Problems we solve

Integrate multiple clouds

  • Build a single view of data across different clouds and regions.

  • Replicate data between different clouds and data warehouses.

Phase transition to the cloud

  • Migrate on-premise data warehouses to the cloud step by step.

  • Create data pipelines for migrated data warehouse and access legacy data warehouses in real-time. Not all data warehouses may be moved to the cloud in one step.

Build an agile data culture

  • Empower data users to find and prepare the data they need in analytics.

  • A fast data strategy based on a Modern Data Pipeline and real-time access prevents delays in data preparation.

Ensure data governance in the cloud

  • Build a searchable data catalog of valuable data sources. Apply table, row and columns security to any data source on-premise, SaaS or in the cloud.

  • Build a Governed Data Lake integrated with the enterprise Active Directory for authentication.

Universal data hub for real time analytics

Lyftrondata is a data layer for real-time analytics. It not only simplifies data loading to a target Data Warehouse, but also modernizes the traditional Data Warehouse architecture, thus adding true real-time query capabilities.

Share the data model

Avoid data models hard-coded in dashboards. Maintain a single data model that is reused between dashboards and different BI tools.

Combine data sources

Join disparate data sources in real-time. Combine SaaS and on-premise data sources both ad-hoc or by creating join views.

Find the right data

Create a global catalog of data sources and essential data sets. Find data sets across all data sources, the Data Warehouse and the Data Lake, all in one place.

Less dependency on IT

Empower data teams with self-service data management that they already do inside dashboards. The wait for a change in the data warehouse should not delay the project.

Engage business

Reverse the natural order of dashboard delivery and put the business needs first. Create prototype dashboards on real-time data, discuss with the business and move the data loading, cleansing and speed-up to the end.

Accelerate slow queries

Cache data from slow performing data sources in fast data warehouses, in-memory or create materialized views with pre-aggregated data.