Qlik AI

Overview

Qlik AI refers to the artificial intelligence and machine learning capabilities embedded within the broader Qlik platform, which encompasses data integration, data analytics, and automation. The company, founded in 1993, offers a suite of products designed to assist enterprises with data management and analysis, particularly emphasizing real-time data processing and self-service business intelligence (BI). Qlik's approach integrates data replication and transformation (Qlik Replicate), cloud analytics (Qlik Cloud, Qlik Sense), and automated machine learning (Qlik AutoML, Qlik Staige) into a unified environment.

The platform is designed for a range of users, from data engineers building real-time pipelines to business analysts creating interactive dashboards, and business users seeking predictive insights without extensive data science expertise. Qlik Sense, a core product, provides interactive dashboards and visualizations, enabling self-service exploration of data. Qlik Replicate offers change data capture (CDC) for real-time data movement, supporting data warehousing and operational analytics. Qlik AutoML simplifies the process of building and deploying machine learning models, catering to business users by automating model selection, feature engineering, and deployment.

Qlik's focus on real-time data integration distinguishes it from some traditional BI tools. This capability is critical for use cases requiring immediate insights from constantly updating data sources, such as financial trading, supply chain optimization, or customer interaction analytics. The platform also supports embedded analytics, allowing organizations to integrate Qlik's visualization and analytical capabilities directly into their operational applications and websites via APIs and SDKs. This allows for context-aware analytics within existing workflows, reducing the need for users to switch between applications to gain insights. The Qlik platform is available as a SaaS offering through Qlik Cloud, as well as on-premise deployments, providing deployment flexibility for various enterprise requirements.

Key features

• Data Integration and Replication (Qlik Replicate): Facilitates real-time, high-volume data movement across heterogeneous data sources and targets, including cloud platforms, data warehouses, and streaming systems, using change data capture (CDC) technology.
• Self-Service Analytics (Qlik Sense): Offers interactive dashboards, data visualization tools, and associative exploration capabilities, allowing business users to explore data and discover insights independently without relying on IT.
• Automated Machine Learning (Qlik AutoML): Automates key steps in the machine learning workflow, including data preparation, model selection, hyperparameter tuning, and deployment. It is designed for business users to generate predictive models without coding.
• Embedded Analytics: Provides APIs and SDKs to integrate Qlik's analytical capabilities, such as charts, dashboards, and data exploration, directly into third-party applications or custom portals.
• Cloud-Native Platform (Qlik Cloud): A fully managed, multi-cloud platform that delivers Qlik's data integration and analytics services, offering scalability, elasticity, and global accessibility.
• Data Catalog and Governance: Tools for discovering, cataloging, and governing data assets, ensuring data quality, lineage, and compliance across the organization.
• Natural Language Interaction: Allows users to ask questions in natural language and receive immediate answers or visualizations, enhancing accessibility for non-technical users.

Pricing

Qlik offers tiered pricing based on user count and required features, with specific plans for business-level and enterprise-level deployments.

For detailed pricing information and to request a quote for enterprise solutions, refer to the official Qlik pricing page.

Common integrations

Qlik's platform is designed to integrate with a range of enterprise data sources and applications. Its data integration capabilities extend to various databases, cloud platforms, and enterprise applications.

• Cloud Data Warehouses: Integration with platforms like Amazon Redshift, Google BigQuery, and Snowflake for data loading and analysis.
• Relational Databases: Connectivity to traditional databases such as Oracle, SQL Server, MySQL, and PostgreSQL for data extraction and replication.
• Enterprise Applications: Connectors for business applications like Salesforce, SAP, and various ERP systems to extract operational data.
• Data Lakes: Integration with data lake solutions built on platforms like Amazon S3, Azure Data Lake Storage, and HDFS for storing and processing large volumes of raw data.
• Streaming Data Sources: Support for real-time data ingestion from Kafka and other streaming platforms.
• Programming Languages/APIs: Qlik provides various APIs, including the Qlik Engine API and Qlik Sense APIs, which allow programmatic interaction and embedding of analytics into custom applications. SDKs are available for languages such as JavaScript and .NET, documented in the Qlik developer resources.

Alternatives

• Tableau: A widely used data visualization and business intelligence tool known for its intuitive drag-and-drop interface and strong visual analytics capabilities.
• Microsoft Power BI: A business intelligence service from Microsoft that offers interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
• ThoughtSpot: Focuses on search and AI-driven analytics, enabling users to get insights from data by asking questions in natural language.

Getting started

To begin with Qlik Sense, a common starting point is to connect to a data source and create your first application. The following example demonstrates a basic flow for loading data from a CSV file into a Qlik Sense application using its scripting capabilities. This script is typically entered in the Data Load Editor within a Qlik Sense application.

// Connect to a folder containing data files
LIB CONNECT TO 'DataFiles';

// Load data from a CSV file named 'SalesData.csv'
// Assuming 'SalesData.csv' has columns like ProductID, SalesAmount, OrderDate
SalesTable:
LOAD
    ProductID,
    SalesAmount,
    OrderDate
FROM [lib://DataFiles/SalesData.csv]
(txt, utf8, embedded labels, delimiter ',');

// Example of creating a calculated field (e.g., Year from OrderDate)
SalesTable:
LOAD
    *, // Load all existing fields
    Year(OrderDate) as SalesYear
Resident SalesTable;

// Drop the temporary table if necessary (optional, good practice for complex scripts)
// DROP Table SalesTable_temp;

// Once data is loaded, you can create visualizations in the Qlik Sense sheet editor.

This script first establishes a connection to a folder (DataFiles) where your CSV is located. It then loads data from SalesData.csv into a table called SalesTable. A second LOAD statement is used to add a derived field, SalesYear, from the OrderDate. After execution, the data becomes available in the Qlik Sense app for creating visualizations and dashboards. For more advanced integration and embedding, developers can utilize the Qlik Engine API documentation.