Enterprise ETL Platform

Move data. Transform it.
Trust it.

A visual ETL platform with conversational AI. Connect any source, transform with natural language, and load to any destination — without the pipeline engineering overhead.

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Workflow

Workflows

Create and manage your data processing workflows

Customer Revenue Analysis

5 nodeslocalApr 21

Custom

Daily Order Sync

3 nodeslocalApr 20

Custom

Salesforce CRM Import

3 nodeslocalApr 18

Custom

Inventory Validation

4 nodeslocalApr 15

Custom

S3 to BigQuery Load

3 nodeslocalApr 10

Custom

Invoice Processing

3 nodeslocalMar 29

Custom

Data Source

PostgreSQL

customers · 45k rows

Data Source

MySQL

orders · 200k rows

Transform

Filter + Aggregate

last 90d · group by id

Join

Cross-DB Join

customer_id · LEFT

Output

BigQuery

analytics.customers

Trigger

Manual

Manual trigger

Data Source

MySQL

orders · 200k rows

Transform

Clean + Filter

remove nulls · last 24h

Output

Snowflake

warehouse.orders_daily

Trigger

Schedule

Every 4 hours

Data Source

Salesforce

contacts · via MCP

Transform

Normalize

deduplicate · lowercase

Output

PostgreSQL

crm.contacts_clean

Data Source

PostgreSQL

inventory · 12k rows

Transform

Aggregate

sum stock · by SKU

Branch

Stock Validation

stock > 0 · no nulls

Output

BigQuery

✓ Valid inventory

Output

Amazon S3

✗ Failed records

Data Source

Amazon S3

exports/*.parquet

Transform

Schema Map

type cast · rename cols

Output

BigQuery

dataset.events_raw

Data Source

Amazon S3

invoices/*.pdf · 847 files

Transform

AI Extract

vendor · total · date

Output

MongoDB

invoices.extracted

Connects to your existing stack

Workflow Builder

Visual pipelines, without the boilerplate

Drag nodes onto a canvas, connect them, and run. Dagflux handles execution order, error recovery, and schema detection — so you focus on the data logic, not the plumbing.

Drag-and-drop node canvas

Build complex multi-step pipelines visually. Source, Transform, Join, Branch, and Output nodes compose into complete ETL workflows.

Real-time execution tracking

Watch row counts, progress, and status update per node as the pipeline runs. Full error messages with suggested fixes.

Auto schema detection

Column types, nullability, and relationships inferred automatically across SQL, NoSQL, and file sources.

Pipeline execution log

Source: PostgreSQL✓ 45,231 rows · 1.2s

Transform: Filter+Agg✓ 12,847 rows · 0.8s

Join: Cross-DB⏳ batch 2/5 · 62%

Output: BigQueryPending

Progress2 of 4 nodes

Conversational AI

Describe the transformation. Get production SQL.

Tell Dagflux what you want in plain English. It writes optimized SQL, handles type conversions, and recovers from errors automatically — across any database dialect.

Natural language to SQL

Filters, aggregations, schema changes — describe once, get database-specific, optimized SQL instantly.

Automatic error recovery

Column not found? Type mismatch? Dagflux detects failures, explains them in plain English, and regenerates a fix.

Schema-aware context

The AI knows your table structures, column types, and naming conventions before generating any query.

Dagflux Assistant

You

Filter customers to only those who placed orders in the last 90 days and calculate their total revenue

Here's the transformation joined on customer_id:

SELECT c.customer_id, c.name, SUM(o.total) AS revenue FROM customers c JOIN orders o USING (customer_id) WHERE o.created_at >= NOW() - '90 days'::interval GROUP BY 1, 2 ORDER BY revenue DESC

You

Add order count and remove rows with null email

Updated — added COUNT(o.id) AS order_count and WHERE c.email IS NOT NULL. Running now…

20+Native connectors

5Node types — Source, Transform, Join, Branch, Output

100M+Rows processed with chunked batch execution

0Lines of SQL required to get started

Use cases

Built for data teams at every stage

From migration projects to production analytics pipelines, Dagflux handles the full data workflow lifecycle.

Analytics

Data warehouse pipelines

Move data from operational databases into BigQuery, Snowflake, or Redshift. Schema creation and type mapping automatic.

ETL Pipeline Builder →

AI / ML

AI-ready data preparation

Clean, normalize, and structure raw data for machine learning. Natural language handles deduplication and type standardization.

AI Data Transformation →

Operations

Cross-platform data sync

Join Salesforce CRM data with PostgreSQL order history. Run scheduled syncs with error handling and retries.

Pipeline Automation →

Text-to-SQL analytics

Ask questions about your data in plain English and get instant SQL-backed answers. No query writing required.

Text-to-SQL →

Documents

Unstructured data extraction

Extract structured fields from PDFs, invoices, and images. Load directly into your database of choice.

Unstructured Data →

Exploration

Conversational data Q&A

Chat with connected databases. Ask aggregate questions and get instant answers without opening a SQL client.

Chat With Your Data →

FAQ

Common questions

Dagflux is a visual ETL platform with a built-in conversational AI layer. Traditional ETL tools require engineers to write transformation code and manage infrastructure manually. Dagflux replaces that with a drag-and-drop node canvas and a natural language interface — you describe what you want, and the platform generates and executes the SQL against your actual schema.

Yes. Dagflux's Join node supports cross-database operations — combining PostgreSQL customer records with MySQL order history, or BigQuery analytics with MongoDB documents. For cross-database joins, Dagflux uses a local SQLite engine to execute the join, then routes the result to your destination.

When a transformation fails — due to a missing column, type mismatch, or syntax error — Dagflux automatically detects the error, explains it in plain English, and generates a corrected query. In most cases it re-executes the fix without user intervention.

PostgreSQL, MySQL, Microsoft SQL Server, Amazon Redshift, Google BigQuery, Snowflake, SQLite, MongoDB, Amazon S3, Azure Blob Storage, Google Cloud Storage, Google Sheets, Salesforce (via MCP), HubSpot (via MCP), and local files including CSV, JSON, Excel, Parquet, and Avro.

Yes. Dagflux includes a scheduling system supporting cron expressions and natural language ("daily at 6 AM"). Each run logs execution time, row counts, and any errors — with optional Slack or email alerts on failure.

Start building data pipelines today

Connect your first data source in minutes. No infrastructure to manage, no pipeline code to write.

Start for free Book a demo Download desktop app

Move data. Transform it.Trust it.

Workflows

Visual pipelines, without the boilerplate

Drag-and-drop node canvas

Real-time execution tracking

Auto schema detection

Describe the transformation. Get production SQL.

Natural language to SQL

Automatic error recovery

Schema-aware context

Built for data teams at every stage

Data warehouse pipelines

AI-ready data preparation

Cross-platform data sync

Text-to-SQL analytics

Unstructured data extraction

Conversational data Q&A

Common questions

Start building data pipelines today

Move data. Transform it.
Trust it.