From Teradata to BigQuery 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

./LakeXpress config create \
-a data/ds_credentials.json \
--log_db_auth_id log_db_ms \
--source_db_auth_id datasource_teradata_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 bigquery_tgt
# First sync - full load
./LakeXpress sync
# Subsequent syncs - incremental updates (much faster!)
./LakeXpress syncSource - Teradata
Teradata is an enterprise-class data warehousing and analytics platform. LakeXpress uses FastBCP with optimized Teradata connectors for efficient data extraction to Parquet format.
Features:
- •Native Teradata .NET driver via FastBCP
- •Full support for Teradata-specific types
- •Parallel extraction to Parquet files
- •Optimized for large-scale data warehouses
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 - BigQuery
Google BigQuery is a serverless, highly scalable data warehouse. LakeXpress loads Parquet files from GCS into BigQuery tables using native bulk loading.
Publishing method:
BigQuery Load Job from GCS
Features:
- •Direct load from GCS
- •Native Parquet format support
- •Automatic schema detection
- •Petabyte-scale analytics
- •Integration with Google Cloud ecosystem

