Lyftron is a modern data platform that expands traditional data loading (ETL) with real-time access to any data, from one place.
A single data platform to
Data preparation for analytics with Lyftron
The data that is located in various data sources is not always usable for analytics without preparation and loading to a central Data Warehouse. Lyftron manages a Data Pipeline that unifies all data sources to a single format and loads the data to a target Data Warehouse, which is used by BI tools.
Lyftron’s unique feature is a Normalized SQL Data Pipeline in the middle of the data transformation process. All data sources are virtually treated like SQL databases and are transformed by defining virtual data sets and views.
Lyftron data pipeline architecture
Normalized SQL data pipeline
Target data loaders
Disadvantages of a traditional data warehouse
Given the length of a data pipeline, there is always a delay before the data is consumable for analytics. What if the whole data preparation process could be simplified and unnecessary delay and rework avoided?
Lyftron addresses the biggest delay in data preparation by giving access to all data sources before they are transformed and loaded:
Universal data platform for analytics
Lyftron is a data layer for Analytics that combines traditional data loading with Logical Data Warehousing to improve collaboration and enable early access to any data.
The SQL interface accepts queries that are routed to either to the target Data Warehouse or executed in real-time at the data source. The data may be loaded to a Data Warehouse later, but the time saved on early prototyping the data model is priceless.
Data management features
Register all data sources in one place. Use ODBC, JDBC, ADO.NET or API data sources.
Register a target Data Warehouse and select which tables and data sets are replicated.
Self-service data preparation for data savvy users to encourage collaboration and reuse.
SQL processing features
SQL server simulation
Fully simulated SQL Server layer makes all data analyzable with T-SQL. Treat everything like an SQL Server Data Warehouse.
Accelerate slow queries by caching them in-memory or in a faster database. Build pre-aggregates that are 1000x smaller.
Simulate a database that has real-time federated tables instead of a staging layer. Build a true Logical Data Warehouse.
Operations & management features
Processing & monitoring
Manage and data management jobs in one place. Keep the Data Warehouse healthy by detecting invalid data sets.
Apply data security features for any data source, on-premise or cloud.
Built-in apache spark
Simply start Lyftron, connect data sources and use a built-in Apache Spark as a Data Lake. Migrate to a bigger instance later.