KNIME Analytics Platform for Data Blending Training Course

Course overview

Data teams often spend more time reconciling spreadsheets, database extracts, CRM exports and operational files than analysing them. Repeated manual joins, inconsistent customer identifiers, undocumented transformations and one-off SQL scripts create reporting delays and make results difficult to audit. This five-day KNIME Analytics Platform for Data Blending course equips professionals to build repeatable, visual data workflows that combine disparate sources, validate quality and produce analysis-ready datasets without relying on fragile manual processes.

Participants learn to use KNIME Analytics Platform to connect to files and databases, profile incoming data, standardise formats, manage missing values, de-duplicate records and blend datasets through joins, concatenations, rule-based transformations and aggregation. The course covers core KNIME nodes, flow variables, component design, database pushdown, error handling and workflow documentation. Participants also learn when to use visual nodes, SQL queries and reusable components to create transparent pipelines that colleagues can inspect, rerun and maintain.

Delivery combines instructor demonstrations with guided builds based on realistic sales, customer, product and transaction data. Each participant develops a documented KNIME workflow that ingests multiple sources, applies data-quality controls, creates a conformed analytical dataset and exports validated results. The final workflow includes reusable components, annotations, quality checks and a handover-ready process map, giving participants a practical template for improving a live reporting or analytics process in their organisation.

The course is designed for analysts and technical professionals who already work with data and need a governed, repeatable way to prepare it for reporting, dashboards, modelling or operational decision-making. It is equally valuable for managers seeking to reduce spreadsheet dependency, shorten data-preparation cycles and improve confidence in the numbers used by their teams.

Course objectives

By the end of this course, participants will be able to:

• Build KNIME workflows that ingest CSV, Excel, database and delimited-text data sources
• Profile datasets using KNIME statistics, domain inspection and data-quality checks
• Standardise dates, text fields, identifiers and missing values with transformation nodes
• Blend datasets using Joiner, Concatenate, Column Appender and GroupBy nodes
• Create reusable KNIME components with input and output ports, configuration and documentation
• Apply flow variables and configuration nodes to parameterise repeatable data-preparation workflows
• Push filtering, joins and aggregations to SQL databases using KNIME database nodes
• Produce a documented, validated analytical dataset and handover-ready KNIME workflow

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to combine operational data into reliable reporting and analysis datasets
• Business Intelligence Analysts — who prepare source data for dashboards and semantic models
• Data Engineers — who need a visual tool for rapid, traceable data-preparation workflows
• Reporting Analysts — who replace recurring spreadsheet consolidation with repeatable processes
• Database Analysts — who blend SQL-based data with files, extracts and business-owned data sources
• Analytics Managers — who need to standardise team data-preparation practices and improve auditability

Requirements and prerequisites

Participants should be comfortable working with tabular data and understand rows, columns, data types, filters, joins and basic aggregations. Experience using Excel, SQL, Power BI, Tableau or another reporting or analytics tool is helpful, as is familiarity with CSV files and relational database tables. No prior KNIME experience is required, and participants do not need to be programmers or data scientists. SQL is used in examples involving databases, but the course explains how KNIME nodes can perform many transformations visually. Participants should be prepared to work through multi-step data-preparation exercises on a laptop.

Training methodology

The course is delivered through instructor-led demonstrations followed by hands-on KNIME builds on supplied multi-source business datasets. Participants configure nodes, inspect intermediate tables, diagnose join failures and compare in-memory processing with database execution. Short case discussions focus on data lineage, quality controls and maintainable workflow design. Group reviews are used to assess alternative blending approaches and documentation choices. On the final day, participants adapt their workflow to a realistic business requirement and create an application plan for a reporting, dashboard or data-preparation process in their own environment.

Course outline

Day 1: KNIME workflow foundations and source connection

• KNIME Analytics Platform interface, workflow editor and node repository
• Workflow execution states, node configuration and table views
• Reading CSV, Excel and delimited-text files with file reader nodes
• Connecting to relational databases through JDBC and KNIME database nodes
• Data types, column domains and metadata inspection
• Workflow annotations, metanodes and basic execution documentation
• Managing paths, file locations and data-source portability

Workshop: Build an intake workflow that reads sales, customer and product files, inspects their schemas and produces a documented source inventory.

Day 2: Data profiling, cleansing and standardisation

• Exploratory profiling with Statistics, Data Explorer and value inspection nodes
• Missing-value treatment using Missing Value and Rule Engine nodes
• String cleaning with String Manipulation, regex and case normalisation
• Date parsing, date-time conversion and period derivation
• Duplicate detection using grouping, row filtering and key analysis
• Column management with Column Filter, Column Renamer and Column Resorter
• Data-quality rules, exception outputs and validation checkpoints

Workshop: Clean a flawed customer extract and produce a data-quality exception table covering missing identifiers, duplicates and invalid dates.

Day 3: Data blending and analytical dataset design

• Join types and key selection with the Joiner node
• Unmatched-record analysis and join validation techniques
• Appending compatible datasets with Concatenate and Column Appender
• Lookup enrichment and rule-based category mapping
• Aggregation with GroupBy, Pivoting and value-count calculations
• Row-level filtering, sampling and conditional transformation logic
• Designing conformed dimensions and analysis-ready fact tables

Workshop: Blend customer, transaction and product data into a validated sales-analysis table and reconcile record counts at each stage.

Day 4: Reusable, scalable and database-aware workflows

• Reusable components, configuration dialogs and component documentation
• Flow variables and variable-controlled node settings
• Looping patterns for repeated files, periods and segmented processing
• Try-Catch patterns and controlled failure handling
• Database Reader, Database Writer and Database SQL Executor nodes
• SQL pushdown, in-database processing and performance considerations
• Workflow organisation, naming conventions and maintainability standards

Workshop: Convert a monthly file-blending process into a parameterised component that writes validated outputs to a database table.

Day 5: Operationalising data blending workflows

• Workflow testing with row counts, reconciliation and expected-value checks
• Data lineage, transformation notes and business-rule documentation
• Exporting outputs to Excel, CSV and database destinations
• KNIME Hub for workflow sharing, versioning and collaboration
• Access considerations for credentials, connections and sensitive data
• Workflow review criteria for peer handover and operational support
• Application planning for reporting, dashboard and analytics use cases

Workshop: Complete and present a handover-ready KNIME workflow that blends multiple sources, documents controls and delivers an analysis-ready dataset.

Tools & standards covered

KNIME Analytics Platform, KNIME Hub, Microsoft Excel, PostgreSQL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions