Modern cloud data warehouse with intuitive and automotive features like Compressing Data, Statistic Collection, Workload Management, and Disaster Recovery instead of DBA-driven data warehouse like Teradata
Multi-cluster cloud infrastructure that separates compute from storage
Enables enterprises to automatically and instantly scale their infrastructure
No software or hardware to install, configure or manage
Teradata Migration Considerations
ETL pipelines to push data to Teradata
Teradata Data Model
Visualization tools to pull data out of Teradata
Client applications dependent on data from Teradata
DevOps and tooling around Teradata.
Migration Steps
Step 1 - Document
Databases and Tables to be migrated
Users, Roles, and applications access to the tables and databases
Scripts and Applications responsible for data load
Scripts and Applications responsible for data pull
Step 2 - Phased Migration Plan
Migrate low-impact tables to the business first
End-to-end ingestion, transformation, and consumption of the tables
Incorporate automated scripts and tools for migration
Step 3 - Migrate DDL
Export DDL scripts for Teradata tables, views and sequences
Create Snowflake equivalent DDL
Skip Teradata system tables like DBC, TD_SYSGPL, TD_SYSXML
Directly code the data type conversions into the script instead of doing any manual search and replace process
Step 4 - One-time Data Load to Snowflake
Extract data from Teradata tables using Teradata Parallel Transporter (TPT)
Move files to S3 or Blob storage or Cloud storage
Load data to Snowflake
Step 5 - Ongoing Data Load to Snowflake
Connect each source of data to Snowflake while it is still writing to Teradata.
Test applications in parallel and ensure a seamless transition.
Step 6 - Update applications to consume data from Snowflake
Run two parallel versions of each application during the migration – one which continues consuming data from Teradata and another consuming data from Snowflake.
Step 7 - Validation
Run parallel validation with Teradata and Snowflake