Data Platform Migration: From On-Premise to Cloud

TL;DR

• Cloud migration = Move Data Platform từ on-premise lên cloud (AWS, GCP, Azure)
• Why migrate: Cost savings (30-50%), scalability, modern features, disaster recovery
• 3 patterns: Lift-and-shift (nhanh, không optimal), Re-platform (cân bằng), Re-architect (best, nhưng mất thời gian nhất)
• Best strategy: Strangler Fig Pattern (migrate incrementally, low risk)
• Timeline: 6-18 months cho enterprise (depends on complexity)
• Challenges: Network bandwidth, downtime, application compatibility, cost overruns
• Case study: Manufacturing company 80TB Oracle → Snowflake, 12 months, 50% cost reduction, 5x performance
• Success factors: Executive buy-in, phased approach, thorough testing, team training

Giới Thiệu: Why Migrate to Cloud?

Vấn Đề với On-Premise Data Platforms

Typical on-premise setup:

Infrastructure:
- Oracle Database (Exadata) hoặc SQL Server cluster
- Physical servers trong data center
- SAN storage (expensive)
- ETL tools: Informatica, SSIS, Talend trên VMs
- BI tools: Tableau Server, Power BI on-prem

Costs:
- CapEx: $500K-$2M upfront (servers, storage, licenses)
- OpEx: $200K-$500K/year (maintenance, data center, team)
- Scaling: Mua thêm hardware (6-12 months lead time)

Pain points:

• ❌ High upfront cost: Phải invest $1M+ trước khi có value
• ❌ Scaling slow: Cần hardware mới 6-12 tháng
• ❌ Disaster recovery expensive: Secondary data center = 2x cost
• ❌ Maintenance overhead: Patching, upgrades, hardware failures
• ❌ Limited innovation: Stuck với old versions, khó upgrade

Benefits of Cloud Migration

Cloud Data Platform:

Infrastructure:
- Snowflake, BigQuery, Redshift (fully managed)
- Auto-scaling compute
- Cheap cloud storage (S3, GCS, ADLS)
- Managed ETL (Glue, Dataflow, Data Factory)
- Cloud BI (Looker, Power BI Service)

Costs:
- CapEx: $0 (no upfront investment)
- OpEx: $50K-$200K/year (pay-as-you-go)
- Scaling: Instant (click a button)

Benefits:

• ✅ Cost savings: 30-50% lower TCO (Total Cost of Ownership)
• ✅ Scalability: Scale từ GB → PB trong minutes
• ✅ Performance: Modern cloud warehouses 5-10x faster
• ✅ Disaster recovery: Built-in geo-replication
• ✅ Modern features: ML, streaming, lakehouse
• ✅ Pay-per-use: Không lãng phí capacity

ROI calculation (5-year TCO):

Beyond cost: Faster time-to-market, better analytics, modern features.

Migration Patterns

1. Lift-and-Shift (Rehost)

Definition: Move VMs/databases to cloud as-is, minimal changes.

Example:

• Oracle on-prem → Oracle on RDS/EC2
• SQL Server on-prem → SQL Server on Azure VM

Pros ✅:

• Fast (1-3 months)
• Low risk (no code changes)
• Quick cost savings (no data center rent)

Cons ❌:

• Not cloud-native (không tận dụng cloud advantages)
• Still pay for licenses (Oracle/SQL Server expensive on cloud)
• Limited scalability improvements

When to use: Quick migration để thoát data center contract, migrate later to cloud-native.

2. Re-platform (Lift-and-Reshape)

Definition: Minor modifications để leverage cloud services.

Example:

• Oracle on-prem → Amazon RDS for Oracle (managed)
• SQL Server on-prem → Azure SQL Database (PaaS)

Pros ✅:

• Faster than re-architect (3-6 months)
• Leverage managed services (less ops)
• Some cost savings

Cons ❌:

• Still tied to legacy tech (Oracle, SQL Server)
• Limited modern features

When to use: Balance giữa speed và modernization.

3. Re-architect (Cloud-Native)

Definition: Redesign Data Platform cho cloud-native architecture.

Example:

• Oracle/SQL Server → Snowflake/BigQuery/Redshift
• Informatica → dbt + Airflow/Prefect
• Tableau Server → Looker/Power BI Service

Pros ✅:

• Maximize cloud benefits (cost, performance, scalability)
• Modern architecture (ELT instead of ETL, lakehouse)
• Best long-term value

Cons ❌:

• Longest timeline (6-18 months)
• Requires code rewrite (SQL, ETL)
• Team reskilling needed

When to use: Khi có time & budget, want modern platform.

