platform LauncherThe KNIME workspace is a folder on your local computer to store your KNIMEworkflows, node settings, and data produced by the workflow. The workflows anddata stored in your workspace are available through the KNIME Explorer in theupper left corner of the KNIME Workbench.After selecting a folder as the KNIME workspace for your project, clickLaunch. When in use, the KNIME Analytics Platform user interface - the KNIMEWorkbench - looks like the screenshot shown in Figure 2.Figure 2. KNIME WorkbenchThe KNIME Workbench is made up of the following components:KNIME Explorer: Overview of the available workflows and workflow groups inthe active KNIME workspaces, i.e. your local workspace, KNIME Servers, and yourpersonal KNIME Hub space.Workflow Coach: Lists node recommendations based on the workflows built bythe wide community of KNIME users. It is inactive if you don’t allow KNIME tocollect your usage statistics.Node Repository: All nodes available in core KNIME Analytics Platform and inthe extensions you have installed are listed here. The nodes are organized bycategories but you can also use the search box on the top of the node repositoryto find nodes.Workflow Editor: Canvas for editing the currently active workflow.Description: Description of the currently active workflow, or

Aggregated data back to Databricks, let’s say in Parquet format, add the Spark to Parquet node. The node has two input ports, connect the DBFS (blue) port to the DBFS port of the Create Databricks Environment node, and the second port to the Spark GroupBy node. To configure the Spark to Parquet node:1. Under Target folder, provide the path on DBFS to the folder where you want the Parquet file(s) to be created.2.Target name is the name of the folder that will be created in which then the Parquet file(s) will be stored.3. If you check the option Overwrite result partition count, you can control the number of the output files. However, this option is strongly not recommended as this might lead to performance issues.4. Under the Partitions tab you can define whether to partition the data based on specific column(s).KNIME supports reading various file formats into Spark, such as Parquet or ORC, and vice versa. The nodes are available under Tools & Services > Apache Spark > IO in the node repository.It is possible to import Parquet files directly into a KNIME table. Since our large dataset has now been reduced a lot by aggregation, we can safely import them into KNIME table without worrying about performance issues. To read our aggregated data from Parquet back into KNIME, let’s use the Parquet Reader node. The configuration window is simple, enter the DBFS path where the parquet file resides. Under the Type Mapping tab, you can control the mapping from Parquet data types to KNIME types.Now that our data is in a KNIME table, we can create some visualization. In this case, we do further simple processing with sorting and filtering to get the 10 airports with the highest delay. The result is visualized in a Bar Chart.Figure 12. 10 airports with highest delay visualized in a Bar Chart (click to enlarge)Now we would like to upload the data back to Databricks in Parquet format, as well as write them to a new table in the Databricks database. The Parquet Writer node writes the input KNIME table into a Parquet file. To connect to DBFS, please connect the DBFS (blue) port to the DBFS port of the Create Databricks Environment node. In the configuration window, enter the location on DBFS where the Parquet file will be written to. Under the Type Mapping tab, you can control the mapping from KNIME types to Parquet data types.To create a new table, add the DB Table Creator node and connect the DB (red) port to the DB port of the Create Databricks Environment node. In the configuration window, enter the schema and the table name. Be careful when using special characters in the table name, e.g underscore (_) is not supported. Append the DB Loader node to the DB Table Creator with the KNIME table you want to load, and connect the DB (red) port and the DBFS (blue) port to the DB port and DBFS port of the Create Databricks Environment node

Databricks DeltaDatabricks Delta offers a lot of additional features to improve data reliability, such as time travel. Time travel is a data versioning capability allowing you to query an older snapshot of a Delta table (rollback).To access the version history in a Delta table on the Databricks web UI:1. Navigate to the Data tab in the left pane.2. Select the database and the Delta table name.3. The metadata and a preview of the table will be displayed. If the table is indeed a Delta table, it will have an additional History tab beside the Details tab (see Figure below).4. Under the History tab, you can see the versioning list of the table, along with the timestamps, operation types, and other information.Figure 15. Delta table versioning historyIn KNIME, accessing older versions of a Delta table is very simple:1. Use a DB Table Selector node. Connect the input port with the DB port (red) of the Create Databricks Environment node.2. In the configuration window, enter the schema and the Delta table name. Then enable the Custom query checkbox. A text area will appear where you can write your own SQL statement.a) To access older versions using version number, enter the following SQL statement:Where is the version of the table you want to access. Check Figure 13 to see an example of a version number.b) To access older versions using timestamps, enter the following SQL statement where is the timestamp format. To see the supported timestamp format, please check the Databricks documentation3. Execute the node. Then right click on the node, select DB Data, and Cache no. of rows to view the table.Figure 16. Configuration window of the DB Table Selector nodeWrapping upWe hope you found this guide on how to connect and interact with Databricks from within KNIME Analytics platform useful.by Andisa Dewi (KNIME)Summary of the resources mentioned in the articleMore blog posts about KNIME and Cloud Connectivity

This blog post is an introduction of how to use KNIME on Databricks. It's written as a guide, showing you how to connect to a Databricks cluster within KNIME Analytics Platform, as well as looking at several ways to access data from Databricks and upload them back to Databricks.A Guide in 5 SectionsThis "how-to" is divided into the following sections:How to connect to Databricks from KNIMEHow to connect to a Databricks Cluster from KNIMEHow to connect to a Databricks File System from KNIMEReading and Writing Data in DatabricksDatabricks DeltaWhat is Databricks?Databricks is a cloud-based data analytics tool for big data management and large-scale data processing. Developed by the same group behind Apache Spark, the cloud platform is built around Spark, allowing a wide variety of tasks from processing massive amounts of data, building data pipelines across storage file systems, to building machine learning models on a distributed system, all under a unified analytics platform. One advantage of Databricks is the ability to automatically split workload across various machines with on-demand autoscaling.The KNIME Databricks IntegrationKNIME Analytics Platform includes a set of nodes to support Databricks, which is available from version 4.1. This set of nodes is called the KNIME Databricks Integration and enables you to connect to your Databricks cluster running on Microsoft Azure or Amazon AWS cluster. You can access and download the KNIME Databricks Integration from the KNIME Hub.Note: This guide is explained using the paid version of Databricks. The good news is: Databricks also offers a free community edition of Databricks for testing and education purposes, with access to 6 GB clusters, a cluster manager, a notebook environment, and other limited services. If you are using the community edition, you can still follow this guide without any problem.Connect to DatabricksAdd the Databricks JDBC driver to KNIMETo connect to Databricks in KNIME Analytics Platform, first you have to add the Databricks JDBC driver to KNIME with the following steps.1. Download the latest version of the Databricks Simba JDBC driver at the official website. You have to register to be able to download any Databricks drivers. After registering, you will be redirected to the download page with several download links, mostly for ODBC drivers. Download the JDBC Drivers link located at the bottom of the page.Note: If you’re using a Chrome-based web browser and the registration somehow doesn’t work, try to use another web browser, such as Firefox.2. Unzip the compressed file and save it to a folder on your hard disk. Inside the folder, there is another compressed file, unzip this one as well. Inside, you will find a .jar file which is your JDBC driver file.Note: Sometimes you will find several zip files inside the first folder, each file refers to the version of JDBC that is supported by the JDBC driver. KNIME currently supports JDBC drivers that are JDBC 4.1 or JDBC 4.2 compliant.3. Add the new driver to the list of database drivers:In KNIME Analytics Platform, go to File > Preferences > KNIME > Databases and