Advanced Architecture Enables Real-Time SQL Queries to All Data Sources and Delivers Business Insights 4x Faster
Lyftron, developers of agile, enterprise-wide data delivery solutions, officially unveiled its universal data access platform to the US market. Its advanced technology layer unifies structured and semi-structured data from more than 100 sources including modern data warehouses like Snowflake, Redshift, BigQuery, Yellowbrick, Azure SQL Data Warehouse, Apache Spark as well as business intelligence tools such as Looker, Tableau and Power BI, and enables real-time analysis and faster insights.
- Data Hub in Minutes – Utilize T-SQL and standard SQL Server drivers to query data from business intelligence (BI) tools and eliminate the need for manually building data pipelines. Data Scientists can use Lyftron’s pre-built connectors to effortlessly connect to any data source and deliver data to warehouses in normalized, ready-to-query schemas, reducing the time required for reporting.
- Shorten Time to Insights – Introduce collaborative data modelling, self-service data preparation and instant logical data warehousing to deliver business intelligence in real-time. Data preparation with Lyftron starts with real-time access to selected sources and the legacy data warehouse from one place. After transformations are applied, the data is bulk loaded to the new database of the users choice. The technology’s logical data warehouse layer provides parallel access to both data warehouses and all data sources in one place.
- Enterprise-Wide Data Catalog and Data Lineage – Enable full database objects search guided by the principles of tagging, alias, and data set definition. Lyftron’s platform empowers data analysts to collaborate with ease. The data lineage process is also simplified, enabling teams to bring in visibility for data sets usage on various stages and maintain a healthy warehouse.
- Hybrid Cloud Management and Migration – Build a hybrid cloud data warehouse that acts as a data bridge between leading cloud platforms, on-premise data warehouses and data sources. With Lyftron, users can access all the data from different regions in a data hub instantly and migrate from legacy databases to a modern data warehouse without worrying about coding data pipelines manually. The data loading part should be responsible for keeping the data synchronized across databases, while the data bridge would let BI tools execute SQL queries across the cloud boundary.
“Our modern data hub is designed to easily and seamlessly serve, process and manage any type of data for any size customer,” said Piotr Czarnas, Lyftron founder & CEO. “After trialing our offerings with organizations across Europe, and refining the design based on client feedback, we’re excited to officially introduce Lyftron to the US market.”
Lyftron’s technology combines columnar data pipeline process with modern data hub architecture that eliminates traditional extraction, transformation and loading (ETL)/ extraction, loading and transformation (ELT) bottlenecks with automatic data pipelines and makes data instantly accessible to BI users with the power of Spark and Snowflake. Lyftron connectors automatically convert any source into normalized, ready-to-query relational format and provides search capabilities on enterprise data sets to improve collaboration and enable early access to all data in one, central location. The SQL interface simulates SQL server protocol and translates Transact-SQL queries to data sources. Lyftron’s unique functionality enables analysts to access sources before they are transformed and loaded and also accepts queries that are routed to the target data warehouse as well as those executed in real-time at the data source.
Lyftron is perfect for technology and data analyst teams hoping to reduce their cost of data management by up to 75% and query any data with ANSI SQL. The platform provides a view of all data in one place without data movement and enables governed data lake and cloud data warehousing. This functionality and the ability to prototype data models in real time result in significant time and cost savings, while also improving scalability. Additional partnerships with major data warehouse providers and integrators will be announced shortly.