From MySQL to Databricks using Google Cloud Storage

Configure your LakeXpress data pipeline with the selected components

LakeXpress Orchestrator

Manages end-to-end pipelines
Orchestrates FastBCP extracts with retries & logging
Handles incremental sync and schema-aware metadata

Control DB for increments/custom rules & logsMetadata-drivenLinux or Windows

Data

FastBCPEngine

Parquet

Data or Link

Terminal

./LakeXpress config create \
  -a data/ds_credentials.json \
  --log_db_auth_id log_db_ms \
  --source_db_auth_id datasource_mysql_01 \
  --source_db_name sales \
  --source_schema_name "sales,dim" \
  --fastbcp_dir_path ./FastBCP_linux-x64/latest/ \
  --fastbcp_p 2 \
  --n_jobs 4 \
  --target_storage_id gcs_lake_prd \
  --generate_metadata \
  --sub_path /ingest/bronze \
  --incremental_table "sales.orders:o_orderdate:date" \
  --incremental_table "sales.lineitem:l_shipdate:date" \
  --publish_method internal \
  --publish_target databricks_tgt

# First sync - full load
./LakeXpress sync

# Subsequent syncs - incremental updates (much faster!)
./LakeXpress sync

Documentation

Get LakeXpress

Source - MySQL

MySQL is the world's most popular open-source database management system. LakeXpress uses FastBCP with the native MySQL driver to ensure fast and reliable extractions.

Features:

•Native MySQL driver for optimal performance
•Transaction support
•Compatible with all modern MySQL versions
•Efficient parallel extraction

Format - Apache Parquet

Parquet is the industry-standard columnar file format for analytics. LakeXpress uses FastBCP to extract data from source databases and convert it to Parquet format, ensuring optimal compression, query performance, and compatibility with all modern data platforms.

Advantages:

•Columnar format optimized for analytics
•Efficient compression (typically 3-10x)
•Schema evolution support
•Predicate pushdown for fast queries
•Universal compatibility with cloud platforms
•Preserves data types and precision

Cloud Stage - Google Cloud Storage

Google Cloud Storage offers unified object storage for developers and enterprises. LakeXpress stages Parquet files in GCS for seamless integration with Google Cloud data platforms.

Features:

•Global edge caching
•Multiple storage classes
•Strong consistency
•Integrated with BigQuery
•Automatic encryption at rest

Destination - Databricks

Databricks provides a unified analytics platform built on Apache Spark. LakeXpress publishes Parquet files as Delta Lake tables for optimal lakehouse performance.

Publishing method:

Delta Lake MERGE or COPY INTO

Features:

•Native Parquet and Delta Lake support
•ACID transactions
•Time travel and versioning
•Optimized for analytical queries
•Unity Catalog integration

Related Pipelines

SQL Server → ABS → Databricks

MySQL → S3 → Fabric

Oracle → ABS → Databricks

PostgreSQL → GCS → Snowflake

MySQL → ABS → BigQuery

MySQL → GCS → MotherDuck