BigQuery Data Export

BigQuery Data Export

Use this skill when exporting data from BigQuery to Cloud Storage or local files.

Basic Extract Command

bq extract \
  --location=LOCATION \
  --destination_format=FORMAT \
  --compression=COMPRESSION \
  PROJECT:DATASET.TABLE \
  gs://bucket/​file.ext

Extract to CSV

Basic CSV Export

bq extract \
  --destination_format=CSV \
  --print_header=true \
  dataset.table \
  gs://bucket/​export.csv

CSV with Options

bq extract \
  --destination_format=CSV \
  --compression=GZIP \
  --field_delimiter=',' \
  --print_header=true \
  dataset.table \
  gs://bucket/​export.csv.gz

CSV flags:

• --field_delimiter=',' - Column separator
• --print_header=true/​false - Include header row

Tab-Delimited Export

bq extract \
  --destination_format=CSV \
  --field_delimiter=$'\t' \
  dataset.table \
  gs://bucket/​export.tsv

Extract to JSON

Newline-Delimited JSON

bq extract \
  --destination_format=NEWLINE_DELIMITED_JSON \
  --compression=GZIP \
  dataset.table \
  gs://bucket/​export.json.gz

Output format:

{"id": 1, "name": "Alice", "amount": 100.50}
{"id": 2, "name": "Bob", "amount": 250.75}

Extract to Avro

bq extract \
  --destination_format=AVRO \
  --compression=SNAPPY \
  dataset.table \
  gs://bucket/​export.avro

Benefits:

• Preserves schema
• Efficient binary format
• Fast re-import to BigQuery

Extract to Parquet

bq extract \
  --destination_format=PARQUET \
  dataset.table \
  gs://bucket/​export.parquet

Benefits:

• Columnar format
• Good compression
• Compatible with many analytics tools

Compression Options

Available compression:

• GZIP - Good compression, slower (CSV, JSON, Avro)
• SNAPPY - Fast, moderate compression (Avro, Parquet)
• DEFLATE - Similar to GZIP (Avro)
• NONE - No compression (fastest)

Example:

bq extract \
  --destination_format=CSV \
  --compression=GZIP \
  dataset.table \
  gs://bucket/​export.csv.gz

Large Table Exports

Using Wildcards (>1 GB)

BigQuery limitation: 1 GB per file

Solution: Use wildcard in destination

bq extract \
  --destination_format=CSV \
  dataset.large_table \
  'gs://bucket/​export-*.csv'

Output:

gs://bucket/​export-000000000000.csv
gs://bucket/​export-000000000001.csv
gs://bucket/​export-000000000002.csv
...

Shard Pattern

# Create sharded exports
bq extract \
  --destination_format=AVRO \
  dataset.large_table \
  'gs://bucket/​shard/​data-*.avro'

Note: Number of files depends on data size, not configurable.

Export Specific Partitions

Single Partition

# Export 2024-01-15 partition only
bq extract \
  --destination_format=CSV \
  dataset.partitioned_table\$20240115 \
  gs://bucket/​export_20240115.csv

Date Range (use WHERE in EXPORT DATA)

See EXPORT DATA section below.

EXPORT DATA SQL Statement

Basic EXPORT DATA

EXPORT DATA OPTIONS(
  uri='gs://bucket/​export-*.csv',
  format='CSV',
  overwrite=true,
  header=true,
  field_delimiter=','
) AS
SELECT * FROM `project.dataset.table`
WHERE date >= '2024-01-01';

Export Query Results

EXPORT DATA OPTIONS(
  uri='gs://bucket/​aggregated-*.parquet',
  format='PARQUET',
  overwrite=true
) AS
SELECT
  customer_id,
  DATE(order_timestamp) as order_date,
  SUM(amount) as total_amount,
  COUNT(*) as order_count
FROM `project.dataset.orders`
WHERE DATE(order_timestamp) >= '2024-01-01'
GROUP BY customer_id, order_date;

Format Options

CSV:

EXPORT DATA OPTIONS(
  uri='gs://bucket/​*.csv',
  format='CSV',
  header=true,
  field_delimiter=',',
  compression='GZIP'
) AS SELECT ...;

JSON:

EXPORT DATA OPTIONS(
  uri='gs://bucket/​*.json',
  format='JSON',
  compression='GZIP'
) AS SELECT ...;

Avro:

EXPORT DATA OPTIONS(
  uri='gs://bucket/​*.avro',
  format='AVRO',
  compression='SNAPPY'
) AS SELECT ...;

Parquet:

EXPORT DATA OPTIONS(
  uri='gs://bucket/​*.parquet',
  format='PARQUET'
) AS SELECT ...;

Export to Local Files (Not Recommended)

Small Results via Query

# For small datasets only
bq query \
  --format=csv \
  --max_rows=10000 \
  --use_legacy_sql=false \
  'SELECT * FROM `project.dataset.table` LIMIT 10000' \
  > local_export.csv

Limitation: Not suitable for large datasets. Use GCS for production.

