Qlik has expanded its partnership with Databricks with the launch of two new integrations.
Qlik, founded in 1993 and based in King of Prussia, Pa., is an analytics and data integration vendor whose platform is built around the principle of active intelligence, which Qlik describes as the delivery of insights to customers in real time on any device. defines in.
Like peers including MicroStrategy and SAS, Qlik has shifted from an emphasis on its on-premises capabilities to cloud-based analytics. And as part of that development, it adopted a cloud-agnostic strategy that includes partnerships and integrations with data cloud providers including AWS, Google Cloud Platform (GCP), Microsoft Azure, Snowflake and Databricks.
Databricks, meanwhile, is a data lakehouse vendor founded in 2013 and based in San Francisco. Its Lakehouse platform is a combination of Data Warehouse and Data Lake, designed to enable customers to work with structured data using SQL like a data warehouse and query unstructured data like a data lake.
The integration of Qlik Cloud with the Databricks Lakehouse (Delta) endpoint and Databrix Partner Connect – both launched on 26 September – represents the latest collaboration between the vendors.
The Databricks Lakehouse (Delta) endpoint is a tool in Qlik Data Integration that leverages the new SQL-based interface from Databricks to allow combined users to ingest data with Qlik Data Integration and transport it to Delta Lake on Databricks in real time. Can you Previous integrations between Qlik Data Integration and Databricks did not feature a SQL-based interface.
Meanwhile, Qlik and Databricks have an integration between Qlik’s analytics tools and Databricks’ Lakehouse that enables combined customers to access and analyze their data already stored in Databricks.
Integration between Databrix Lakehouse and Qlik Data Integration is critical in ensuring that Qlik customers can leverage their existing investments and skills to integrate data into Databrix Lakehouse. Matt Aslett Analyst, Ventana Research
According to Ventana Research analyst Matt Aslett, the ability to use Qlik before and after storing data in Databricks is what makes up the integration of Qlik – which includes the development of the Databricks Lakehouse (Delta) endpoint.
“The integration between Databricks Lakehouse and Qlik Data Integration is critical in ensuring that Qlik customers can leverage their existing investments and skills to integrate data into Databrix Lakehouse,” he added.
Aslet said the demand for data lakes is increasing, and with that growth comes the need for analytics and data integration vendors to ensure that their tools work with the data lake and data lakehouse vendors.
“The data lake environment primarily coexists with existing data processing and analytical investments, so it is imperative that the data lake environment can be used with both customers’ existing data integration and their analytics products and services.”
According to Itamar Ankorian, senior vice president of technology alliances and managing director of enterprise data integration at Qlik, the new integration results in a more efficient data ingestion process than the previous integration.
In addition to enabling Qlik customers to use their existing tools with Databricks during the analytics process, according to Donald Farmer, Founder and CEO, the Databricks Lakehouse (Delta) endpoint is as important to Qlik users as it is to data science. Enables integration with customized Lakehouse more easily. Head of Treehive Strategy.
In addition, Qlik’s connectors enable the integration of complex data into Databricks so that users can perform analysis on a more complete data set, he adds.
“The Qlik architecture – especially its wide range of replicated and changed-data-capture connectors – enables Databricks Lakehouse to incorporate heterogeneous data sources, including legacy sources, that may be very difficult to integrate in any other way, ‘ said the farmer.
A sample dashboard of Qlik showing the sales performance of various retail outlets.
Meanwhile, the integration between Qlik Cloud and Databricks Partner Connect aims to enable Databricks customers to try out Qlik and experience the platform’s simultaneous performance.
According to Ankorian, the development of the new integration resulted from a combination of both the SQL-based interface in the case of the Databricks Lakehouse (Delta) endpoint – and Qlik’s willingness to take advantage of Databricks’ latest technology, in the case of customer feedback.
“As Databrix’s product evolved with new capabilities, we made investments to align with them to deliver greater value to United customers,” he said. “In addition, customers always prioritize and ask for cost-performance optimization.”
Compare cloud connectivity
As more customers migrate their data to the cloud, many analytics vendors have made efforts to enable those customers to use the cloud of their choice by developing connectors and integrations with various data clouds.
For example, SAS, although its platform is compatible with most major clouds, has developed a close partnership with Microsoft Azure and continues to add functionality in concert with Microsoft. Even smaller vendors like Toucan Toco are aligning themselves with various cloud data platforms.
But with data integration, according to Farmer, Qlik is the furthest away from joining the likes of AWS, Azure, GCP, Databricks, Snowflake and others.
The vendor’s platform does not provide scenario planning tools. And it could improve its extract, transform and load capabilities, analysts have said. But Qlik is moving forcefully as a result of its 2019 acquisition of Attunity, in order to combine data integration tools with cloud data warehouses and data lakes.
ATunity was the first third-party data integration vendor to integrate with Amazon Redshift, dating back to 2013, just after the launch of the tech giant’s cloud data warehouse, Farmer noted.
“That’s why Qlik has more experience in cloud data integration than any other vendor in the market,” he said. “And it shows in the robustness, performance and thoroughness of their offering.”
Looking ahead, Farmer said he would like to see more connections between Qlik’s data management and administration tools and cloud service providers.
“Many cloud data platforms are weak in administration and management, and Qlik can fill that gap,” he said.
Meanwhile, Ankorian noted that Qlik is indeed working with various cloud data platform providers, including Databricks, to add more integrations, though he did not name the specific Qlik capabilities involved in the next wave of those integrations.