Take data from any source and load to Amazon Redshift, analyze instantly

Lyftron replicates data from multiple data sources and manages data loading to Amazon Redshift. Lyftron shortens data preparation activities by letting data teams create virtual data sets first, evaluate the data and delay the data loading until the data sets are verified or data loading is required.

Stream & Load data to Amazon Redshift in minutes

Lyftron Data Pipeline manages connections to data sources and loads data to Amazon Redshift. All transformations are defined in standard SQL and pushed down to data sources and Amazon Redshift.

Automatically Manage Tables

Lyftron creates target tables in Amazon Redshift and picks the right data types.

Transform with SQL

Transform or filter the data using SQL that is pushed down to Amazon Redshift. Enable a true Extract-Load-Transform to avoid extra data copies.

Prototype Data Sets

Avoid needless delay in data preparation. Define virtual data sets on source data. Verify and load to Amazon Redshift when required.

Snowflake Solution Brief

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Join Amazon Redshift with any source and create your data hub in minutes

Connect Amazon Redshift with your other data warehouses

Analyze your Amazon Redshift data with your BI tools instantly 

Expand Analytical Capabilities

Go beyond the limitations of data replication and democratize the data management. Add a Logical Data Warehouse across data sources and Amazon Redshift. Let data users define virtual data sets from data sources and tables loaded to Amazon Redshift.

Lyftron Logical Data Warehouse

Migrate step-by-step

Migration to Amazon Redshift may not be possible in one step. Connect legacy data warehouses as data sources and use the SQL Interface to access data from all data warehouses, also not migrated yet.

Use any BI Tool

Lyftron fully simulates SQL Server on the wire so you can use standard SQL Server drivers available in all BI tools and query Amazon Redshift or combine it with other data sources.

Pre-aggregate data

Define virtual data marts with aggregations, groupings and joins. Lyftron will materialize pre-aggregated views. Avoid full table scans of fact tables and let Lyftron use pre-aggregates that are 1000x smaller.

Create a data catalog

Democratize data access and let data users define data sets, describe them in the data catalog and share with others.

Apply transparent security

Transparently apply dynamic data masking, row access security or pseudonymization of data. Secure access to any data with Windows Integrated Security.

Join with any source

Join data between Amazon Redshift and other data sources in real-time. Replicate data from other data sources only when it is required for performance reasons.

Learn how Lyftron can help in analytics

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