Centralized & Decentralized Data Management
datAtlas can be used either to transfer data to a centralized platform such as a data warehouse or a data lake or to dispatch data to different teams and services inside a decentralized platform such as a data mesh (exposing data as a product that can be consumed by different teams)
Hybrid cloud integration
Today's organizations store their data both in public clouds and in on-premise clouds due to data privacy and security. datAtlas is able to consolidate data from and to both clouds
On-Premise Solution
datAtlas solution is very clear in its internal architecture that separates the data plane from the control plane, so the only actors who have access to the data are data owners and the destinations to whom it is sent. That's why datAtlas provides an on-premise version of its product, so you can install it on your infrastructure and begin synchronizing your data without any external interference.
Edge computing
datAtlas supports ETL processes which means that you can filter and aggregate data on sources before sending it to your data warehouses and data lakes.
Cost-effective
Filtering and aggregating data on the edge allow you to optimize the data traffic sent to the destination, so you can pay less to cloud providers as they all have a pay-as-you-go pricing model.
Green
With its based edge computing model, datAtlas allows you to pull only the data your data teams need. No more non-useful data resides in your data warehouse!
Code-free
intuitive easy-to-use data management solution - no hided code or configurations to do from user side
Separated control plane & data plane
The data plane is where the data is collected and processed. The control plane is what controls the data plane, it orchestrates the data pipelines. datAtlas provides you an intuitive console to control your data flows and provides you, agents, that you launch on your own servers to process the data transfers. In this way, datAtlas has no access to your data!
Large data volumes
datAtlas is designed to support petabytes of data per hour
Pricing model
You pay a flat fee per month based on how many data pipelines you need to run so the price won't go up if you collect more data or if you have more users. Compared to competitors, who charge with a pay-as-you-use or pay-as-you-go model. datAtlas pricing model is not only more predictable but costs organizations less overall. This model is based on predictability to make your data teams more comfortable with data pipelines and less worried about the budgets