Qlik Glossary

Data catalog: A component in Data manager and Data load editor that enables you to select and load data from all the datasets to which you have access. It serves as a catalog or repository of available data sources.

Data connection: Used to let data tasks access data sources and external storage and cloud data warehouses used in a data project. Data connections are the links or interfaces that allow data to be transferred or accessed.

Qlik Data Gateway - Data Movement: Allows you to move firewalled data from your enterprise data sources to cloud and on-premises targets over a strictly outbound encrypted and mutually authenticated connection. It facilitates secure data transfer between different environments.

Data Gateway Direct Access: Allows Qlik Sense SaaS applications to securely access firewalled data over a strictly outbound encrypted and mutually authenticated connection. It provides direct access to otherwise restricted data sources.

Data leakage: An undesired phenomenon in machine learning where an algorithm is trained with data that it will use for generating predictions, leading to unrealistically high model performance from memorization rather than actual learning. It can result in biased or overfit models.

Data load editor: A script editor that allows you to build and customize the script that loads data into your app. It provides a way to manipulate and transform data during the loading process.

Data manager: An app component that allows you to load and manage data sources in an app. Data managers are responsible for organizing and maintaining data within the application.

Data mart: Part of your data pipeline containing a subset of data from Storage or Transform data assets, ideally containing summarized data collected for analysis on specific sections or units within an organization. Data marts are specialized databases optimized for specific purposes.

Data model viewer: An app component that allows you to view the structure of the data added to an app and metadata about tables and fields. It provides insights into the organization of data within an application.

Data pipeline: A set of tasks for integrating data in a data project, which can be a simple linear pipeline or a complex one consuming several data sources and generating many outputs. Data pipelines define the flow of data processing within a project.

Data profiling: Displays statistics and information about your data sets. It provides insights into the characteristics and quality of data, helping in data preparation and analysis.

Data project: A workspace where you create your data pipeline using data assets, associated with a data platform used as the target for all outputs. Data projects are where data integration and transformation activities are managed.

Data task: The main unit of work in a data project for moving, storing, transforming data, and creating data marts. Data tasks define specific actions within a data project.

Dataset: Synonymous with table, referring to original source tables, transformed tables, or the fact and dimension tables in a data mart. Datasets are organized collections of data.

Dimension: An entity used in Analytics Services to categorize data in a chart, and in Data Integration, a dataset in a data mart forming part of the star schema. Dimensions provide context for data analysis.

Dynamic views: Allows you to query and view relevant subsets of large datasets from another app in a chart, with the ability to refresh dynamically as selections are made. Dynamic views provide a flexible way to interact with data.

Fact: A table that holds data to be analyzed, working together with dimension tables to store data on the ways in which fact table data can be analyzed. Facts contain the measurable data points in a data model.

Favorites: A section available to all users to add apps, datasets, automations, notes, experiments, and charts from the hub, which are private. Favorites allow users to bookmark and access frequently used items.

Feature (machine learning): A variable in a machine learning problem that can influence the value of the target column, recognized as columns in a dataset within Qlik AutoML. Features are input variables used to make predictions.

Field: Contains values loaded from a data source, corresponding to a column in a table and used to create dimensions and measures in visualizations. Fields represent individual data attributes.

Full load: Refers to the initial replication of data from the data source to the landing in Qlik Cloud Data Integration. It involves transferring all data without incremental updates.

Sheet: A sheet in Qlik Sense is a canvas where you can create a customized view of your data, arranged in a way that tells a story or answers specific questions. Sheets are used for data visualization and analysis.

Sheet objects: Components used to create an interface on a sheet, which can include data visualizations like tables and charts, as well as other objects such as buttons and text objects. Sheet objects are elements placed on sheets for interaction.

Snapshot: Graphical representations of a visualization at a certain point in time, used to create stories. Snapshots capture the state of visualizations for storytelling purposes.

Space data: Governed areas of the Qlik Cloud tenant used to create and store data projects, manage new data connections, and access Data Movement gateways. Space data is where data integration and management activities occur.

Space managed: Controlled spaces used to share apps with a limited group of users. Managed spaces provide a controlled environment for collaborative app development.

Space personal: A private space belonging to users where they can develop apps. Personal spaces are individual workspaces for app development.

Space shared: Areas where apps and data sources can be shared with other users for collaborative development. Shared spaces facilitate teamwork and sharing of resources.

