That's what our advanced ETL/ELT solution have under the hood

On-premise and cloud availability

Runs on any Kubernetes cluster on-premises or cloud, ensuring multi-cloud compatibility

Spark processing engine

The power of the Spark processing engine available with no single line of code

Stage palette with all main operators features

Stage palette featuring tens discrete data transformation functions

Unlimited scalability

Virtually unlimited horizontal scalability

External invoking of pipelines

Ability to externally invoke Visual Flow pipelines using POST requests

Deployment on any Kubernetes cluster

Ability to deploy your ETL/ELT on any Kubernetes cluster anywhere, once designed

Visual Flow benefits that our users highlight

Saving money leveraging open source solution

The migration to Visual Flow allowed us significantly reduce maintenance cost.
During the development process, it was necessary to add new features to Visual Flow to satisfy client’s business needs. Finally, it was implemented successfully, what allowed us fully migrate the existing ETL process to Visual Flow.

CTO of IT company

Improved productivity and scalability

Visual Flow team helps us in supporting clinical and medical research to empower clients’ healthcare capabilities and to provide cutting-edge services to patients all around the world. We created new frontend for easy mapping on the base of Visual Flow. Now its GUI allows client’s business users to aggregate data from multiple sources, integrate it and prepare for further analysis.

CTO of US based IT company

Unlimited scalability

With Visual Flow my team is successfully working on processing permanently growing volumes of data due to unlimited scalability and parallel processing capabilities of containerized Spark jobs.

Lead ETL Developer

Saving money leveraging open source solution

The migration to Visual Flow allowed us significantly reduce maintenance cost.
During the development process, it was necessary to add new features to Visual Flow to satisfy client’s business needs. Finally, it was implemented successfully, what allowed us fully migrate the existing ETL process to Visual Flow.

CTO of IT company

Improved productivity and scalability

Visual Flow team helps us in supporting clinical and medical research to empower clients’ healthcare capabilities and to provide cutting-edge services to patients all around the world. We created new frontend for easy mapping on the base of Visual Flow. Now its GUI allows client’s business users to aggregate data from multiple sources, integrate it and prepare for further analysis.

CTO of US based IT company

Unlimited scalability

With Visual Flow my team is successfully working on processing permanently growing volumes of data due to unlimited scalability and parallel processing capabilities of containerized Spark jobs.

Lead ETL Developer

Seems like a Visual Flow is quite useful… And lovable:) Let’s try Visual Flow
The roadmap of your Visual Flow experiences

Visual Flow is a low-cost, low-code, open-source product. Its portability, flexibility, and multi-cloud capability 'makes it the most powerful of all ETL solutions for businesses

Load data from transactional sources into the data lake.
Load application logs and structured files from a cloud object storage to the data lake.
Load application logs and structured files from a cloud object storage to the data lake.
Load data from the data lake into the data warehouse.
Cleanse and transform information inside the data lake.
Transform the data to fulfill the needs of BI tools.
Transform the data within the data warehouse to fulfill the needs of data analytics tools.
Transform the data to fulfill ML and Science needs.
Load data from transactional sources into the data lake.
Load application logs and structured files from a cloud object storage to the data lake.
Load application logs and structured files from a cloud object storage to the data lake.
Load data from the data lake into the data warehouse.
Cleanse and transform information inside the data lake.
Transform the data to fulfill the needs of BI tools.
Transform the data within the data warehouse to fulfill the needs of data analytics tools.
Transform the data to fulfill ML and Science needs.