From MySQL to DuckLake using Azure Datalake Gen2

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 adls_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 ducklake_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 - Azure Data Lake Storage Gen2

ADLS Gen2 combines Azure Blob Storage with a hierarchical namespace. LakeXpress leverages ADLS for enterprise-grade data lake storage with optimized analytics performance.

Features:

•Hierarchical namespace for big data analytics
•Fine-grained access control with ACLs
•Optimized for Databricks and Fabric
•High-performance parallel access
•Native integration with Azure analytics services

Destination - DuckLake

DuckLake is an open lakehouse platform powered by DuckDB. LakeXpress stages Parquet files that DuckLake can access directly for fast analytical queries.

Publishing method:

DuckDB External Tables or COPY

Features:

•Open lakehouse architecture
•Native Parquet and Delta Lake support
•ACID transactions with DuckDB
•Fast local and remote queries
•No vendor lock-in

Related Pipelines

SQL Server → OneLake → DuckLake

MariaDB → OneLake → DuckLake

MySQL → GCS → DuckLake

Oracle → GCS → DuckLake

Oracle → OneLake → DuckLake

MySQL → S3 → MotherDuck