BI.Qube 2.0

BI.Qube 2.0 is a framework you can use to connect sources, generate models, create data marts, scale, monitor, and improve data quality from the same interface. Its metacomponents automate data uploads into the staging area, creation of references, fact updates, calculation of aggregates, and other actions using a single parameterization mechanism. The framework supports CI/CD for migration of data models between environments. This reduces the requirements for developer competencies and team size, accelerates development and makes it less costly.

Framework functions

• Update automation.
• Full-cycle data processing.
• Integration of open source components.
• Automated Data Lineage.
• Automatic generation of the global execution graph.

MetaStaging is a tool for data extraction from various sources (accounting systems, databases, websites, mobile applications, internet services) and guaranteed delivery to storage. You can use a relational DBMS or file storage as a storage.

• Transparent transfer of files from sources.
• Data receivers: Data Lake, Kafka^®, DWH.
• Compatibility with SMP and MPP systems (PostgreSQL, Greenplum^®).
• Many different connectors, including a universal one for 1C.
• Tolerant to wrong formats and metadata changes.

MetaVault is a tool you can use to automatically build a DWH data model based on the Data Vault methodology for high performance, scalability, and storage flexibility.

• Automation of DWH development through Data Vault 2.0.
• Business Vault generation with optional materialization.
• Change history in SCD2 format.
• Supports non-destructive changes.
• Three target platforms: MS SQL, PostgreSQL, and Greenplum^®.

MetaMasterData is a comprehensive management tool for master data (MDM, RRD): references, classifiers, standards, etc. Supports ad-hoc management of data that cannot be obtained from sources or requires additional processing.

• RRD (MDM) management in low-code/no-code mode.
• Operates within the Data Vault core layer without requiring integration.
• Adjust, add attributes without the risk of losing your changes.
• Implements the golden record concept, deduplication.
• Supports hierarchies and links between objects.

MetaControl is a tool for flexible data quality management, including data accuracy, completeness, relevance, and consistency checks and notifications on errors and violations.

• Data quality, process and business rule implementation control.
• Context-enriched notifications.
• Business rules can be described in SQL.
• Alerts via email and Telegram.
• Grouping and combining by recipient category.

MetaFact is a tool that generates fact tables for data marts and creates context-independent metrics within a unified data model.

• Working with fact tables as data model objects.
• Automation of partitioning, increment processing, and service fields.
• Managing the fact formation procedure queue.
• Flexible scenarios, including sequential-parallel assembly of dependent objects.

MetaCube is a tool for automated data model transfer to data marts and flexible partitioning.

• Creating a BI model.
• Analytical section and measure management.
• Automatically built aggregates.

1. Create a Kubernetes cluster.

2. Create a node group with minimal computing resource requirements:

   • Number of vCPUs: 4
   • RAM: 16 GB
   • Disk size: 100 GB

3. Create a network load balancer.

4. Create a service account. Assign the license-manager.subscriptionAgent and license-manager.user roles to it.

5. Select the cluster you created earlier and click Go to install on the Marketplace tab of the BI.Qube 2.0 page.

6. On the BI.Qube 2.0 installation page, specify the following parameters under Application settings:

   • Create or select an existing namespace.
   • Enter the application name.
   • Select the service account you created earlier.
   • Enter the domain name for the application.
   • Enter the BI.Qube administrator password. The administrator password must be between 12 and 20 characters long and contain lowercase and uppercase letters, numbers, and special characters.
   • Enter the PostgreSQL administrator username.
   • Enter the PostgreSQL administrator password. The password must be between 8 and 20 characters long and contain lowercase and uppercase letters, numbers, and special characters.

7. Click Install.

The platform will be fully deployed and ready for use within 5 minutes.

Wait for BI.Qube 2.0 to get Deployed status, and for the pods in the Workload tab to get Running status.

Once the network load balancer is successfully deployed and linked up, the authorization page will become accessible via the domain name specified during deployment. Use the following credentials:

• User: biqube-admin.
• Password: BI.Qube 2.0 administrator password specified during deployment.

• Integration of data from DBMS, 1C, API, web services.
• Building a unified scalable DWH model.
• Ensuring the quality of data and notifications about the results of inspections.
• Master Data Management.
• Working with fact tables as data model objects in low-code mode.
• Transferring the data model to data marts.

BI.Qube
BI.Qube provides technical support to Yandex Cloud users. You can contact their support team at support@biqube.ru.

Yandex Cloud
Yandex Cloud does not provide technical support for this product. If you have any issues, please refer to the vendor’s information resources.