That's what our advanced ETL/ELT solution have under the hood
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On-premise and cloud availability

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

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Spark processing engine

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

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Stage palette with all main operators features

Stage palette featuring tens discrete data transformation functions

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Unlimited scalability

Virtually unlimited horizontal scalability

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External invoking of pipelines

Ability to externally invoke Visual Flow pipelines using POST requests

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Deployment on any Kubernetes cluster

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

Visual Flow benefits that our Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-28 at Visual Flow users highlight

Saving money leveraging open source solution
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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
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-32 at Visual Flow

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
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-34 at Visual Flow

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

Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-35 at Visual Flow
Saving money leveraging open source solution
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-36 at Visual Flow
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-37 at Visual Flow

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

Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-38 at Visual Flow
Improved productivity and scalability
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-39 at Visual Flow
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-40 at Visual Flow

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

Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-41 at Visual Flow
Unlimited scalability
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-42 at Visual Flow
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-43 at Visual Flow

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 Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-45 at Visual Flow 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.
Users create jobs and pipelines in Visual Flow UI.
The jobs and pipelines are stored as configmaps and workflow templates in the Kubernetes namespace.
Users trigger pipelines manually or define run schedules.
Visual Flow creates an instance of each workflow from a template.
Argo lunches each workflow in a separate pod. Spark Jobs are executed as tasks in separate pods as well.
Argo sends a notification to the Slack channel if any of the tasks fail.
Users create jobs and pipelines in Visual Flow UI.
The jobs and pipelines are stored as configmaps and workflow templates in the Kubernetes namespace.
Users trigger pipelines manually or define run schedules.
Visual Flow creates an instance of each workflow from a template.
Argo lunches each workflow in a separate pod. Spark Jobs are executed as tasks in separate pods as well.
Argo sends a notification to the Slack channel if any of the tasks fail.
Just draw the data flow and go!
Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-60 at Visual Flow
DB2
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IBM COS
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AWS S3
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Elastic Search
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PostgreSQL
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Maria DB
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MySQL
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MS SQL
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ClickHouse
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Oracle
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Cassandra
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Mongo
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Redis
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Redshift
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Kafka
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Azure Blob Storage
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Google Cloud Storage
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Delta Lake
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Parquet
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ORC
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Avro
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API based
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JSON File
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CSV File
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Text File
Guiding principles

We combine the most powerful technologies and put them under the Set up data pipelines in  15 minutes without single line of code no code ETL solution - img-85 at Visual Flow drag-and-drop interface of the Visual Flow ETL solution platform to simplify complex development while keeping engineering excellence

Simplification for the sake of increased productivity

Creative freedom: use Visual Flow with no code, low-code or custom code

Ripping all benefits of the best open-source solutions

Scaling at the pace of the data — not your human resources

P.S. We know what can make you cringe in the ETL/ELT process and take care about your nervous cells Let’s try Visual Flow

Frequently asked questions

Is Visual Flow software free?

Absolutely! Visual Flow low code data engineering is designed for scalability and runs natively within Kubernetes and Apache Spark.

How is Visual Flow conceptually different from its competitors?
What if the functionality of Visual Flow is not enough for my project?
What if the data source that we need is not listed now?
How can I migrate my ETL jobs coded on Spark or designed with other proprietary solutions to Visual Flow?
What skills should a Visual Flow user have?
Does Visual Flow support Pyspark?
Is Visual Flow scalable?
What should I do if an error occurs in my data pipelines?
How can I run Visual Flow on my laptop?

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