Assessment Phase

Step 1: Inventory

Data sources:

# Spreadsheet hoặc tool (e.g., Collibra, Alation)
inventory = {
    "databases": [
        {"name": "Oracle Prod", "size": "50TB", "tables": 500},
        {"name": "SQL Server DWH", "size": "30TB", "tables": 200},
        {"name": "MySQL App DB", "size": "5TB", "tables": 100}
    ],
    "etl_jobs": [
        {"tool": "Informatica", "jobs": 200},
        {"tool": "SSIS", "jobs": 150}
    ],
    "reports": [
        {"tool": "Tableau", "dashboards": 100, "users": 500}
    ]
}

Output: Complete list of databases, pipelines, reports, users.

Step 2: Complexity Scoring

Total effort: 42 weeks (10 months) → Budget 12-15 months với buffer.

Step 3: Dependencies Mapping

Dependency graph:

[Oracle Prod DB]
      ↓
[Informatica ETL] → [SQL Server DWH]
      ↓                    ↓
[Business Apps]      [Tableau Dashboards]
      ↓                    ↓
  [Users]              [Users]

Critical path: Oracle → Informatica → DWH → Tableau Migration order: Migrate này theo thứ tự reverse (Tableau first, Oracle last).

Step 4: Estimate Cost

Cloud cost estimation:

# BigQuery example
storage_cost = 80 * 1024 * 0.02  # 80TB * $0.02/GB/month = $1,638/month
compute_cost = 50 * 5  # 50TB scanned/month * $5/TB = $250/month

total_monthly = storage_cost + compute_cost  # $1,888/month
annual = total_monthly * 12  # $22,656/year

# vs On-Premise Oracle: $200K/year licenses
savings = 200_000 - 22_656  # $177K/year (89% savings)

ROI: Positive sau 6 months (migration cost: $100K, annual savings: $177K).

Migration Strategies

Strategy 1: Big Bang

Definition: Migrate toàn bộ hệ thống cùng lúc trong 1 weekend.

Timeline:

Friday 6 PM: Stop all pipelines
Friday 6 PM - Sunday 6 PM: Migrate data
Sunday 6 PM: Test
Monday 6 AM: Go live (or rollback)

Pros ✅:

• Fast (1 weekend)
• No dual operations

Cons ❌:

• High risk (nếu fail, toàn bộ down)
• Long downtime (24-48 hours)
• Hard to rollback

When to use: Small systems (<10TB), tolerate downtime.

Strategy 2: Phased Migration

Definition: Migrate từng component, incrementally.

Timeline:

Phase 1 (Month 1-2): BI layer (Tableau → Looker)
Phase 2 (Month 3-4): DWH (SQL Server → Snowflake)
Phase 3 (Month 5-8): ETL (Informatica → dbt)
Phase 4 (Month 9-12): Source DBs (Oracle → RDS)

Pros ✅:

• Lower risk (fail 1 phase, others unaffected)
• Short downtime per phase
• Learn & improve each phase

Cons ❌:

• Dual operations (run both old & new systems)
• Complex (data sync old ↔ new)
• Longer total timeline

When to use: Enterprise systems, cannot tolerate long downtime.

Strategy 3: Hybrid (Temporary)

Definition: Keep một phần on-prem, migrate phần khác lên cloud.

Example:

• Keep Oracle on-prem (legacy apps depend on it)
• Migrate DWH to Snowflake (analytics workloads)
• Sync Oracle → Snowflake via CDC

Pros ✅:

• Immediate value (analytics on cloud)
• Don't break legacy apps

Cons ❌:

• Complex networking (VPN, data sync)
• Dual costs (on-prem + cloud)
• Not long-term solution

When to use: Legacy systems khó migrate, want quick wins.

Recommended Approach: Strangler Fig Pattern

What is Strangler Fig?

Concept: Build new cloud platform parallel to old, migrate workloads one-by-one, retire old when empty.

Year 0: 100% on-prem
         ↓
Year 1: 70% on-prem, 30% cloud (migrate low-risk workloads)
         ↓
Year 2: 30% on-prem, 70% cloud (migrate critical workloads)
         ↓
Year 3: 0% on-prem, 100% cloud (retire old system)

Named after: Strangler fig tree (mọc quanh cây cũ, dần thay thế).

