
Trino
Fast distributed SQL query engine for big data analytics.

The all-in-one low-code data workspace for integration, transformation, and AI automation.

Datagran represents a paradigm shift in the modern data stack by unifying the fragmented layers of ETL, data warehousing, machine learning, and Reverse ETL into a single, low-code visual environment. Designed for the 2026 data landscape, Datagran's architecture allows data engineers and business analysts to collaborate on high-velocity data pipelines. Its core engine supports complex SQL and Python operators within a drag-and-drop workflow, enabling the deployment of predictive models (like churn or LTV) directly into production without infrastructure overhead. Unlike traditional CDPs that focus purely on storage, Datagran emphasizes 'Actionable Data'—moving beyond ingestion to ensure that processed insights are immediately pushed into operational tools like Salesforce, Slack, and Google Ads. This approach drastically reduces the time-to-value for machine learning initiatives, positioning Datagran as a critical hub for companies that require real-time data orchestration and automated decision-making. The platform's scalability is rooted in its modular 'Operator' system, which allows for custom-built integrations and proprietary ML models to be embedded into any data flow.
Datagran represents a paradigm shift in the modern data stack by unifying the fragmented layers of ETL, data warehousing, machine learning, and Reverse ETL into a single, low-code visual environment.
Explore all tools that specialize in reverse etl. This domain focus ensures Datagran delivers optimized results for this specific requirement.
Explore all tools that specialize in integrate data sources. This domain focus ensures Datagran delivers optimized results for this specific requirement.
Explore all tools that specialize in build predictive models. This domain focus ensures Datagran delivers optimized results for this specific requirement.
Explore all tools that specialize in orchestrate data workflows. This domain focus ensures Datagran delivers optimized results for this specific requirement.
Ability to join data from SQL databases, NoSQL, and SaaS APIs within a single visual operator.
Embedded IDE for running Pandas, NumPy, and Scikit-learn directly within the data flow.
Pre-trained algorithms for churn, LTV, and segmentation that can be dragged into any pipeline.
Syncs processed data back into 100+ business applications like CRM and Ad platforms.
Processes only new or changed data using delta-tracking mechanisms to optimize compute costs.
Git-style versioning for workflows, allowing users to roll back to previous pipeline states.
A UI layer that generates complex SQL queries through a drag-and-drop interface.
Create a Datagran account and establish a new Workspace.
Connect primary data sources (e.g., PostgreSQL, Shopify, or Google Analytics) via native connectors.
Define a Data Warehouse destination (e.g., BigQuery or Snowflake) for centralized storage.
Use the Visual Canvas to drag and drop 'Operators' for data cleaning.
Write custom SQL within an operator to join data across multiple disparate sources.
Insert a Python operator to run custom logic or feature engineering for ML.
Attach a pre-built ML model operator for tasks like Clustering or Regression.
Set up 'Action' operators to push output to destinations like HubSpot or Facebook Ads.
Configure the scheduler (Hourly/Daily/Monthly) or set up a Webhook trigger.
Run a test workflow and monitor data throughput in the Live Dashboard.
All Set
Ready to go
Verified feedback from other users.
"Users highly value the combination of ETL and ML in one place, though some note the learning curve for advanced Python operators."
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Fast distributed SQL query engine for big data analytics.

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