Technology and Features#

Broad connectivity to Data Sources#

Querona can directly connect to over 200 data source types like relational, MPP, NoSQL, SaaS, CRM’s, or Social Media and supports data access technologies like ODBC, JDBC, and ADO.Net.

For a full list of supported data sources please consult the Data sources article.

Emulation of SQL Server#

Querona’s emulation of Microsoft SQL Server allows any client supporting connectivity to SQL Server to connect to Querona, without additional configuration. The built-in drivers just work.

Emulation delivers compatibility with the following SQL Server features:

  • Metadata model, including metadata catalog, system views, stored procedures, and functions

  • Authentication using Windows Integrated Authentication and Standard Authentication (Mixed Mode)

  • Mandatory privacy and data security for communications (Strict mode with TLS 1.2 or TLS 1.3)

  • Strong audit capabilities involving tracking, logging, and reporting of events

  • Security model with object-level permissions, row-level security, and data masking

  • TransactSQL dialect (TSQL)

  • Tabular Data Stream 7.x and v8.0 network protocol as described in Microsoft TDS documentation

  • Data types and automatic conversions (data types from sources are automatically converted into matching data types)

  • Procedural programming constructs: IF, WHILE, DECLARE

  • Temporary tables (stored in memory)

  • Job scheduling model and accompanying stored procedures

Connectivity to Querona was verified using notable tools and technologies listed below.

Analytical, reporting, or development tools, for example (not a complete list):

  • Apache SuperSet

  • DBeaver (JDBC)

  • Microsoft Office

  • Microsoft SQL Server Management Studio

  • Microsoft Azure Data Studio

  • PowerBI

  • Qlik

  • Tableau

  • Targit

Data access technologies:

  • ADO.Net

  • JDBC

  • OLE DB

  • ODBC

Built-in Apache Spark#

Apache Spark engine is built into Querona. No configuration is needed and a standalone instance of Spark is ready to use for querying and data storage. To utilize the power of Spark users do not have to learn to code in Scala or SparkSQL, because everything is wrapped and hidden by Querona’s emulation of Microsoft SQL Server.

Metadata lineage#

Querona traces and maintains the metadata lineage using all created objects and their dependencies. Each view’s dependency graph can be examined graphically and searched.

Columnar processing#

Querona is a columnar data virtualization engine. Incoming rows of data are converted into a columnar format for high parallelism and high-performance processing. Outgoing results are converted into rows for compatibility with the SQL Server protocol (TDS).

No ETL#

Compared to classic, flow-based ETL tools, Querona implements ETL responsibilities differently. Most of the processing requirements can be expressed using SQL and Views and that is the approach in Querona.

  • Virtual Databases are wrappers over a data source to provide direct access to data, or created over many supported data processing engines, that may be used for data integration and materialization.

  • Views may depend on each other and blend data using the full power of SQL language.

  • Views created in Querona’s virtual databases are materializable (aka. cachable) using multiple strategies like Full load, Incremental load, adn others.

  • Many processing-engine-dependent strategies can be utilized, for example, in-memory materialization (caching) or persistent materialization using table rotation.

  • SQL Server’s restrictions of so-called “indexed views” do not apply to Querona.

Transparent data pseudonymization#

Data pseudonymization works transparently for the end user. If any user decides to cache sensitive data on the untrusted system, Querona will automatically detect that sensitive data is being stored and will encrypt the data. The user will not detect that encryption happened because he or she will see real data, even if processing happened on an untrusted system.

For more information see Data Security.

Query retargeting#

The use of pre-computed aggregate data is a technique to address performance challenges in Data Warehouse systems (DW). An aggregate retargeting mechanism redirects a query to an available physical aggregate when possible. The adoption of aggregates is completely transparent to the users.

For more information see Overview of Query Retargeting.

Flexible deployment model#

Querona can be deployed using the following models:

  • on-premise, using a physical or virtual machine with Windows (server and desktop)

  • cloud, IaaS

  • hybrid (mixed), on-premise, cloud, or desktop computer

Multilanguage user interface#

Querona supports the following GUI localizations:

  • English

  • German

  • Polish