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Qlik launches new cloud-based data integration platform

The new integration platform as a service joins data preparation and cataloging capabilities in one place, enabling organizations ready their data in real time for analysis.

Qlik launched a cloud-based new data integration platform service that brings together data from disparate sources in real time.

Qlik Cloud Data Integration, which the vendor unveiled on Nov. 2, is an enterprise integration platform as a service designed for use by data engineers who cultivate and prepare their organization's data for data-informed decision-making.

The platform consists of a set of services that form a data fabric, which is the unification of those services to connect data sources to enable an organization to create a unified view of its data.

Emphasis on data integration

Founded in 1993 and based in King of Prussia, Pa., Qlik was historically a business intelligence vendor. Beginning in 2018, the vendor added data integration capabilities through the acquisitions of Podium Data (2018), Attunity (2019) and (2020).

Qlik has since joined the tools it acquired from those vendors to enable users to integrate and catalog their data.

Now, however, the vendor has combined those tools in a single cloud-based platform for the first time. That unification makes the release of Qlik Data Integration significant, according to Doug Henschen, an analyst at Constellation Research.

"This is a significant move for Qlik in that it's bringing together multiple integration capabilities in its portfolio into a cloud-based integration platform as a service," he said. "The challenge will be that the iPaaS market is already somewhat crowded, though different vendors have different sweet spots."

One of those "sweet spots" is an emphasis in on integrating data points from various sources while the other is integrating applications, Henschen continued.

This is a significant move for Qlik in that it's bringing together multiple integration capabilities in its portfolio into a cloud-based integration platform as a service. The challenge will be that the iPaaS market is already somewhat crowded, though different vendors have different sweet spots.
Doug HenschenAnalyst, Constellation Research

Vendors such as Informatica and Talend are among those with a focus on traditional data integration, while Boomi and Jitterbit are among those that emphasize application integration.

Given that Podium and Attunity were traditional data integration vendors and focused on application integration, Qlik's new platform has to the potential to do both.

"This iPaaS is likely to be more data integration centric -- although Blendr was more application integration and workflow centric," Henschen said.

In fact, Qlik Data Integration was developed partly to appease customers needing to integrate their applications and the data from those applications, according to James Fisher, chief product officer at Qlik.

He noted that data integration is evolving. In the past, it was simply about integrating data itself. As organizations deploy an increasing number of applications, integrating those applications and the data inside them is critical.

"We're seeing … a shift away from very data-centric data integration into a world where enterprise applications exist in the cloud. And the notion of data pipelines is less data-centric and more about how to move data from one cloud application to another and how to combine data from multiple applications," Fisher said.

New capabilities

Like Qlik's analytics platform, Qlik Data Integration is cloud agnostic and can work in concert with major cloud data platforms, including AWS, Databricks, Google Cloud, Microsoft Azure and Snowflake, according to Qlik.

And like Qlik's analytics platform -- which has been optimized for the cloud in recent years after being geared toward on-premises customers in the past -- the new data integration platform demonstrates the vendor's transformation toward a cloud-first mentality, according to Fisher.

"We built out the cloud platform first on the analytics side. And now we [want] to bring our data integration capabilities into the cloud," he said.'s application automation capabilities were quickly added to Qlik's cloud platform following its acquisition, pushing Qlik's notion of active intelligence by automating actions, Fisher added. Now,'s capabilities have been joined by those that Qlik gained through its other acquisitions.

"Bringing the data integration component rounds out that cloud offering," he said.

Features of Qlik Data Integration include the following:

  • real-time movement of data to the cloud with Qlik's change data capture engine;
  • automated transformation of raw data into analytics-ready data through a reusable pipeline of data models and data quality guidelines that work with organizations' data models, data marts and other customized tools;
  • data catalog and data lineage tools to identify where a customer's data originated, how it was transformed and how it's being used by employees to inform decisions; and
  • prebuilt templates so data engineers can automate ETL and data cataloguing workflows.

Product development plans

While Qlik Data Integration represents Qlik's first iPaaS platform, vendors including Boomi, Informatica, SAP, Talend and Tibco offer similar cloud-based tools.

And as Qlik adds functionality to its data integration platform, Henschen said he'd like to see the vendor incorporate some of the capabilities its competitors already possess.

"I've seen several data-centric iPaaS vendors stepping up in areas including API management, workflow and automation capabilities as well as low-code development options," he said.

Fisher, meanwhile, noted that Qlik plans to add more automated machine learning functionality to its iPaaS offering. The platform's change data capture, data catalog and automation capabilities are strengths, he said. But there is room to add more autoML.

As with its analytics platform, machine learning is built into Qlik Data Integration. But autoML is only available toward the end of the data pipeline as users are readying data for analysis.

A goal for Qlik is to build autoML in earlier in the data management process, just as it has now been built in earlier in development of analytics models and applications.

"We see a huge opportunity to extend that notion of AI/machine learning into the full analytics pipeline by bringing it into what want to do with data integration," Fisher said. "That's an opportunity for us to evolve what we're already doing in the market."

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