Implementation Steps

Step 1: Setup Cloud Landing Zone

# AWS example
1. Create VPC (network isolation)
2. Setup VPN/Direct Connect to on-prem
3. Configure IAM roles (security)
4. Enable CloudTrail, GuardDuty (compliance)
5. Create S3 buckets (data lake)
6. Provision Redshift/Snowflake/BigQuery

Step 2: Migrate BI Layer First

Why BI first?

• Low risk (doesn't affect source systems)
• Quick win (better dashboards)
• Users adopt cloud early

Example:

Old: Tableau Server on-prem → Oracle DWH
New: Looker (cloud) → Snowflake

Step-by-step:
1. Snapshot Oracle DWH → Snowflake (initial load)
2. Setup CDC (Oracle → Snowflake sync)
3. Recreate Tableau dashboards in Looker
4. UAT với users
5. Cutover (point users to Looker)
6. Deprecate Tableau Server (sau 2 weeks validation)

Step 3: Migrate Data Warehouse

Example:

Old: SQL Server DWH (on-prem)
New: Snowflake (cloud)

Data migration:
1. Initial bulk load (SQL Server → S3 → Snowflake)
   - Use AWS DMS or COPY command
2. CDC setup (ongoing sync during migration)
   - Debezium, Qlik Replicate, Striim
3. Parallel run (queries on both old & new, compare results)
4. Cutover

Step 4: Migrate ETL Pipelines

Old: Informatica PowerCenter (200 jobs) New: dbt + Airflow

# Conversion
Informatica mapping → dbt SQL model

# Example
# Informatica: Transformation "Aggregate Sales"
# Input: orders (order_id, product_id, amount, date)
# Output: daily_sales (date, product_id, total_amount)

# dbt equivalent
# models/marts/fct_daily_sales.sql
SELECT
    DATE(order_date) AS sale_date,
    product_id,
    SUM(amount) AS total_amount
FROM {{ ref('stg_orders') }}
GROUP BY 1, 2

Migration:

1. Convert 10 jobs (pilot)
2. Test, validate
3. Convert remaining 190 jobs (batch)
4. Deprecate Informatica (sau khi all jobs stable)

Step 5: Migrate Source Databases (Last)

Old: Oracle 50TB (legacy apps read from it) New: Oracle RDS (managed) hoặc migrate apps to cloud

Why last? Most risky, many dependencies.

Step 6: Decommission On-Premise

Checklist:
☑ All workloads migrated
☑ No traffic to old system (monitor 2 weeks)
☑ Backups transferred to cloud
☑ Data retention policies met
☑ Shutdown old servers
☑ Cancel data center contract

Technical Steps: Data Migration

Step 1: Initial Bulk Load

AWS Database Migration Service (DMS):

# Create replication instance
aws dms create-replication-instance \
  --replication-instance-identifier oracle-to-snowflake \
  --replication-instance-class dms.c5.xlarge

# Create source endpoint (Oracle)
aws dms create-endpoint \
  --endpoint-identifier oracle-source \
  --endpoint-type source \
  --engine-name oracle \
  --server-name oracle.company.com \
  --port 1521 \
  --database-name PROD

# Create target endpoint (Snowflake via S3)
aws dms create-endpoint \
  --endpoint-identifier s3-target \
  --endpoint-type target \
  --engine-name s3 \
  --s3-settings BucketName=migration-data

# Create migration task
aws dms create-replication-task \
  --replication-task-identifier migrate-oracle-tables \
  --source-endpoint-arn <oracle-arn> \
  --target-endpoint-arn <s3-arn> \
  --migration-type full-load-and-cdc \
  --table-mappings file://table-mappings.json

Transfer 50TB data: 2-5 days (depends on network bandwidth).

Bandwidth calculation:

50TB = 50 * 1024 GB = 51,200 GB
Network: 100 Mbps = 12.5 MB/s = 45 GB/hour

Transfer time = 51,200 GB / 45 GB/hour = 1,138 hours = 47 days

With 1 Gbps link: 4.7 days

Solution: AWS Snowball (ship hard drives) cho >10TB.

Step 2: Change Data Capture (CDC)

Why CDC? Initial load mất vài ngày, data thay đổi trong lúc đó. CDC sync changes.

