Snowflake Data Platform Migration Services

Section 01

The 2025 AI-Readiness Crisis: Why Legacy Data Warehouses Can't Keep Up

Legacy data warehouses (Oracle, Teradata, SQL Server) were built for batch BI queries, not GenAI workloads. Here's what's breaking in 2025:

• ❌ No vector embeddings (RAG requires co-locating structured data + embeddings)
• ❌ No elastic GPU compute (LLM fine-tuning needs 0→1,000 GPUs in minutes)
• ❌ Hardware refresh cliff ($5M-$10M CapEx for Teradata/Oracle EOL)

The 2025 calculus: Snowflake's 3-year TCO ($1.5M-$5M OpEx) now costs less than a single Teradata hardware refresh.

---

How to Choose a Snowflake Migration Partner

If you prioritize AI/ML enablement: Slalom. They have deep Snowpark and Cortex AI expertise for building modern data apps.

If you have massive legacy SQL (Oracle/Teradata): Infosys. Their automated conversion tools can handle millions of lines of PL/SQL and BTEQ code.

If you need industry-specific data models: Cognizant. They bring pre-built accelerators for Healthcare, Retail, and Finance verticals.

If you have a petabyte-scale global estate: Accenture. They have the scale and methodology to handle the largest, most complex migrations with minimal downtime.

Red flags:

• Vendors who suggest a "Lift and Shift" without a clear optimization phase (a likely cost explosion)
• No experience with "SnowConvert" or similar automated code conversion tools
• Ignoring "Data Egress" costs in the TCO model
• Lack of FinOps governance in the project plan

---

Section 02

Top 3 Reasons Snowflake Migrations Fail

1. Lift-and-Shift Without Optimization

Porting Teradata DDLs 1:1 to Snowflake leads to full table scans and cost overruns.

Reality: Migrating a large table set without clustering keys produces much higher first-month consumption than budgeted. Fix: Define clustering keys on filtered columns before go-live. Use Snowflake Query Profile to identify table scans.

2. SQL Conversion Underestimation

Teradata BTEQ macros and Oracle PL/SQL packages don't auto-convert. Budget a large share of the timeline for SQL refactoring.

Reality: Automated conversion handles simple SELECT/INSERT best; complex stored procedures need manual rewrite. Measure the split with SnowConvert on your own code. Fix: Run SnowConvert before signing the SI contract. Add schedule for very large codebases (100K+ lines).

3. Data Egress Costs

Moving 100TB from on-prem to cloud incurs $5K-$12K in network fees + 4 weeks of bandwidth saturation.

Reality: Egress is easy to forget in the budget. Price it from your data volume and network path before committing. Fix: Use AWS Direct Connect or phased migration (migrate BI workloads first, then ETL).

---

Section 03

Snowflake Migration Roadmap

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

Activities:

• Run SnowConvert analysis to size SQL complexity
• Define "Lift & Shift" vs "Re-architect" strategy per workload
• Calculate TCO and ROI model
• Select SI partner and cloud provider (AWS/Azure/GCP)

Deliverables:

• Migration Strategy Document
• TCO Model
• Signed SI Contract

---

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

Activities:

• Set up Snowflake Organization & Accounts
• Implement RBAC (Role-Based Access Control)
• Configure Networking (PrivateLink) & Security
• Migrate a pilot workload (e.g., Marketing Data Mart)

Deliverables:

• Secure Snowflake Environment
• Pilot Success Report
• Validated Migration Patterns

---

Phase 3: Migration Factory (Months 7-18)

Activities:

• Automated SQL Conversion (SnowConvert)
• Historical Data Load (Snowball / Direct Connect)
• ETL/ELT Pipeline Migration (Informatica → Matillion/dbt)
• Validation & Testing (Row counts, Hash checks)

Risks:

• SQL conversion errors in complex stored procedures
• Data egress bandwidth saturation

Deliverables:

• Migrated Data Warehouse
• Converted ETL Pipelines
• UAT Sign-off

---

Phase 4: Optimization & Cutover (Months 19-24)

Activities:

• Performance Tuning (Clustering Keys, Warehouse Sizing)
• FinOps Setup (Resource Monitors, Auto-Suspend)
• Parallel Run (Legacy vs Snowflake)
• Final Cutover & Legacy Decommission

Deliverables:

• Production Snowflake Environment
• Decommissioned Legacy Hardware
• Project Closure

---

Section 04

Post-Migration: Best Practices

Months 1-3: FinOps & Stabilization

• Cost Governance: Implement strict Resource Monitors. Watch for "runaway queries" that burn credits.
• Performance: Monitor "Spilling to Disk" in Query Profile. Resize warehouses if needed.

