---
title: Data ingestion
meta:
  description: Common issues and solutions for data ingestion problems in ClickHouse and Tinybird.
---

# Data ingestion

Common issues and solutions for data ingestion problems in ClickHouse and Tinybird.

## Overview

This section covers troubleshooting for data ingestion issues, including format problems, parsing errors, and data validation issues.

## Ingestion categories

### JSON formatting

Common JSON ingestion issues:

- **CANNOT_PARSE_TEXT** - Malformed JSON data
- **UNKNOWN_TYPE** - ClickHouse can't infer types from JSON
- **Nested JSON objects** - Complex nested structure handling
- **Array handling** - JSON arrays causing parsing issues

[View JSON formatting troubleshooting →](/sql-reference/ingestion/json-formatting)

### CSV type issues

Common CSV ingestion issues:

- **CANNOT_PARSE_TEXT** - Malformed CSV data or wrong delimiter
- **TYPE_MISMATCH** - Non-numeric data in numeric columns
- **Missing headers** - CSV files without column headers
- **Inconsistent delimiters** - Mixed delimiters in CSV files

[View CSV type issues troubleshooting →](/sql-reference/ingestion/csv-type-issues)

### Cannot parse date

Common date parsing issues:

- **CANNOT_PARSE_DATE** - Date strings in unexpected format
- **CANNOT_PARSE_DATETIME** - DateTime strings in unexpected format
- **Timezone issues** - Dates with timezone information
- **Mixed date formats** - Same column with different date formats

[View cannot parse date troubleshooting →](/sql-reference/ingestion/cannot-parse-date)

### Unexpected null

Common null value issues:

- **Missing data in source** - Source data with missing values
- **Schema inference issues** - ClickHouse inferring nulls from sample data
- **Data validation** - Checking for null values in data
- **Handling strategies** - Using COALESCE and CASE statements

[View unexpected null troubleshooting →](/sql-reference/ingestion/unexpected-null)

## Common patterns

### Data validation

Strategies for validating ingested data:

1. **Check data formats** - Validate JSON, CSV, and other formats
2. **Verify data types** - Ensure data matches expected types
3. **Handle missing data** - Provide defaults for missing values
4. **Monitor ingestion** - Track ingestion success and failure rates

### Schema handling

Best practices for schema design during ingestion:

1. **Use explicit schemas** - Don't rely on automatic inference
2. **Handle mixed types** - Use string types for mixed data
3. **Provide defaults** - Use default values for missing data
4. **Validate early** - Check data quality during ingestion

## Best practices

1. **Validate data formats** - Check JSON, CSV, and other formats before ingestion
2. **Use explicit schemas** - Specify expected types in schema
3. **Handle missing data** - Provide appropriate defaults
4. **Monitor ingestion quality** - Track parsing errors and data quality
5. **Document data sources** - Keep track of data source characteristics

## Related documentation

- [Data source configuration](/forward/core-concepts/data-sources)
- [Data types](/sql-reference/data-types)
- [ClickHouse errors](/sql-reference/clickhouse-errors)