Tools:

• Debezium: Open-source CDC (MySQL, Postgres, SQL Server, Oracle)
• AWS DMS: Built-in CDC
• Qlik Replicate: Enterprise CDC tool
• Striim: Real-time data integration

Debezium example (Postgres → Kafka → Snowflake):

# Debezium connector config
{
  "name": "postgres-cdc-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "postgres.company.com",
    "database.port": "5432",
    "database.user": "replication_user",
    "database.dbname": "production",
    "database.server.name": "prod_db",
    "table.include.list": "public.orders,public.customers",
    "plugin.name": "pgoutput"
  }
}

Data flow:

Postgres → Debezium → Kafka → Snowflake Kafka Connector → Snowflake

Latency: 1-5 minutes (near real-time).

Step 3: Data Validation

Row count validation:

-- Source (Oracle)
SELECT COUNT(*) FROM orders WHERE order_date >= '2025-01-01';
-- Result: 1,234,567

-- Target (Snowflake)
SELECT COUNT(*) FROM orders WHERE order_date >= '2025-01-01';
-- Result: 1,234,567 ✅ Match

Checksum validation:

-- Source
SELECT SUM(CAST(order_id AS NUMBER)) AS checksum FROM orders;
-- Result: 98765432109876

-- Target
SELECT SUM(order_id::NUMBER) AS checksum FROM orders;
-- Result: 98765432109876 ✅ Match

Schema validation:

import pandas as pd

# Compare schemas
source_schema = get_schema("oracle", "orders")
target_schema = get_schema("snowflake", "orders")

diff = compare_schemas(source_schema, target_schema)
# Check: column names, types, nullability

Step 4: Parallel Run

Run queries on both old & new systems, compare results:

-- Query on Oracle
SELECT product_id, SUM(amount) as revenue
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY product_id
ORDER BY revenue DESC
LIMIT 10;

-- Same query on Snowflake
SELECT product_id, SUM(amount) as revenue
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY product_id
ORDER BY revenue DESC
LIMIT 10;

-- Compare results (should match)

Parallel run duration: 2-4 weeks (build confidence).

Step 5: Cutover

Cutover weekend:

Friday 6 PM:
- Stop CDC (no more changes to old system)
- Final data sync
- Final validation

Saturday:
- Update connection strings (apps point to new DB)
- Test critical workflows

Sunday:
- Monitor, fix issues

Monday:
- Go live

Rollback plan: Keep old system running 2 weeks (in case need to rollback).

Challenges & Solutions

Challenge 1: Network Bandwidth

Problem: 80TB data transfer over 100 Mbps = 74 days.

Solutions:

• Upgrade network: 1 Gbps link (7.4 days)
• AWS Snowball: Ship hard drives (receive in 1 week, upload in 1 day)
• Incremental migration: Migrate 10TB/month over 8 months
• Compress data: Reduce 80TB → 40TB (2x faster)

Challenge 2: Downtime Requirements

Problem: Business requires < 4 hours downtime.

Solutions:

• CDC: Continuous sync, minimal downtime cutover
• Blue-Green deployment: New system parallel, instant switch
• Read replicas: Migrate read traffic first, writes last

Challenge 3: Application Compatibility

Problem: Legacy apps expect Oracle-specific SQL/features.

Example:

-- Oracle syntax
SELECT * FROM orders WHERE ROWNUM <= 10;

-- Snowflake syntax
SELECT * FROM orders LIMIT 10;

Solutions:

• Code audit: Find Oracle-specific code
• Rewrite: Convert to standard SQL or Snowflake syntax
• Emulation layer: Use tools like Striim to translate queries (temporary)

Challenge 4: Cost Overruns

Problem: Cloud costs higher than expected.

Example: Forgot to optimize queries → BigQuery scans 100TB/month = $500 (expected $50).

Solutions:

• Cost monitoring: Set budget alerts
• Query optimization: Partition tables, cluster keys
• Reserved capacity: Commit to 1-year for 30% discount
• Governance: Review & approve expensive queries

Challenge 5: Team Skillset

Problem: Team knows Oracle, not Snowflake/BigQuery.

Solutions:

• Training: 2-week bootcamp on new platform
• Vendor support: Snowflake/Google provide migration assistance
• Hire experts: Temporary contractors for migration
• Carptech consulting 😊

Timeline: Typical Enterprise Migration

6-Month Timeline (Small/Medium)

12-Month Timeline (Large Enterprise)

Best Practices

1. Executive Buy-In

Critical: Migration is strategic decision, needs C-level support.

