Business intelligence acceleration
Simplify Business Intelligence projects by collaborative data modelling,self-service data preparation and instant Logical Data Warehousing.
Reduce the time to insights
Universal data platform for analytics
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
Reverse the natural order of dashboard delivery and put the business needs first. Create prototype dashboards for real-time data, discuss with the business and move the data loading, cleansing and speedup to the end.
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 from IT
Empower data teams with self-service data management which they already do inside dashboards. The wait for a change in the data warehouse should not delay the project.
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.
Universal data hub
Load data from 130+ supported data sources into all popular data warehouses. The Lyftron pipeline will normalize all data into a data pipeline that may be managed using standard SQL. The processing may be pushed down to the source to limit the data size or pushed down to the target database to enable advanced Extract-Load-Transform capabilities.
Import metadata from data sources in a few clicks. All data management activities are performed in a self-service data management portal for BI and IT users.
Apply data transformation, filtering and data cleansing using a unified SQL that is compatible with SQL Server. Complex transformations are pushed down to data sources and target databases when possible.
Choose the target data warehouse and source tables that must be loaded. Scheduled jobs keeps the data synchronized.
Modernize the data warehouse
Improve the Data Management by using a unified SQL interface to all data sources and target data warehouses.
Build a Logical Data Warehouse to gain flexibility and agility in all data management and transformation activities.
Improved data stack with Lyftron
A Logical Data Warehouse that is put on top of all source and target databases turns the data stack into a real data hub, where all data is available. The SQL Interface in Lyftron simulates SQL Server wire protocol, so all 150+ may be queried using Transact-SQL and SQL Server drivers (ODBC, JDBC, ADO.NET) that are already provided in all BI tools.
Create data sets
Create virtual data sets as SQL views using Transact-SQL for all data sources. Materialize selected data sets to avoid overloading source systems. Describe data sets in the data catalog to foster collaboration.
Use real-time data
Query all data sources and target data warehouses in one place using Transact-SQL. Run real-time queries on multiple data warehouses before the data is aggregated in one target data warehouse to enable incremental data warehouse migration.
Secure all data
Apply data security rules over all available data. Secure data with dynamic data masking and row-access security. Use Windows Integrated Authentication for all data sources, both on-premise and cloud.