Storage: Part of the data pipeline containing ready-to-consume datasets in Qlik Cloud from data copied from the landing zone. Storage is where data is stored and made available for analysis.

Story: A tool that allows the sharing of data insights and discoveries made in an app with other users, combining reporting, presentation, and exploratory analysis. Stories enable data-driven narratives.

Subscription: Reports that let you schedule recurring emails containing a PDF of selected sheets or charts. Subscriptions automate the delivery of data insights to users.

Synthetic key: A composite key between two tables in the data model, created when two or more tables have common fields, which may need to be reviewed if it results in a data model error. Synthetic keys are generated to link related tables.

Tables: ODS, HDS, and Change: Types of tables in a data project such as the Current table (ODS), the Prior table (HDS), and the Change table, serving different purposes within the data architecture. These tables are used to manage historical data changes.

Target: The destination or endpoint where data is intended to be transferred, stored, or loaded, in data movement, migration, or synchronization processes. Targets define where data should be placed.

Tenant: The deployment of Qlik Cloud, holding items such as users, apps, and spaces. Tenants represent the individual environments within Qlik Cloud.

Training dataset: The dataset used to train a machine learning model in Qlik AutoML, designed to learn patterns and make predictions on new data. Training datasets are used to teach models.

Transform: A task that allows creation of reusable data transformations in a data pipeline with rules and custom SQL. Transformations modify data to prepare it for analysis or storage.

Type 1 - Operational Data Store (ODS): In ODS datasets, new information overwrites the original information, i.e., no historical data is kept. Type 1 ODS tables update existing data with new information.

Type 2 - Historical Data Store (HDS): In HDS datasets, a new record representing the new information is added to the table, including both the original and the new record. Type 2 HDS tables maintain historical data.

Variable: A variable in Qlik Sense is a value container which can store a static or a calculated value, like a numeric or an alphanumeric value. Variables hold values that can be used in expressions and calculations.

Views: Virtual representations of physical datasets in data projects, which can query and fetch relevant data dynamically without occupying significant disk space. Views provide efficient data access.

Visualization: Charts, extensions, and other objects that help visualize data for exploration on a sheet. Visualizations are used to represent data graphically.

Vocabulary: A business logic feature in Qlik that allows the addition of synonyms and custom analyses to Insight Advisor Search and Chat. Vocabulary customization enhances business analysis capabilities.

Sheet view: A view in Qlik Sense representing a canvas where users can arrange data visualizations and other objects to tell a story or answer questions. Sheet views are used for creating customized data presentations.

Working in spaces in Qlik Cloud Data Integration: Refers to the governed areas in Qlik Cloud where users can create and store data projects, manage data connections, and access Data Movement gateways. Working in spaces involves data management and integration activities.

Working in managed spaces: Involves utilizing tightly controlled spaces to share applications with a select group of users. Managed spaces ensure controlled access and collaboration.

Working in personal spaces: Related to a user's private space where they can develop applications independently. Personal spaces provide individual workspaces for app development.

Working in shared spaces: Pertains to areas in Qlik Cloud where users can collaborate and share applications and data sources. Shared spaces facilitate teamwork and sharing of resources.

Storing datasets: Involves keeping datasets up-to-date in the Qlik Cloud data pipeline without manual intervention after data is transferred from the landing zone. Data storage ensures data availability for analysis.

Using data storytelling: A feature that enables users to share insights and discoveries from data analysis through a narrative combining reporting, presentation, and exploratory analysis. Data storytelling enhances data communication.

Scheduling reports with subscriptions: Allows users to configure and send recurring reports via email containing selected sheets or charts. Subscriptions automate report delivery.

Synthetic keys: Composite keys in the data model created when common fields between two or more tables exist, which may require review if data model errors are present. Synthetic keys are generated to link related tables.

Machine learning concepts: Encompass general principles of machine learning, such as targets for predictions in Qlik AutoML and the concept of data movement to a target destination. Machine learning concepts provide the foundation for predictive analytics.

Working with visualizations: Encompasses creating and interacting with charts, extensions, and other objects on a sheet to explore and understand data patterns. Working with visualizations is a key aspect of data analysis.

Business logic vocabulary: A feature in Qlik that allows adding synonyms and custom analyses to Insight Advisor Search and Chat, enhancing business analysis capabilities. Vocabulary customization improves data understanding and search capabilities.