Build business case:

Cost savings: $2M over 5 years
Performance: 5x faster queries → better decisions
Scalability: Support 10x data growth
Innovation: ML, real-time analytics

Present to CFO, CTO, CEO → Get approval & budget.

2. Start Small (Pilot)

Don't migrate everything at once:

Pilot: Migrate 1 low-risk dashboard
Timeline: 2 weeks
Learn: Network bottlenecks, query performance, team gaps
Iterate: Fix issues before full migration

Success → Confidence → Full rollout.

3. Thorough Testing

Test types:

• Functional: Queries return correct results
• Performance: Queries faster on cloud (or acceptable)
• Load: Handle peak traffic
• Failover: Disaster recovery works

UAT (User Acceptance Testing): 10-20 power users test 2 weeks before cutover.

4. Communication

Stakeholders to inform:

• Users: New BI tool, new login, new features
• Executives: Progress updates, risks
• IT teams: Network changes, security
• Finance: Cost tracking

Weekly status reports: On track / at risk / blockers.

5. Monitor Costs

Cloud cost surprises common:

# Set budget alert
aws budgets create-budget \
  --budget BudgetName=DataPlatformMigration,BudgetLimit=10000,TimeUnit=MONTHLY

# Daily cost review
SELECT
    DATE(usage_start_time) AS day,
    SUM(cost) AS daily_cost
FROM billing_export
GROUP BY 1
ORDER BY 1 DESC;

Action: If cost spike → Investigate & optimize immediately.

Case Study: Manufacturing Company

Background

Company: Manufacturing, 2,000 employees Old System:

• Oracle Exadata (80TB data)
• Informatica PowerCenter (300 ETL jobs)
• Tableau Server (150 dashboards, 500 users)
• Cost: $500K/year (licenses + data center)

Pain Points:

• Oracle queries slow (10-20 seconds)
• Scaling expensive (need new Exadata box = $1M)
• ETL jobs brittle (failures common)

Migration Plan

Target: Snowflake (cloud data warehouse)

Timeline: 12 months

Strategy: Strangler Fig (phased migration)

Implementation

Month 1-2: Assessment

• Inventoried 500 tables, 300 ETL jobs, 150 dashboards
• Complexity scoring
• Estimated Snowflake cost: $150K/year (70% savings)

Month 3: Cloud Setup

• Provisioned Snowflake (X-Large warehouse)
• Setup VPN (on-prem ↔ AWS)
• Created S3 data lake

Month 4-5: Pilot Migration

• Migrated 1 dashboard (Sales dashboard)
  • Oracle → Snowflake (initial load: 5TB)
  • Recreated in Tableau Cloud
• UAT: 10 sales users tested 2 weeks
• Result: Queries 8x faster (20s → 2.5s)

Month 6-8: DWH Migration

• Bulk load: 80TB Oracle → S3 → Snowflake (3 weeks)
• CDC setup: Debezium (Oracle → Kafka → Snowflake)
• Parallel run: 4 weeks (validated results)
• Cutover: Weekend, 6 hours downtime

Month 9-11: ETL Migration

• Converted 300 Informatica jobs → dbt (100 models)
  • Hired Carptech consultants (accelerated migration)
• Orchestration: Airflow on MWAA (AWS managed)
• Testing: 3 weeks

Month 12: BI Migration

• 150 Tableau dashboards → Tableau Cloud (reconnected to Snowflake)
• User training: 2-day workshop
• Go live

Results

Performance:

• Query speed: 5x faster (average 10s → 2s)
• ETL runtime: 50% faster (dbt parallel execution)
• Data freshness: 24 hours → 1 hour (CDC)

Cost:

• Old: $500K/year
• New: $250K/year
  • Snowflake: $150K
  • AWS (S3, Airflow, networking): $50K
  • Tools (Tableau Cloud, dbt Cloud): $50K
• Savings: $250K/year (50% reduction)

Other Benefits:

• ✅ Zero downtime scaling (vs 6 months for Oracle)
• ✅ Built-in disaster recovery (Snowflake replication)
• ✅ Team productivity: Data engineers focus on analytics, not infrastructure

ROI: Migration cost $400K (consultants, team time), payback in 1.6 years.

