Direct Query now available in Qlik Sense SaaS!

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Extend your analytics capabilities with cloud databases using Direct Query!  Qlik is excited to announce the release of Direct Query, a new capability that allows applications in Qlik Sense SaaS to directly query cloud databases using SQL pushdown as users interact with data through visualizations and user filtering.

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Differing from our industry leading analytics engine, Direct Query generates SQL queries on the fly against the source and executes the query and compute at the database level. From big data to near real time use cases, Direct Query empowers users of any level to easily explore and analyze the most recent data right from the database, increasing the speed to insight and decision-making. 

Direct Query complements our best-in-class analytics engine and extends the range of consumption techniques for analyzing data from cloud databases. The first connector rolling out for Direct Query will be for Snowflake with Databricks next on the roadmap and others planned, so stay tuned.  

Can't see this video? - View it on the Qlik Video Page - Direct Query Part 1 - Overview and Operation

Can't see this video? - View it on the Qlik Video Page - Direct Query Part 2 - Building Hybrid Analytic Solutions

How does Direct Query work?  

Direct Query can be activated at the database connection level. So, using Snowflake as the example, when building a new application and starting with a data connection, you will have the option to use Direct Query versus our analytics engine.  

The user can then select certain components of the source database –in the Snowflake connector users will see the options for role, database, and schema. Authentication for Direct Query is governed at the database level so users will only have access to what is granted from the source database. 

The user can then select a single view or multiple tables with the option of choosing the type of relationship between the data sets like full outer join or inner join.  

Once the relation is designated, an import live statement is inserted into the script in the load editor on the back-end and the visualizations can begin being built on the front-end. Since this is a SQL pushdown tool, users will need to use the function library of the underlying database. As of this blog, we will support the major 5 aggregations of sum, avg, min, max, and count. Also, some simple forms of set analysis will be available for filtering within measures of charts. 

After the charts and objects have been built, users can filter and interact with data in real time as Direct Query generates SQL on the fly and pushes the query and compute down to the source database. Keep in mind that every selection or filter within the app from a user, results in a query pushed down to the database and compute. 

Direct Query will also feature a seamless transition into our analytics engine for advanced interactivity and the full suite of capabilities. Check out Part 2 ‘Building Hybrid Solutions’ from the videos below.  

To learn more, check out these two videos on Direct Query: 

Part 1: Overview and Operation 

Part 2: Building Hybrid Analytics Solutions with On Demand App Generation (ODAG) 

To learn more about how Qlik and Snowflake together can optimize your investment into data and analytics, read our corporate blog here.  

Join us for the webinar series, Do More with Qlik, on August 24th where our very talented Mike Tarallo will be showcasing Direct Query for Snowflake.  

[male_carrasco]

Hi All! Is it possible to combine Direct Query with QVD files in real time?

[MatthiasAnders]

@male_carrasco : This use case would fit for ODAG or Dynamic Views as Direct Query goes directly against the source system. With ODAG or Dynamic Views you can freely define the source, which can be QVDs or any other file based source as well.

Regards