Software Alternatives & Startups

Officially verified details DeltaForge

Delta Lake, without the cluster tax.

DeltaForge

DeltaForge Reviews and Details

This page is designed to help you find out whether DeltaForge is good and if it is the right choice for you.

Screenshots and images

  • Spark-verified Delta Lake & Iceberg reads and writes, no Spark, no JVM. //
    2026-06-20
  • GPU Accelerated Graph Analytics //
    2026-06-20
  • Landing page //
    2026-06-18

Features & Specs

  1. Delta Lake and Iceberg SQL

    Read and write open lakehouse tables with MERGE, UPDATE, DELETE, time travel, and maintenance, without a Spark cluster.

  2. Native BI Connectivity

    Connect Power BI, Tableau, Excel, Python, and other tools directly through ODBC and ADBC.

  3. GPU-Accelerated Graph Analytics

    Build property graphs over Delta tables, run Cypher and graph algorithms with GPU acceleration, and keep the data in the lakehouse.

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Questions & Answers

As answered by people managing DeltaForge.
  1. What makes DeltaForge unique?

    DeltaForge runs native SQL directly on Delta Lake and Apache Iceberg without the Spark cluster or JVM overhead. It keeps data in open tables you control, adds BI connectivity, GPU-accelerated graph analytics, and MCP support without creating another copy of the lakehouse.

  2. Why should a person choose DeltaForge over its competitors?

    Choose DeltaForge when you want full SQL writes, MERGE, time travel, and maintenance on open lakehouse tables without paying the operational cost of an always-on cluster. It is self-hosted, works with standard Delta and Iceberg data, and scales across workers only when the workload needs it.

  3. How would you describe the primary audience of DeltaForge?

    Data engineering, analytics, and platform teams running Delta Lake or Apache Iceberg who want direct SQL, Power BI connectivity, graph analytics, and AI integration while keeping data in their own cloud or datacenter.

  4. What's the story behind DeltaForge?

    Most lakehouse workloads are relational, selective, and metadata-heavy; they do not always need distributed-cluster coordination. DeltaForge was built for the middle ground: native agents run directly on open tables, with distributed execution available when it genuinely helps.

  5. Which are the primary technologies used for building DeltaForge?

    Rust and Apache Arrow for native, vectorized execution; Delta Lake and Apache Iceberg for open table storage; PostgreSQL-flavored SQL, ODBC and ADBC for connectivity; GPU acceleration for graph analytics; and MCP for AI-assistant integration.

Videos

Turn Any File Into a Databricks-Ready Delta Table, No Spark

Graph Analytics on the GPU | 10 Million Accounts, in Seconds | DeltaForge

Delta Lake Without Spark: ACID CRUD + Time Travel, Verified by Apache Spark | DeltaForge

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Is DeltaForge good? This is an informative page that will help you find out. Moreover, you can review and discuss DeltaForge here. The primary details have been verified within the last quarter. So they could be considered up to date. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.