Qlik Data Catalog

Qlik Data Catalog

Qlik Data Catalog: Qlik Data Catalog is a powerful module designed to simplify and accelerate the data discovery and preparation process. It provides a centralized catalog of data assets, making it easier for users to find, understand, and trust the data they need. Here are the technical details and requirements of Qlik Data Catalog:

Technical Details:

  • Qlik Data Catalog integrates seamlessly with Qlik Sense, allowing users to search and access relevant data assets directly within the Qlik Sense environment.
  • It supports a wide range of data sources, including databases, data warehouses, spreadsheets, cloud repositories, and more.
  • Qlik Data Catalog employs intelligent algorithms to automatically profile and tag data assets, enabling users to quickly assess data quality, lineage, and relevance.
  • It offers a user-friendly interface for data stewards to curate and manage metadata, ensuring data governance and compliance.
  • Qlik Data Catalog leverages AI and machine learning capabilities to recommend related data assets, enhancing data exploration and discovery.


  • Qlik Data Catalog requires Qlik Sense Enterprise on Windows.
  • It requires a separate installation and configuration, which can be performed by system administrators.
  • Qlik Data Catalog integrates with various data sources using connectors and APIs, necessitating proper connectivity and authentication configurations.

Benefits of Qlik Data Catalog:

  • Improved Data Governance: Qlik Data Catalog promotes data governance by providing a single source of truth for data definitions, lineage, and quality.
  • Enhanced Data Discovery: Users can easily search and find relevant data assets using intelligent search capabilities and rich metadata.
  • Accelerated Data Preparation: With data profiling and tagging, users can quickly assess data quality and relevance, reducing time spent on data preparation.
  • Collaboration and Knowledge Sharing: Qlik Data Catalog enables collaboration between data stewards and business users, fostering a culture of data-driven decision-making.

Qlik Alerting

Qlik Alerting

Qlik Alerting: Qlik Alerting is a module that adds proactive intelligence to Qlik Sense, enabling users to receive real-time notifications based on predefined conditions or changes in data. Here are the technical details and requirements of Qlik Alerting:

Technical Details:

  • Qlik Alerting leverages the associative engine of Qlik Sense to monitor data and trigger alerts based on specified conditions.
  • It supports various delivery channels for notifications, including email, SMS, and in-app alerts.
  • Qlik Alerting offers a user-friendly interface for configuring alerts, defining conditions, and managing recipients.
  • It supports dynamic alerting, allowing users to create alerts based on changing data values or trends.


  • Qlik Alerting is available for both Qlik Sense Enterprise on Windows and SaaS editions.
  • It requires installation and configuration by system administrators.
  • Proper connectivity and access to data sources within the Qlik Sense environment are essential for configuring alerts.

Benefits of Qlik Alerting:

  • Proactive Monitoring: Qlik Alerting ensures that users stay informed about critical changes or anomalies in their data in real-time.
  • Operational Efficiency: By automating the monitoring process, Qlik Alerting reduces manual effort and enables timely actions.
  • Customizable Notifications: Users can tailor alerts based on their specific requirements and receive notifications through their preferred channels.
  • Data-Driven Decision-Making: Real-time alerts empower users to make data-driven decisions promptly, ensuring they stay ahead of changing business conditions.

Unlock the true potential of your data with Qlik Sense and its powerful additional modules. Empower your organization to make data-driven decisions and drive success in the digital age.

Qlik AutoML

Qlik AutoML

In today’s data-driven world, organizations are constantly seeking innovative ways to extract valuable insights from their data. One such innovation is automated machine learning (AutoML), which streamlines and accelerates the process of building and deploying machine learning models. Qlik, a leader in data analytics and business intelligence, offers Qlik AutoML as an additional module within its comprehensive platform. This article explores the capabilities of Qlik AutoML, its technical requirements, and the benefits it brings to organizations.

Understanding Qlik AutoML: Qlik AutoML is designed to democratize the power of machine learning by automating the end-to-end model creation process. It eliminates the need for extensive coding or data science expertise, allowing business users to leverage the power of machine learning effortlessly. With Qlik AutoML, organizations can extract meaningful insights, make accurate predictions, and optimize decision-making through advanced analytics.

Technical Requirements: To harness the capabilities of Qlik AutoML, organizations need to ensure their infrastructure meets the following technical requirements:

  1. Qlik Sense Enterprise: Qlik AutoML is a module available within Qlik Sense Enterprise, so organizations must have a licensed version of Qlik Sense Enterprise to access and utilize Qlik AutoML.
  2. Data Connectivity: Qlik AutoML requires connectivity to relevant data sources, such as databases, data lakes, or data warehouses, to access the necessary data for model training and validation.
  3. Adequate Hardware Resources: AutoML tasks can be computationally intensive. Organizations should ensure they have sufficient computational resources, including CPU and memory, to support the AutoML process effectively.

