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Enterprise Layer Intelligence
Categories/Data Platform & Integration/DataForge
DataForge

DataForge

Founded 2023

Build, orchestrate, and observe declarative data pipelines directly inside your cloud warehouse

Pricing model

Free Tier, Paid

You can use DataForge to build, orchestrate, and govern enterprise data pipelines across your Databricks or Snowflake environments. Instead of manually writing and maintaining complex DAGs and staging tables, it enforces a structured refinement architecture from raw data to final outputs. Teams can define business logic using standard SQL rules in a centralized catalog, trace data lineage automatically, and replicate database changes. It also connects with AI coding agents to inspect, validate, and safely deploy data transformations using natural language instructions.

What DataForge does

Define business logic rules using vanilla SQLReplicate change data from SQL Server to cloud storageValidate AI-generated data transformations before savingTrace data lineage from raw inputs to final reportsOrchestrate pipeline schedules without manual DAG writingMonitor pipeline execution history and cloud compute costsEnforces structured refinement layers across all batch and streaming pipelinesGenerates pipelines from business logic rules written in vanilla SQLReplicates SQL Server change data directly into destination tablesExecutes and processes workloads directly within Databricks or Snowflake environmentsConnects with AI coding assistants via MCP server with save-time validationTraces end-to-end lineage and audit trails for every calculated columnEliminates manual DAG configuration and dependency scripting

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
CommunityFree
Professional (per process)$2.50 / mo

Frequently asked

DatabricksDatabricksSnowflakeSnowflakeAWSAWSGoogle CloudGoogle CloudMicrosoft AzureMicrosoft AzurePower BIPower BI

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DataForge, data pipeline, data orchestration, data governance, Databricks, Snowflake, declarative data management, SQL transformations, data lineage, data observability, data replication, change data capture, cloud data warehouse, automated DAGs, agentic data engineering