Tool suite with data management functions for connecting, preparing and providing data for analyses to support various data integration use case pattern.
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The information in this section has been provided by the vendor of Qlik Data Integration.
This section contains our independent analyst views on Qlik Data Integration.
Qlik, originally founded in 1993 in Lund, Sweden, moved its headquarters to Radnor, Pennsylvania in the United States in 2005 after raising funds from several venture capital firms. It was acquired by the current owner – private equity company Thoma Bravo – in 2016. Qlik offers a broad portfolio of end-to-end, platform-based solutions for analytics and data management. In addition to Qlik Sense and Qlik Cloud Analytics, its portfolio includes data management capabilities such as data ingestion, transformation, cataloging, quality and governance.
Qlik has greatly expanded its data management offerings through acquisition in recent years. Major acquisitions include Podium Data for data cataloging in 2018; Attunity for data integration in 2019; Blendr.io for IPaaS in 2020; NodeGraph for graph-based data cataloging, lineage and governance in 2021; and Big Squid for AutoML in 2021. In 2023, Qlik acquired Talend to enhance its data transformation, data quality and data governance capabilities. And in early 2024, it purchased the technology and key talent of Kyndi, a provider of generative AI, natural language processing and search, to help companies explore and analyze unstructured data alongside structured data.
Qlik Data Integration and related tools manage data in support of both BI and data science projects. This includes Qlik Replicate, Qlik Cloud Data Integration, Talend Data Fabric, Talend Data Preparation and Talend Data Catalog. Together these products deliver data for consumption by Qlik Sense, Qlik AutoML, and third-party analytics tools. While Qlik is transitioning to a SaaS model, products such as Compose for data warehouse automation are also available on premises or as a hybrid deployment.
Qlik Replicate supports real-time streaming via change data capture (CDC) from database sources such as Db2 z/OS, SAP and Oracle to popular targets such as Amazon Redshift, Snowflake, Databricks, Google BigQuery and Microsoft Azure Synapse. Qlik Compose for Data Warehouses enables rapid graphical development of data warehouse models and marts, while Compose for Data Lakes consolidates and re-formats data for analytics. The catalog provides a business-user-friendly interface to search and analyze connected datasets. And Qlik Enterprise Manager manages, orchestrates and monitors these various pipelines across heterogeneous environments. Together, these capabilities enable midsized and large enterprises to manage and analyze data in support of real-time decisions and actions.
BARC’s viewpoint on the product’s strengths and challenges.
The information contained in this section is based on user feedback and actual experience with Qlik Data Integration.
The information and figures are largely drawn from BARC’s The BI & Analytics Survey, The Planning Survey, The Financial Consolidation Survey and The Data Management Survey. You can find out more about these surveys by clicking on the relevant links.
All key figures for Qlik Data Integration at a glance.
Performance und User-Freundlichkeit.
Administation der QLIK Server Lösung.
QLIK Cloud als Alternative zu prüfen.
Begeistert unsere Anwender.
Die einfache Integration von Daten aus unterschiedlichen Quellen.
Die Laufzeiten könnten verbessert werden.
Gute Strategie zur Implementation von Datenquellen erstellen.
Wir haben sehr gute Erfahrungen gemacht, wobei die ersten Steps schwierig waren.
Schneller Einstieg.
Hardwarebedarf.
In potente Hardware investieren, damit es Spaß macht.
Durchweg gute Erfahrungen.
Versatility with multiple data sources and fast loading speeds. Great for knitting data from multiple sources together for single customer view.
Steep learning curve to get up to speed.
Do not under estimate the time it will take to design the user interface.
Adds a great deal of functional ability to your data that can help users break down their data barriers.
Ease of use and availability support when needed.
All is fine, so nothing that I do not like.
Just try and you will be surprised how it can help.
Very impressed of all the possibilities.
Qlik Data Integration offers a comprehensive suite of solutions to address various data-related needs. Here are some key aspects that make it stand out: Data Sources and Targets: Qlik provides a trusted data foundation to power AI, ML, and analytics by integrating various data sources and targets. Automation: With Qlik, you can automate data replication, ingestion, and streaming using Qlik Replicate. Additionally, Qlik Application Automation allows you to trigger informed actions on most SaaS applications. Analytics-Ready Data Sets: Qlik Compose for Data Lakes and Qlik Compose for Data Warehouses automate data pipelines, creating analytics-ready data sets for efficient analysis. Real-Time Unification: Qlik Cloud Data Integration enables real-time data transformation and unification for all your analytics, ML, or digital transformation initiatives.
The licensing process turns out to be very complex, as it takes into account the processing of source bases.
Every negotiation process has to be well aligned with the supplier's team, as there are always aggressive conditions and discounts. For the technical team, we must follow all the permissions requested for the data source and destination users.
Qlik Data Integration is a great solution for data integration and the generation of DW, DM and DL, bringing a lot of agility to the delivery of data ready for consumption by the company's business areas.
Speed of development in the low-code environment is much better compared to traditional tooling. Replication capabilities with CDC in particular are excellent, warehouse automation also extremely efficient
Limited flexibility in warehouse design without heavy customisation. Not a commonly used tool so next to no reference material/community support available.
Take time to get engineers familiar with it, several months of experience needed to get comfortable.
It is an effective, efficient and powerful tool.
The AI integrated modules that leads to fast model training and development.
The hard integration with external applications.
You can use it best for business intelligence. It is made for BI. It is also a good tool to make AI predictions without specific AI knowledge.
It is really good!