Capabilities and Benefits: Qlik AutoML offers a range of capabilities and benefits that empower organizations to leverage the power of machine learning:

  1. Automated Model Building: Qlik AutoML automates the process of model selection, feature engineering, and hyperparameter tuning. It intelligently explores different algorithms, evaluates performance, and selects the best model, saving time and effort.
  2. Time-Saving: With Qlik AutoML, organizations can build machine learning models in a fraction of the time it would typically take. This empowers business users to explore and iterate on models quickly, leading to faster insights and improved decision-making.
  3. Democratizing Machine Learning: Qlik AutoML eliminates the need for extensive data science expertise. Business users can leverage the power of machine learning without the complexities of coding or algorithm selection, making advanced analytics accessible to a broader audience.
  4. Improved Accuracy: By automating the model selection and tuning process, Qlik AutoML helps organizations achieve higher accuracy in predictions. It leverages advanced algorithms and techniques to optimize models, leading to more reliable and precise outcomes.
  5. Seamless Integration: Qlik AutoML seamlessly integrates with Qlik Sense, allowing users to incorporate machine learning models directly into their data analytics workflows. This enables users to blend machine learning insights with traditional analytics for comprehensive data-driven decision-making.

Conclusion: Qlik AutoML empowers organizations to leverage the power of automated machine learning, enabling them to extract valuable insights, make accurate predictions, and optimize decision-making. By automating the end-to-end model creation process, Qlik AutoML democratizes machine learning and makes it accessible to business users without extensive data science expertise. With its time-saving capabilities, improved accuracy, and seamless integration with Qlik Sense, Qlik AutoML unlocks the potential of machine learning and enhances the data analytics journey.

To embark on your automated machine learning journey with Qlik AutoML, reach out to our experts or explore our website to learn more about how Qlik can empower your organization with cutting-edge data analytics capabilities.

By embracing Qlik AutoML, organizations can empower their business users, accelerate the adoption of machine learning, and drive actionable insights that lead to better decision-making. Take the next step in your data analytics journey with Qlik AutoML and revolutionize the way you leverage machine learning within your organization.

Qlik Lineage

Qlik Lineage Connectors

Features and Capabilities of Qlik Lineage:

  1. Complete Data Lineage: Qlik Lineage provides end-to-end visibility into the data journey, allowing users to track the flow of data from its original source through various transformations and aggregations to its final destination.
  2. Visual Representation: Qlik Lineage presents data lineage information in a visual and intuitive manner, making it easy for users to understand and explore the relationships between different data assets, tables, fields, and transformations.
  3. Impact Analysis: With Qlik Lineage, users can perform impact analysis to understand the potential consequences of making changes to a particular data source or transformation. This helps organizations make informed decisions and avoid unintended impacts on downstream processes and applications.
  4. Data Quality Assessment: Qlik Lineage enables users to assess the quality of data by tracing its lineage. By understanding the sources and transformations that contribute to a particular data asset, organizations can identify and address data quality issues more effectively.
  5. Collaboration and Documentation: Qlik Lineage facilitates collaboration among data stakeholders by providing a centralized platform to document and share insights about data lineage. Users can add annotations, comments, and metadata to enhance the understanding of data assets and their lineage.

Technical Requirements for Qlik Lineage: Qlik Lineage can be deployed as part of the Qlik Data Integration platform, and it integrates seamlessly with other Qlik products and solutions. Here are the key technical requirements for implementing Qlik Lineage:

  1. Qlik Data Integration Platform: Qlik Lineage is a module within the Qlik Data Integration platform. Therefore, you need to have the Qlik Data Integration platform installed and configured in your environment.
  2. Data Sources: Qlik Lineage supports a wide range of data sources, including databases, data warehouses, cloud platforms, and big data systems. Ensure that your data sources are compatible with the Qlik Data Integration platform.
  3. Metadata Repository: Qlik Lineage relies on a metadata repository to store and manage the lineage information. You need to set up a metadata repository, which can be a relational database or a compatible metadata management tool.
  4. User Access: Qlik Lineage requires user access and appropriate permissions to access the metadata repository and the data sources. Ensure that users have the necessary credentials and permissions to retrieve and view the lineage information.

Benefits of Qlik Lineage:

  1. Improved Data Governance: Qlik Lineage enhances data governance by providing a transparent view of data lineage, helping organizations understand data origins, transformations, and usage across the entire data landscape.
  2. Enhanced Data Trustworthiness: With Qlik Lineage, organizations can ensure data trustworthiness by tracking data quality and lineage. By identifying the sources and transformations that impact data quality, organizations can take proactive steps to maintain and improve data integrity.
  3. Compliance and Regulatory Support: Qlik Lineage helps organizations meet compliance requirements by providing an audit trail of data lineage. This supports regulatory reporting, data lineage documentation, and compliance with data privacy regulations.
  4. Faster Issue Resolution: Qlik Lineage enables faster issue resolution by facilitating impact analysis and root cause identification. When issues arise, users can quickly trace the lineage to identify the source of the problem and take corrective actions.
  5. Data-driven Decision Making: Qlik Lineage empowers organizations to make data-driven decisions with confidence. By having a clear understanding of data lineage, users can trust the data they are working with and make informed decisions based on accurate and reliable information.

Additional Materials