Months 4-6: Modernization

• Data Sharing: Replace FTP file transfers with Snowflake Secure Data Sharing.
• AI/ML: Start using Snowpark for Python-based ML workloads directly in the database.

---

Section 05

Engagement Models: Choose Your Path

1. DIY / Assessment (<$100K)

• Tools: SnowConvert AI (free), SQL Analyzer, FinOps Tooling
• Goal: Understand SQL complexity and data volume before hiring an SI.

2. Guided Strategy ($100K-$500K)

• Deliverables: Migration Roadmap, SQL Assessment, Vendor Selection (RFP)
• Goal: Choose the right strategy (Lift-and-Shift vs Re-Architecture).

3. Full Migration ($500K-$10M+)

• Deliverables: SQL Conversion, Data Migration, Testing, FinOps Setup
• Goal: Execute migration on time and on budget.

---

Section 06

Snowflake vs Databricks vs Redshift: Decision Matrix

Decision Guide:

• 80% SQL queries, BI dashboards → Snowflake
• 80% Python/ML, custom models → Databricks
• Already deep in AWS, no multi-cloud plans → Redshift Serverless

---

Section 07

Migration Strategies: Lift-and-Shift vs Re-Architecture

1. Lift-and-Shift ⚡ Fastest (But Expensive Long-Term)

What it is: Direct port of Teradata/Oracle DDLs to Snowflake. Minimal SQL changes, use SnowConvert for automation.

Timeline: 12-15 months
Cost: $500K-$2M (lower upfront, higher consumption)

Pros:

• ✅ Fastest path (critical for hardware EOL deadlines)
• ✅ Lower initial consulting costs
• ✅ Minimal business logic rewrite

Cons:

• ❌ Poor query performance (no cluster optimization)
• ❌ 2-3x higher Snowflake consumption costs
• ❌ Technical debt carried forward

Best For: Hardware refresh deadline, compliance-driven migration, limited budget

---

2. Re-Architecture 🏗️ Optimal (Higher Upfront, Better ROI)

What it is: Redesign for Snowflake's micro-partitions, clustering keys, and ELT methodology. Full query optimization.

Timeline: 18-24 months
Cost: $2M-$5M+ (higher upfront, lower consumption)

Pros:

• ✅ Optimized for Snowflake (better query pruning)
• ✅ Lower long-term cloud costs (3-year ROI)
• ✅ Unlocks AI/ML capabilities (Snowpark, Cortex)

Cons:

• ❌ Longest timeline (may miss hardware EOL)
• ❌ Requires data engineering expertise
• ❌ Higher consulting fees

Best For: Long-term migration, AI/ML roadmap, FinOps-mature organizations

---

3. Hybrid (Phased) 🔄 Most Common

Timeline: 15-20 months
Approach: Lift-and-shift BI workloads (Phase 1: 6 months), then re-architect ETL/ML (Phase 2: 12 months)

Why it works: Immediate cost savings from hardware decommission, then optimize high-value workloads.

---

Section 08

Cost Breakdown: Where the Money Goes

The split below is an illustrative allocation for a hypothetical $2M migration, not a measured benchmark.

Hidden Costs:

• Data egress: $0.05-$0.12/GB (100TB = $5K-$12K)
• Snowflake credits: First 3 months often 2x budget (poor optimization)
• Talent: Cloud data engineers ($150K-$180K vs $110K for legacy DBAs)

---

Section 09

SQL Conversion Challenges

Teradata → Snowflake

• BTEQ scripts → SnowSQL stored procedures (SnowConvert can automate part of the conversion)
• Primary Index (PI) → Clustering Keys (manual redesign required)
• MERGE statements → MERGE or INSERT/UPDATE (syntax differs)

Oracle → Snowflake

• PL/SQL packages → Snowflake JavaScript UDFs (manual rewrite)
• ROWNUM → ROW_NUMBER() window function
• CONNECT BY → Recursive CTEs

SQL Server → Snowflake

• T-SQL cursors → Array functions or SQL set-based logic
• Linked servers → Snowflake data sharing or external tables
• tempdb → Transient tables (auto-dropped after session)

SnowConvert AI conversion coverage:

• Simple SELECT/INSERT: most likely to convert cleanly
• Complex stored procedures: partial conversion, manual review required
• Custom macros/UDFs: lowest coverage, manual rewrite likely

No general conversion rate is verified here; run SnowConvert on your own code to measure it.

