Qlik Sense

Qlik Sense
Name	Qlik Sense
Developer	Qlik
Released	2014
Latest release	(varies)
Operating system	Cross-platform
Genre	Business intelligence, data visualization, analytics
License	Proprietary

Qlik Sense is a commercial business intelligence and data visualization platform designed for self-service analytics, interactive dashboards, and guided analytics. It competes with platforms aimed at enterprise analytics and data discovery and is used across industries for reporting, exploration, and embedded analytics. The product evolved alongside shifting trends in cloud computing, big data, and user-driven analytics.

Overview

Qlik Sense operates as an associative analytics engine for exploratory analysis, blending datasets and enabling users to create interactive visualizations and custom applications. It emphasizes in-memory processing, drag-and-drop authoring, and responsive design for web, mobile, and embedded contexts. The platform is used by corporations, government agencies, and research institutions to support decision-making in contexts ranging from finance and healthcare to retail and manufacturing.

History and Development

Development began after the founding of Qlik in the 1990s, building on earlier technologies and market trends established by firms such as Microsoft with Power BI, Tableau Software, SAP SE with BusinessObjects, IBM with Cognos, and Oracle Corporation with Oracle BI. Early milestones include the introduction of an associative engine influenced by in-memory innovations from vendors like TIBCO Software and academic work from institutions such as MIT. Strategic shifts in the 2010s responded to cloud initiatives led by Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Corporate acquisitions and partnerships with firms like Accenture, Deloitte, Capgemini, and PwC shaped go-to-market and integration strategies. Industry recognition placed the product alongside analytics offerings from SAS Institute, MicroStrategy, Looker (company), and open-source projects that grew in prominence through communities around Apache Hadoop, Apache Spark, and PostgreSQL.

Architecture and Components

The platform is built on a multi-tier architecture combining an in-memory associative engine, a web-based visualization layer, data connectors, and a management console. Components interact with enterprise systems including Salesforce, SAP ERP, Oracle Database, Microsoft SQL Server, Teradata, and cloud warehouses such as Snowflake (company). Security and governance integrate with identity providers and directory services like Microsoft Active Directory, Okta, and Ping Identity. Scaling strategies reference containerization platforms and orchestration tools including Docker and Kubernetes, as well as virtualization stacks from VMware and public cloud infrastructure provided by Amazon EC2 and Google Compute Engine.

Features and Functionality

Core features include associative data indexing, visual analytics, storytelling, mashups, and augmented analytics capabilities such as natural language search and AI-assisted insights. The developer and authoring tools support scripting, expression languages, and APIs for customization, comparable to extensibility models used by Salesforce Platform, SAP Fiori, and Microsoft Power Platform. Visualization types encompass charts, heatmaps, scatter plots, and geographic maps often integrated with mapping services like Esri and Google Maps. Data preparation features connect to ETL and ELT patterns seen in tools from Informatica, Talend, Alteryx, and Fivetran.

Deployment and Administration

Administrators manage deployments via management consoles and command-line tools, applying policies for access control, resource allocation, auditing, and monitoring. Typical deployment models include on-premises clusters, private cloud, and SaaS offerings hosted on AWS, Azure, and Google Cloud Platform. Operational practices align with DevOps and site reliability engineering approaches advocated by GitHub, Atlassian, and HashiCorp for CI/CD, configuration management, and infrastructure as code. High-availability patterns mirror strategies used in enterprise middleware such as Apache Kafka and Redis for caching and messaging.

Licensing and Editions

The product is offered under commercial licensing with tiers that may include enterprise, professional, and cloud editions, similar to packaging strategies used by Adobe Systems, Oracle Corporation, and IBM. Licensing models consider named-user, capacity-based, and subscription pricing, reflecting market practices seen with Microsoft 365, Salesforce CRM, and Amazon Web Services subscription services. Enterprise agreements, partner programs, and managed service frameworks resemble arrangements common to Accenture, Cognizant, and Capgemini.

Integration and Extensions

Integration options include REST APIs, SDKs, and embed frameworks for integrating analytics into portals, products, and mobile apps, paralleling approaches from Tableau, Looker (company), and Power BI. Ecosystem extensions enable connectors, mashups, and custom visualizations developed with JavaScript frameworks such as React (JavaScript library), Angular (application platform), and Vue.js. Data engineering and streaming integrations align with platforms like Apache Kafka, Apache Spark, Databricks, and ETL providers including Talend and Informatica. Third-party marketplaces and partner ecosystems mirror channels maintained by Microsoft AppSource and Salesforce AppExchange.

Category:Business intelligence software