More and more companies are moving data and applications to public and private clouds to take advantage of scalable data storage and processing. At the same time, there is still a need for local data storage and processing, for example for legacy systems or due to security and compliance regulations.
To address these challenges, Qlik Sense is offering new ways to deploy a multi-cloud architecture since one of its latest releases.
With the new deployment package “Qlik Sense Enterprise for Elastic”, Qlik Sense installations can be flexibly distributed, consumed and managed across a wide range of environments – whether in the cloud or locally. The architecture can be easily and flexibly adapted at any time – depending on how business requirements change over time.
The multi-cloud capabilities were developed to provide users with a unified platform that is easily and economically scalable, irrespective of the different local and cloud infrastructures. Accordingly, all users have a single, common license and login in all environments to make access easy and seamless.
Customers can benefit from the multi-cloud features since the release of Qlik Sense Enterprise June 2018.
In many companies, clean data are the base of important business processes. Analyzed data is essential – especially for new insights or as a decision-making aid. Because these data don’t come from a single internal source any longer, the possibility of integrating external data sources should always be considered at choosing data analysis software.
The storage of company data has gone through a major change in the recent years. In particular, the cloud movement into many companies means that information is no longer stored at a central location, but is obtained from multiple sources. Companies often base decisions on efficiency and go for various cloud models.
But the increased storage options pose a particular challenge to analytics tools, as all relevant data from the various sources must be put together without much effort. The best way to do this is to move away from data silos. Instead, the analysis software should extend across all data sources without having to migrate the data required for evaluation to a central location via various infrastructure barriers.
The various information formats are also important. It should be possible to include external social media data from the cloud in the analyses also as classics like Excel. Open APIs or other connectors can be helpful, as they allow the various sources, whether on-premise or cloud, to be linked to each other without any problems.
The user-friendliness of the software should also not be underrated. The combination of data should be flexible, scalable and simple in order to prevent user mistakes in data analysis and to be able to provide the required data as quickly as possible.