From PostgreSQL to Databricks using AWS S3

Configure your LakeXpress data pipeline with the selected components

LX

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
FastBCP
FastBCPEngine
Terminal
./LakeXpress config create \
  -a data/ds_credentials.json \
  --log_db_auth_id log_db_ms \
  --source_db_auth_id datasource_postgresql_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 s3_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
Get LakeXpress

Source - PostgreSQL

PostgreSQL is a powerful and robust open-source relational database. LakeXpress optimizes extractions from PostgreSQL using FastBCP's native connector for excellent performance.

Features:

  • Direct streaming read from database
  • Full support for PostgreSQL data types
  • Secure SSL connections
  • Parallel extraction to Parquet

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 - Amazon Simple Storage Service

AWS S3 is the industry-leading object storage service. LakeXpress stages Parquet files in S3 for scalable, durable, and cost-effective cloud storage before publishing to your target platform.

Features:

  • Unlimited scalability
  • 99.999999999% durability
  • Lifecycle policies for cost optimization
  • Server-side encryption
  • Integration with AWS Glue Data Catalog

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