Export Scheduled (Automation)

Using Cloud Scheduler + EXPORT DATA

# Create scheduled query
bq mk --transfer_config \
  --target_dataset=dataset \
  --display_name='Daily Export' \
  --schedule='every 24 hours' \
  --params='{"query":"EXPORT DATA OPTIONS(uri='\''gs://bucket/​daily-*.csv'\'', format='\''CSV'\'') AS SELECT * FROM dataset.table WHERE date = CURRENT_DATE()"}' \
  --data_source=scheduled_query

Using Cloud Composer (Airflow)

from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator

export_task = BigQueryInsertJobOperator(
    task_id='export_to_gcs',
    configuration={
        'extract': {
            'sourceTable': {
                'projectId': 'project',
                'datasetId': 'dataset',
                'tableId': 'table'
            },
            'destinationUris': ['gs://bucket/​export-*.csv'],
            'destinationFormat': 'CSV'
        }
    }
)

Monitoring Exports

Check Extract Jobs

bq ls --jobs --max_results=10

Job Details

bq show -j JOB_ID

Failed Exports

SELECT
  job_id,
  user_email,
  error_result.message as error_message,
  creation_time
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE
  job_type = 'EXTRACT'
  AND state = 'DONE'
  AND error_result IS NOT NULL
  AND creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
ORDER BY creation_time DESC;

Export Best Practices

Format Selection

CSV:

• ✅ Human-readable
• ✅ Universal compatibility
• ❌ Larger file size
• ❌ No schema preservation

JSON:

• ✅ Human-readable
• ✅ Preserves nested structures
• ❌ Larger file size

Avro:

• ✅ Preserves schema
• ✅ Efficient binary format
• ✅ Fast BigQuery re-import
• ❌ Not human-readable

Parquet:

• ✅ Columnar format
• ✅ Good compression
• ✅ Analytics tool compatible
• ❌ Not human-readable

Compression Recommendations

For long-term storage: GZIP (best compression) For processing pipelines: SNAPPY (fast) For network transfer: GZIP (smaller size) For speed: NONE (no compression overhead)

Wildcards for Large Exports

Always use wildcards for:

• Tables >500 MB
• Unknown data size
• Distributed processing

Example:

bq extract dataset.large_table 'gs://bucket/​export-*.avro'

Cost Considerations

Export Costs

• BigQuery extract: FREE
• GCS storage: Standard GCS pricing
• Network egress: Free within same region

Optimization

Reduce costs:

• Export only needed columns (use EXPORT DATA with SELECT)
• Filter rows before export (WHERE clause)
• Use compression (smaller files)
• Export to GCS in same region as BigQuery

Example - filtered export:

EXPORT DATA OPTIONS(
  uri='gs://bucket/​*.parquet',
  format='PARQUET'
) AS
SELECT customer_id, order_date, amount  -- Only needed columns
FROM `project.dataset.orders`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)  -- Last 30 days only
  AND amount > 0;  -- Filter out zero amounts

Common Patterns

Daily Export

#!/​bin/​bash
DATE=$(date +%Y%m%d)
bq extract \
  --destination_format=CSV \
  --compression=GZIP \
  dataset.table\$$DATE \
  gs://bucket/​exports/​daily_export_$DATE.csv.gz

Incremental Export

-- Create temp table with new data
CREATE TEMP TABLE new_data AS
SELECT * FROM `project.dataset.table`
WHERE updated_at >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR);

-- Export only new data
EXPORT DATA OPTIONS(
  uri='gs://bucket/​incremental/​data-*.parquet',
  format='PARQUET'
) AS
SELECT * FROM new_data;

Export with Transformation

EXPORT DATA OPTIONS(
  uri='gs://bucket/​transformed-*.csv',
  format='CSV'
) AS
SELECT
  customer_id,
  UPPER(customer_name) as customer_name,
  ROUND(amount, 2) as amount,
  FORMAT_DATE('%Y-%m-%d', order_date) as order_date
FROM `project.dataset.orders`
WHERE order_date >= '2024-01-01';

Troubleshooting

"Permission denied"

Problem: No write access to GCS bucket Solution: Grant BigQuery service account Storage Object Creator role

"Table too large"

Problem: Export exceeds 1GB without wildcard Solution: Use wildcard pattern gs://bucket/​export-*.csv

"Invalid URI"

Problem: Incorrect GCS path format Solution: Use gs://bucket/​path/​file format, not https://

"Quota exceeded"

Problem: Too many extract jobs Solution: Batch exports or increase quota

Quick Reference

Format recommendations:

• Re-import to BigQuery → Avro
• Analytics tools → Parquet
• Data exchange → CSV
• API consumption → JSON

Compression guide:

• Best ratio → GZIP
• Fastest → SNAPPY or NONE
• Balance → SNAPPY

Size limits:

• 1 GB per file (use wildcards)
• 10 TB per extract job
• 50,000 URIs per export

Syntax patterns:

# Single file
gs://bucket/​file.csv

# Wildcard (recommended)
'gs://bucket/​prefix-*.csv'

# Sharded with path
'gs://bucket/​path/​to/​shard-*.parquet'