[Feature Request]: Iceberg Input&Output as standard component

### What would you like to happen?

Feature Request: Support for Apache Iceberg Format in ETL Tool

Overview

We're requesting support for the Apache Iceberg table format within the ETL tool. Specifically, this would include:

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  Input components to read from Iceberg tables

  
  


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  Output components to write to Iceberg tables

  
  


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  Support for connecting to a meta-repository (e.g. Hive Metastore, AWS Glue, REST Catalog)

  
  

This would enable teams to build modern, scalable data pipelines using Iceberg, all within a low-code interface.

Why Iceberg?

Apache Iceberg is gaining traction as a core component in modern data lakehouse architectures. It brings many of the benefits of data warehouses—like ACID transactions and schema management—to cloud storage.

Key reasons to consider Iceberg:

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  Schema and Partition Evolution: Modify table structures without rewriting entire datasets.

  
  


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  Time Travel: Easily query previous versions of your data.

  
  


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  ACID Transactions: Safe concurrent reads/writes without corrupting data.

  
  


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  Performance: Works well with large-scale datasets thanks to partition pruning, metadata filtering, and columnar reads.

  
  


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  Open and Engine-Agnostic: Supported by Spark, Flink, Trino, Presto, and others.

  
  

Why It Matters for a Low-Code ETL Tool

Adding native Iceberg support would:

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  Enable Non-Engineers: Business and data analysts could work with Iceberg tables without writing Spark jobs or scripts.

  
  


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  Streamline Workflows: Build end-to-end data flows from source to Iceberg without leaving the ETL tool.

  
  


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  Reduce Duplication: Avoid needing to copy Iceberg data into other formats or systems just to make it usable in the pipeline.

  
  


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  Stay Relevant: Many organizations are shifting away from traditional data lakes to Iceberg-based lakehouses. Native support ensures the ETL tool stays compatible with current trends.

  
  

Suggested Components

Final Thoughts

Iceberg is becoming a critical part of the modern data stack. Adding it to the ETL tool—especially in a low-code way—would open up new use cases and attract teams working on modern, scalable architectures.

Let us know if you'd like help drafting a more detailed spec or use case doc.

### Issue Priority

Priority: 3

### Issue Component

Component: Metadata

No files, tests, or entry points are identified in the issue, so begin by locating the ETL tool's existing input, output, and metadata components. Define the scope for Iceberg table reads, writes or upserts, and catalog connections before implementation; done should include working Iceberg Input, Iceberg Output, and metadata connectivity.

Written by the indexing model from the issue text.