The Advanced QlikView Developer Bootcamp

The course presents a consistent, challenge-driven approach to mastering QlikView, with downloadable cube files, GitHub access, and scripted solutions for incremental and full reload scenarios, organized by tabs.

Survey fundamental data modeling concepts and terminology, then explore techniques such as the snowflake schema, olap perspectives, and click view methods with link tables, mixed granularity schema, and data islands.

Explore information density and subset ratio to assess field population across fact and dimension tables, and distinguish primary keys from perfect keys for reliable data models.

Examine synthetic keys and tables, then use composite keys to avoid them; understand circular references and join behavior to model national teams, clubs, and country cities.

Define the relationship between tables to model data in QlikView, enabling bidirectional filtering and cross-table questions, with star or snowflake patterns, concatenated keys, and testing anchored to business requirements.

Explore types of data models, from traditional modeling and single-table schemas to snowflake schemas, then examine Stotsky, clickthrough specific modelling with concatenated factor, mixed granularity schemas, and league tables.

Learn to build a star schema by loading a fact table and four dimensions (calendar, customers, employees, products), define keys, rename fields for clean joins, and validate information density.

Understand how snowflake schema extends the Stotsky mind by normalizing dimension tables, enabling multi-step connections and parent-child hierarchies to reduce redundancy.

Demonstrates building a snowflake schema in QlikView by adding and linking dimension tables such as product description, supplier, and employee address to the fact table.

Demonstrate how to build and concatenate fact tables from store and web sales, align fields across a star and snowflake schema, and use flags to track load type.

Explore how to simplify a snowflake schema data model that uses a fact table with related tables such as customer, employee, employee address, teams, master calendar, and products.

Learn to simplify a data model by converting a snowflake schema model into a Stotsky model, using left joins and mapping tables to consolidate supplier, product description, and employee data.

Learn how to use apply map with mapping load to derive supplier names from a mapping table, including defaults, expressions, nesting, and handling missing values with dollar sign expansion.

Apply a mapping load to bring employee city, country, and postcode from employee address into the employee table, ensuring postcode is a string and only one mapping load is used.

Implement a second challenge by renaming three fields with mapping load, converting employee name to team MPLX name from input to output using a liberty file to explore mapping nodes.

Explore how QlikView script execution flows left to right and top to bottom, grouping load and select statements into blocks, with preceding load evaluating inputs and tabs as logical placeholders.

Learn how resident load works in QlikView, including why it requires a preloaded table, how multiple instances occur, and when to drop or avoid auto concatenation, or use order clause.

Explore preceding load in QlikView, compare it to resident load, and learn how it fetches from cache using a single table, reduces memory footprint, supports aliases, and forbids crosstabs joins.

Explain the difference between where and having clauses; where filters rows before grouping, having filters groups after, using grouping by sales order to compute total price and apply thresholds.

Learn how to emulate a having clause in QlikView by using a preceding load with group by and aggregation, filtering on an aggregated line sales amount.

Convert a synthetic-key load into two tables using wildcard loads and preceding or resident load, and create the percentage ki required date and a Phelim field.

Learn how synthetic keys slow down QlikView data models and explore three remedies: composite keys from concatenated common fields, renaming ambiguous fields, and creating linkable fields by using link tables.

Learn to use linkable tables to replace synthetic ones by moving common fields into a composite key, labeled with a percent prefix, and connect two tables via a link cable.

Link the inventory transaction table to a common calendar dimension via a synthetic link table and composite key, enabling sales and purchases to align through the calendar.

Learn how incremental reload helps manage large data sets, such as tables with 10 million rows. Explore three types: insure, incertain update, and insert update and delete.

Learn how incremental reload starts with a full reload and then adds new or updated rows, purging deleted records and concatenating with existing data, guided by a primary key.

Learn incremental reload architecture by loading new data. Identify new records with the primary key, load old data from the cubed file, ensure same schema, concatenate, and store for reloads.

Master incremental reload scripting in QlikView to load only new records, following a nine-step blueprint that uses a stored primary key for subsequent reloads and full reload fallbacks.

Learn how to implement incremental reload in QlikView by checking covid existence, performing incremental preload, then an incremental reload, and concatenating data with the full reload into covid.

Learn how to implement incremental reloads in QlikView by extracting the max key, loading only new inserts, concatenating them with existing data, and storing the result to the cube.

Extend the incremental reload to handle both insert and update operations by formatting the date field as a date and replacing outdated records with updated ones using not exists logic.

Create an incremental load for the orders table that handles new and updated records using the primary key and order date, with key renaming and two updates for validation.

Master incremental load in QlikView by creating scripts for inserts and updates on the orders table using a max key and order date, then validating with full and incremental reloads.

Walk through the solution for incremental load—including handling deleted records, validating with delete operations on the orders table, and ensuring removed data no longer exists in the cube.

Explore associative joins versus explicit ANSI style and natural joins in QlikView, highlighting default outer joins, ANSI 92 inner joins, and practical examples with colors and fruit data.

Explore how outer joins merge colors and food tables, showing how matching and nonmatching rows appear and how null values emerge when no data exists.

Master left join by stacking the left-hand side table with matching records, producing a result from the left table and ignoring non-matching right-hand side rows.

Explore right join fundamentals by combining fruit and colors tables to yield three common rows, and learn that its effect mirrors a left join when tables are swapped.

Introduces chapter nine goals and fundamentals of master calendar, explains creating a better script with the field value function to avoid scanning entire table, and ends with a practice challenge.

Compare less and more efficient master calendar scripts, extract min and max dates, generate a date table, and build a master calendar with preceding load for performance.

Master calendar challenge: replace interpretation function with a formatting function, use NUM and percent key order date field, and ensure the calendar reload shows 805 rows and 45 fields.

Extract min and max dates, generate calendar, and create master calendar; use the filled value function to scan unique field values (symbol table) with preceding, interpretation, and permitting functions.

Explore chapter 10 goals, covering folder structure, script layout, config components, environment variables, source control options, and variable naming conventions for QlikView development.

Leverage source control to version QlikView components by committing the QlikView script, objects without data, config files, metadata, and external scripts, with clear self-documenting commit messages.

Apply consistent QlikView naming conventions by prefixing variables with v and expressions with xp, name colors clearly, and designate navigation and temp variables to stay organized.