Lift ‘n’ shift data from hundreds of on-premise tables to the cloud in a single day.

antFarm is a lift ‘n’ shift data migration solution that supports both cloud and on-premises data sources. For this reason, it is opening paths to IT modernization by bringing in various benefits, including reduced costs, improved performance and the resiliency of cloud architecture.

antFarm solves many challenges of quickly reading data sources and generating files that are optimized for the fastest data load.

Why have we developed antFarm?

Most vendors are focused on cloud-to-cloud data synchronization. Then again, a lot of the enterprise data workloads (90%) are still on-premises. Extracting data from on-premises sources is addressed with many challenges, such as:

  • Limited and short time frames in which data can be accessed and extracted because operational systems are performance sensitive.
  • On-premises applications generate enormous quantities of data on a daily or even hourly basis
  • Each data source has its own specifics, e.g. IBM DB2 mainframe, SAP Hana, Microsoft SQL Server.
  • Security related challenges for accessing the data, e.g. VPN are very common.

Benefits and Key Features

Easy to use

Whole data movement processing is defined with standard SQL syntax.

Bulk loads

Data is imported into the destination in batches.


Whole data movement processing is defined with standard SQL syntax.


Detailed configurable logging and reporting are available out-of-the box.

Serial execution

Streaming for processing and synchronization.


Target tables, data types conversion and ETL queries are automatically generated.

Completely open solution

antFarm can be integrated into any data integration tool.

Any custom Process

You can run any kind of SQL or Python processes (operations).

Data Sources

Data Warehouse Destinations

We’re constantly growing the list of supported data sources and target destinations. If you need to access data from a source that isn’t currently supported, please get in touch. antFarm can be easily extended.

Use Cases

  • Implementing extract / staging area of the data warehouse
  • Replicating data from the legacy data warehouse to the cloud in order to reduce on-premises infrastructure costs and enjoy compelling performance of complex analytical queries
  • Replicating data from operational systems to analyse data near real time without any negative performance influences on the transactional application
  • Updating development environment with data from a testing or production environment
  • Importing data from CSV files to the database
  • Using it as a backbone for Snowflake POC projects