---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: bbd20fc11ca740230bfa36eb30790a17272621ec44c622c126c2bda8a7a1842b
  pageDirectory: concepts
  sources:
    - delta_duplicate_columns_found-error-condition-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - delta-lake-data-ingestion-duplicate-detection
    - DLDIDD
  citations:
    - file: delta_duplicate_columns_found-error-condition-databricks-on-aws.md
title: Delta Lake data ingestion duplicate detection
description: Sub-conditions under DELTA_DUPLICATE_COLUMNS_FOUND that detect duplicate columns during data ingestion operations, including DATA (saving data) and CONVERT_TO_DELTA (converting formats to Delta).
tags:
  - delta-lake
  - data-ingestion
  - validation
timestamp: "2026-06-19T18:24:33.085Z"
---

# Delta Lake Data Ingestion Duplicate Detection

**Delta Lake data ingestion duplicate detection** refers to the mechanisms by which Delta Lake identifies and reports duplicate column names during schema-related operations such as table creation, schema evolution, data writing, and table conversion. Detecting duplicate columns at ingestion time prevents ambiguous data access and ensures that the underlying Parquet files and Delta transaction log maintain a clean, unambiguous schema. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## How Duplicate Detection Works

Delta Lake raises the `DELTA_DUPLICATE_COLUMNS_FOUND` error whenever it encounters two or more columns with the same name in a given context. The error includes the SQLSTATE identifier `42711` (class 42: syntax error or access rule violation) and lists the duplicate column(s) found. The specific operation that triggered the detection is indicated by a nested sub‑condition, allowing users to pinpoint the source of the duplication. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## Common Scenarios (Sub‑Conditions)

The error can appear in many contexts during ingestion and schema modification. Each sub‑condition corresponds to a different phase or operation:

- **ADDING_COLUMNS** – Duplicate column names were present in a set of columns being added to an existing table, for example via `ALTER TABLE ADD COLUMNS` or during schema evolution on write. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **CLUSTER_BY** – Duplicate column names were specified in a `CLUSTER BY` clause of a `CREATE` or `REPLACE` statement. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **CONVERT_TO_DELTA** – The source data being converted to a Delta table (e.g., via `CONVERT TO DELTA`) contained duplicate columns in its schema. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **DATA** – The actual data being saved to the Delta table (for example, a DataFrame or CSV file) contained duplicate column names. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **EXISTING_SCHEMA** – The existing schema of the Delta table already had duplicate columns when an operation attempted to update metadata. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **PARTITION_COLUMNS** – Duplicate column names were specified in the partition column list, e.g., `PARTITIONED BY (col1, col1)`. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **PARTITION_SCHEMA** – The partition schema itself contained duplicate columns, typically when inferred from partitioned Parquet directories. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **READ_SCHEMA** – The schema of the data being read (e.g., a source file) had duplicate column names. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **REPLACING_COLUMNS** – Duplicate column names were present when replacing columns via `ALTER TABLE REPLACE COLUMNS` or write‑mode operations that overwrite the schema. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **SPECIFIED_COLUMNS** – A user-specified column list in a statement (such as `MERGE` or `INSERT`) contained duplicates. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]
- **TABLE_SCHEMA** – The table schema itself contained duplicate columns, possibly from a previous corruption or manual transaction log edit. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## Preventing Duplicate Column Errors

To avoid this error during ingestion:

- Inspect source data schemas before writing, especially when using schema inference from CSV, JSON, or Parquet files.
- Use `struct` specifications that explicitly define each column name once.
- When performing schema evolution with `mergeSchema` or `overwriteSchema`, ensure no duplicate column names are introduced.
- Validate partition columns and `CLUSTER BY` lists for repeated names.

If the error occurs, examine the error message’s sub‑condition to determine which operation caused the duplicate, then correct the schema or data source.

## Related Concepts

- [Delta Lake schema enforcement](/concepts/delta-table-schema-requirements.md) – How Delta Lake enforces that all writes conform to a table’s schema.
- [Delta Lake schema evolution](/concepts/delta-lake-schema-migration.md) – The process of updating a table’s schema automatically or manually.
- [Delta Lake Transaction Log](/concepts/delta-lake-transaction-log.md) – The commit log that records all schema changes and operations.
- Delta Lake error messages – Comprehensive list of Delta Lake errors and troubleshooting.
- Parquet schema inference – Automatic schema discovery from Parquet files, which can produce duplicate column names.

## Sources

- delta_duplicate_columns_found-error-condition-databricks-on-aws.md

# Citations

1. [delta_duplicate_columns_found-error-condition-databricks-on-aws.md](/references/delta_duplicate_columns_found-error-condition-databricks-on-aws-42950ac1.md)