---

Section 10

Cost Control (FinOps): Preventing the $50K Surprise Bill

1. Resource Monitors (Budget Alerts)

Set credit limits per warehouse:

CREATE RESOURCE MONITOR analytics_limit WITH CREDIT_QUOTA = 1000
TRIGGERS ON 80 PERCENT DO NOTIFY
         ON 100 PERCENT DO SUSPEND;

2. Auto-Suspend Policies (Idle Shutdown)

Default: 1 minute. Prevents waste from forgotten queries.

3. Clustering Keys (Query Pruning)

Define clustering on frequently filtered columns:

ALTER TABLE orders CLUSTER BY (order_date, region);

Impact: Reduces scanned data and therefore compute cost; measure the effect with Query Profile.

4. Commitment Purchases (Reserved Capacity)

• Discount on upfront annual commitment (get the current terms from your Snowflake account team)
• Risk: If usage drops, you overpay
• Rule: Buy ONLY after 6 months of steady-state usage

5. Query Optimization

Use Snowflake's Query Profile to identify full table scans and missing filters.

---

Section 11

Vendor Selection Criteria

---

Section 12

ROI & Break-Even Analysis

Operational Savings (Post-Migration)

• Hardware decommission: $2M-$10M CapEx (Teradata/Oracle licenses + maintenance)
• Elastic scaling: No over-provisioning (pay only for active compute)
• Faster queries: result caching and clustering can cut query time; measure on your workload

Break-Even Timeline

Divide one-time investment by documented annual savings (hardware, licenses and DBA headcount, net of Snowflake consumption), and include the parallel-run period. Use your own cost baseline rather than a generic payback figure.

Only migrate if:

1. Hardware EOL approaching (forcing $5M+ refresh)
2. AI/ML roadmap requiring elastic compute
3. Multi-cloud strategy (avoiding AWS/Azure lock-in)

---

Evidence note: Earlier versions of this page cited project counts, median costs and timelines, success rates, failure shares and unsourced conversion-rate statistics that could not be reproduced from available records; they have been withdrawn. Cost and timeline ranges are editorial scoping ranges, not supplier quotes. See our methodology.

---

Section 13

Risk Factors

SQL Dialect Conversion Complexity

Teradata BTEQ scripts, Oracle PL/SQL, and SQL Server T-SQL don't translate 1:1 to SnowSQL. Custom macros, stored procedures, and proprietary extensions require manual rewriting or automated conversion tools (SnowConvert). Expect SQL refactoring to be a major share of migration effort; size it with a SnowConvert assessment of your own code.

Query Cost Explosion from Unoptimized Workloads

Snowflake is consumption-based ($2/credit). A bad query in Teradata runs slow; in Snowflake, it burns thousands in cloud credits. Without proper clustering keys, auto-suspend policies, and query optimization, costs can exceed legacy TCO by 2-3x.

Data Egress Costs for Terabyte-Scale Migrations

Moving 50TB+ from on-prem to cloud incurs network egress fees ($0.05-$0.12/GB). For 100TB migration, data transfer alone costs $5K-$12K, plus 2-4 weeks of bandwidth saturation. Requires phased migration or dedicated network circuits.

---

Section 14

Feasibility Analysis

Ideal candidates

• Legacy DW hardware refresh approaching (Teradata, Oracle Exadata = $2M-$10M CapEx)
• Need to support GenAI/ML workloads (RAG, vector embeddings, LLM fine-tuning)
• Multi-cloud strategy or avoiding AWS/Azure vendor lock-in
• Elastic scaling requirements (unpredictable workload spikes)

Break-even cost: Divide one-time investment by documented annual savings (hardware, licensing and operations, net of Snowflake consumption and FinOps costs); include the parallel-run period Talent risk: MEDIUM - Cloud data engineers ($120K-$180K) available but expensive. Teradata/Oracle DBAs require 3-6 month reskilling.