Rationale behind Querona¶
The primary reason that companies implement Business Intelligence (BI) solutions is to support and improve their decision-making process. Changing world puts pressure on companies to react and make decisions faster. Time to decide is shrinking and companies find it harder to make timely decisions. Organizations need to be agile and embrace change quickly by adopting their culture, existing IT systems and reporting. New valuable, external data sources emerge frequently. Cloud usage is growing due to the utilization of SaaS platforms, putting more pressure on companies IT and operations. New technologies like analytical column databases, in-memory databases, mobile, machine learning or BigData systems, create an opportunity to speed up analysis and open new possibilities. Current data management processes utilizing ETL, require a lot of effort to change or process and are usually not suitable for an agile approach.
Querona’s vision is to unleash the potential of data virtualization and data federation technologies. Backed by BigData technology stack, to concurrently allow for high compatibility with existing technologies used for data access and analysis. And abstraction layer provided for all applications and data, helps to achieve flexibility for change, pervasive and consistent data access, and greatly reduced costs because of less need to create physically integrated data structures.
The end result is greater agility from, and freer access to, an organization’s data assets and promotion of self-service. Among other benefits, offer an opportunity for organizations to change and optimize the manner in which data is processed and physically persisted, while not impacting the applications and business processes.
Querona’s goals are:
- Through Data Virtualization enable customers to rapidly develop and deploy data services that access, federate, abstract, and deliver data on-premise and in the cloud
- Data efficiently delivered via established protocols and technologies can be reused on multiple projects, allowing to achieve agility
- Gain faster business insights by almost instant, access to all the data, in a customizable and secure way
- Respond faster to ever changing requirements of analytics and BI enabling 5-10 times shorter time to solution than traditional EDW
- Enable savings of 50-75% over data integration and consolidation.
Vision meets technology¶
Vision and goals execution:
- Provide a high performance columnar data virtualization engine,
- Emulate most popular and successful enterprise-grade database engine on a protocol level, allowing for broad and fast adoption without additional changes to existing tools and infrastructure,
- Provide pure data virtualization tool that is fully integrated with Microsoft technology stack,
- Take advantage of the fastest data processing engines on the market like Apache Spark and others, enabling users to easily switch engines and use them in parallel,
- Provide full web-based self-service and administration portal,
- Provide internal ETL pipeline that uses parallel columnar processing,
- Provide on-premise, cloud and hybrid deployment models.
|Support of||Querona’s answer|
|Any analytics client||Microsoft SQL Server emulation so any client can connect without additional configuration allowing for top level compatibility with existing solutions|
|Any execution engine||Multiple data processing engines supported for caching, that can be used simultaneously and work as a single unit|
|Any data source||128+ data source types supported out of the box|
|Any platform||Windows and soon Unix/Linux using Dotnet Core|
|Any deployment model||On-premise, Cloud, Hybrid|
|Any scale||MPP engines supported, Apache Spark BigData engine is built-in and ready to use|