How to Migrate from MySQL to BigQuery

How do I migrate data from MySQL to BigQuery without losing schema or data integrity?

Move tables, schema, and data from a MySQL database into Google BigQuery using export-and-load or direct transfer commands.

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Description

Why migrate MySQL data to BigQuery?

Teams move OLTP data into BigQuery to run large analytical queries, join with other sources, and leverage Google’s serverless compute.BigQuery scales reads without the operational overhead of sharding MySQL.

What are the high-level steps?

1) Export MySQL tables to Cloud Storage.
2) Create matching BigQuery datasets and tables.
3) Load the files with bq load or schedule a BigQuery Data Transfer Service (DTS).
4) Validate row counts and column types.

How do I export MySQL tables?

Use mysqldump --tab for delimited data or SELECT ... INTO OUTFILE.Save each table as a CSV or newline-delimited JSON file in Cloud Storage.

Example export

mysqldump -u root -p ecommerce \ --tables Customers Orders Products OrderItems \ --tab=/tmp/ecommerce_export \ --fields-terminated-by="," --fields-enclosed-by="\""

How do I create BigQuery tables?

Define a dataset, then create tables matching MySQL schema.Use SQL DDL or auto-detect during load.

Dataset creation

bq mk --location=US ecommerce_raw

Table DDL (Customers)

CREATE TABLE `ecommerce_raw.Customers` ( id INT64 NOT NULL, name STRING, email STRING, created_at TIMESTAMP);

How do I load CSV files?

Run bq load for each table or script the process. Include field delimiter, skip header rows, and source format.

Example load

bq load --source_format=CSV --field_delimiter="," \ --skip_leading_rows=1 \ ecommerce_raw.Customers \ gs://my-bucket/Customers.csv \ id:INT64,name:STRING,email:STRING,created_at:TIMESTAMP

Can I automate repeated loads?

Use BigQuery DTS for Cloud SQL or Cloud Storage.DTS monitors new files and ingests them on a schedule, keeping BigQuery in sync without custom code.

How do I validate the migration?

Compare counts and sample rows. Run SELECT COUNT(*) FROM in both systems.Use checksums or hashed concatenations of key columns for extra assurance.

Best practices for production

• Export to GZip-compressed CSV to cut transfer costs.
• Use partitioned tables in BigQuery for time-based data.
• Convert MySQL DATETIME to BigQuery TIMESTAMP.
• Grant least-privilege IAM roles.

What are common pitfalls?

Incorrect encoding, mismatched null handling, and default string lengths cause load failures. Always set --encoding=UTF-8 and explicit schemas.

Why How to Migrate from MySQL to BigQuery is important

How to Migrate from MySQL to BigQuery Example Usage


-- Row-count validation after import
SELECT
  'Customers'   AS table_name,
  (SELECT COUNT(*) FROM ecommerce_raw.Customers) AS bigquery_rows,
  (SELECT COUNT(*) FROM my_mysql.customers)     AS mysql_rows;

How to Migrate from MySQL to BigQuery Syntax


# Export tables from MySQL
mysqldump -u USER -p DB_NAME --tables TABLE_LIST \
  --tab=/path/to/export \
  --fields-terminated-by="," \
  --fields-enclosed-by="\"" \
  --lines-terminated-by="\n"

# Upload to Cloud Storage
gsutil cp /path/to/export/*.csv gs://BUCKET_NAME/

# Create BigQuery dataset
bq mk --location=LOCATION DATASET_NAME

# Load CSV into BigQuery
bq load \
  --source_format=CSV \
  --field_delimiter="," \
  --skip_leading_rows=1 \
  --encoding=UTF-8 \
  DATASET.TABLE \
  gs://BUCKET_NAME/FILE.csv \
  id:INT64,name:STRING,email:STRING,created_at:TIMESTAMP

# Schedule recurring loads (Cloud Storage trigger)
bq mk --transfer_config \
  --data_source=google_cloud_storage \
  --target_dataset=DATASET \
  --display_name="MySQL Daily Loads" \
  --params='{"data_path_template":"gs://BUCKET/*.csv","file_format":"CSV"}'

Common Mistakes

Skipping explicit schemas and relying on auto-detect causes STRING columns where NUMERIC or TIMESTAMP are required. Always supply a detailed schema in the <code>bq load</code> command.
Forgeting to convert MySQL DATETIME to UTC timestamps leads to shifted analytics. Normalize dates in the export step or use BigQuery's <code>SAFE.PARSE_TIMESTAMP</code>.