Teradata to Snowflake Migration Services

Technical Deep Dive

1. Warehouse vs. Lakehouse

• Snowflake: Started as a better warehouse (SQL-first). Excellent for BI, reporting, and structured data.
• Databricks: Started as a better data lake (Spark/AI-first). Excellent for ML, unstructured data, and complex transformations.
• Convergence: Both are converging, but your choice depends on whether your primary user is a Business Analyst (SQL) or a Data Scientist (Python).

2. The Indexing Shift

• Teradata: Relies heavily on Primary Indexes (PI) for data distribution. A bad PI means skewed data and slow performance.
• Snowflake: Uses Micro-partitions and Clustering Keys. You don't "index" tables in the traditional sense.
• Gotcha: Direct porting of Teradata DDLs without rethinking clustering will lead to poor pruning and high scan costs.

3. Cost Control (FinOps)

• The Trap: Teradata is a sunk cost (you bought the appliance). Snowflake is consumption-based. A bad query in Teradata just runs slow; in Snowflake, it burns cash.
• Governance: Implement Resource Monitors and strict Auto-Suspend policies (e.g., 1 minute) from Day 1. Use "Commitment Purchases" only after you understand your steady-state usage.

How to Choose a Teradata to Snowflake Migration Partner

If you need automated code conversion: Cognizant or Infosys. They have built proprietary accelerators to convert BTEQ scripts and stored procedures to SnowSQL/Python.

If you need a massive enterprise migration: Accenture. They have the scale to handle petabyte-scale data movements and global delivery teams.

If you need strategic data architecture: Slalom. They excel at redesigning your data model for the cloud (Data Mesh / Data Vault) rather than just lifting and shifting.

If you need complex financial modeling: Deloitte. They can build the detailed business case to justify the move from CapEx (Teradata) to OpEx (Snowflake).

Red flags:

• Vendors who suggest a "Lift and Shift" of the data model without optimization (leads to poor performance)
• No strategy for handling proprietary Teradata extensions (Macros, FastLoad)
• Ignoring the "Data Egress" cost from on-prem to cloud
• Lack of FinOps/Governance planning in the SOW

When to Hire Teradata Migration Services

1. The Appliance Refresh

Your Teradata hardware is reaching End-of-Life (EOL). The cost to renew/upgrade the appliance is $5M+.

Trigger: "Do we really want to buy another box?"

2. AI/ML Demand

Data Scientists want to run Python/ML models on the data. Teradata is great for SQL, but poor for ML. They are extracting data to S3 anyway.

Trigger: "We need a Data Lakehouse."

3. Concurrency Bottlenecks

Monday morning reports are timing out because the appliance is maxed out. You can't scale compute without buying more storage (coupled scaling).

Trigger: "The dashboard takes 20 minutes to load."

4. Data Democratization

You want to share data with external partners or other business units. Teradata makes this hard. Snowflake Data Sharing makes it instant.

Trigger: "We need to send this data to our suppliers securely."

5. Cost Transparency

You want to charge back data costs to individual departments. Teradata is a shared black box. Snowflake allows per-warehouse billing.

Trigger: "Who is using all the resources?"

Total Cost of Ownership: Teradata vs Snowflake

Hidden Costs NOT Included:

• Dual Run: You will pay for Teradata AND Snowflake for 6-12 months during the transition.
• Egress Fees: Moving 1PB of data to the cloud costs money.

Break-Even Analysis:

• Median Investment: $1.5M
• Annual Savings: $1M (Hardware Support + Admin Costs)
• Break-Even: 1.5 - 2 years

Typical Teradata to Snowflake Migration Roadmap

Phase 1: Discovery & Assessment (Months 1-3)

Activities:

• Inventory all BTEQ scripts, Macros, and Stored Procedures
• Analyze query logs (DBQL) to identify usage patterns
• Define the "To-Be" architecture (Data Vault / Star Schema)

Deliverables:

• Migration Complexity Scorecard
• Future State Architecture

Phase 2: Foundation & Pilot (Months 4-6)

Activities:

• Set up Snowflake Organization & Security (RBAC)
• Configure Networking (PrivateLink)
• Migrate a "Vertical Slice" (End-to-End Data Mart)

Deliverables:

• Production-Ready Snowflake Account
• Pilot Use Case Live

Phase 3: Historical Data & Code Conversion (Months 7-12)

Activities:

• Data: Use Snowball or Direct Connect to move historical data.
• Code: Run automated conversion tools. Manual fix for complex logic.
• Testing: Row count checks, Hash validation, Performance comparison.

Deliverables:

• 90% of Data in Cloud
• Converted Codebase

Phase 4: Cutover & Decommission (Months 13-18)

Activities:

• Parallel Run (Compare reports from both systems)
• Point BI tools (Tableau/PowerBI) to Snowflake
• Turn off Teradata

Deliverables:

• Retired Appliance
• Fully Modernized Data Platform

Architecture Transformation

Post-Migration: Best Practices

Months 1-3: FinOps

• Resource Monitors: Set hard limits on warehouse spending.
• Query Tuning: Identify the "Top 10 Expensive Queries" and rewrite them.

Months 4-6: Data Democratization

• Data Sharing: Use Snowflake Data Sharing to share live data with partners without copying it.
• AI Integration: Connect your data warehouse to SageMaker or Azure ML.

Other Data & AI Migrations

Data Warehouse Migrations (Analytical):

• Teradata → Snowflake (this guide)
• Oracle → Snowflake

Transactional Database Migrations:

• Oracle to PostgreSQL - OLTP database migration
• MongoDB to PostgreSQL - NoSQL to relational for ACID compliance, cost savings

Risk Factors

Proprietary SQL Extensions

Teradata has many proprietary SQL extensions (BTEQ scripts, macros, specific join syntaxes) that don't translate 1:1 to cloud data warehouses.

Concurrency & Workload Management

Teradata is famous for its robust workload management (TASM). Snowflake/Databricks handle concurrency differently (auto-scaling warehouses). Poorly tuned queries can lead to massive cloud bills.

Data Egress Costs

Moving petabytes of data out of on-prem data centers can incur significant time and network costs.

Feasibility Analysis

Ideal candidates

• Teradata hardware refresh is coming up (expensive)
• Need to separate compute from storage
• Want to democratize data access for AI/ML teams

Break-even cost: $500k/year in hardware/support Talent risk: Medium. Cloud data engineers are expensive but available.