Migration Checklist (60 Items)

Assessment Phase

• Inventory all databases, sizes, schemas
• List all ETL/ELT jobs, dependencies
• Catalog BI reports, dashboards, users
• Map data lineage (source → transformations → reports)
• Identify PII/sensitive data (GDPR/PDPA compliance)
• Assess network bandwidth (on-prem ↔ cloud)
• Complexity scoring (simple/medium/high for each component)
• Estimate cloud costs (calculator: AWS, GCP, Azure)
• Build business case (cost savings, benefits)
• Get executive buy-in (CFO, CTO, CEO approval)

Planning Phase

• Choose cloud provider (AWS, GCP, Azure)
• Choose target architecture (Snowflake, BigQuery, Redshift)
• Define migration strategy (big bang, phased, strangler fig)
• Create project plan (timeline, milestones, resources)
• Identify pilot workload (low-risk, high-value)
• Assign roles (project manager, architects, engineers, testers)
• Setup communication plan (stakeholder updates)
• Risk assessment (network, downtime, skills, budget)
• Contingency planning (rollback procedures)

Cloud Setup Phase

• Provision cloud accounts (AWS, GCP, Azure)
• Setup VPC/networking (isolation, subnets)
• Configure VPN/Direct Connect (on-prem connectivity)
• Setup IAM roles & policies (security)
• Enable logging & monitoring (CloudTrail, CloudWatch)
• Create S3/GCS buckets (data lake staging)
• Provision cloud data warehouse (Snowflake, BigQuery, Redshift)
• Setup encryption (at rest, in transit)
• Configure backup policies
• Compliance checks (GDPR, PDPA, SOC 2)

Data Migration Phase

• Install migration tools (AWS DMS, Striim, Debezium)
• Create source endpoints (on-prem databases)
• Create target endpoints (cloud warehouse, S3)
• Schema conversion (Oracle → Snowflake, etc.)
• Initial bulk load (full copy of data)
• Validate row counts (source vs target)
• Validate checksums (data integrity)
• Setup CDC (ongoing sync)
• Monitor CDC lag (< 5 minutes)
• Test data freshness

ETL/Pipeline Migration Phase

• Audit existing ETL jobs (Informatica, SSIS, etc.)
• Choose new orchestration (Airflow, Prefect, dbt Cloud)
• Convert ETL jobs to ELT (SQL-based transformations)
• Rewrite jobs in dbt (models, tests, docs)
• Setup CI/CD for dbt (git, dbt Cloud)
• Test transformed data (compare old vs new)
• Schedule jobs (Airflow DAGs, dbt Cloud scheduler)
• Monitor job success rates

BI Migration Phase

• Inventory dashboards (Tableau, Power BI, etc.)
• Choose cloud BI (Looker, Tableau Cloud, Power BI Service)
• Recreate dashboards in new BI tool
• Update data sources (point to cloud warehouse)
• User training (2-day workshop)
• UAT with power users (2 weeks)
• Gather feedback, iterate

Testing Phase

• Functional testing (queries return correct data)
• Performance testing (query latency, throughput)
• Load testing (simulate peak traffic)
• Failover testing (disaster recovery)
• Security testing (access controls, encryption)
• Compliance testing (PII handling, audit logs)

Cutover Phase

• Schedule cutover window (weekend, low-traffic)
• Notify all stakeholders (1 week before)
• Stop CDC (freeze old system)
• Final data sync
• Final validation (row counts, checksums)
• Update connection strings (apps → cloud)
• Test critical workflows
• Monitor errors, performance
• Rollback plan ready (if issues)

Post-Migration Phase

• Monitor cloud costs (daily)
• Optimize queries (partitioning, clustering)
• Review performance (vs SLAs)
• Gather user feedback
• Document lessons learned
• Decommission on-premise (after 2 weeks validation)
• Cancel data center contracts
• Celebrate success! 🎉

Kết Luận

Key Takeaways

✅ Cloud migration delivers 30-50% cost savings, 5x performance, infinite scalability ✅ 3 patterns: Lift-and-shift (fast), Re-platform (balanced), Re-architect (best long-term) ✅ Strangler Fig strategy: Migrate incrementally, low risk, high success rate ✅ Timeline: 6-18 months (depends on size, complexity) ✅ Challenges: Network bandwidth, downtime, skills, costs → All solvable with planning ✅ Success factors: Executive buy-in, pilot first, thorough testing, communication

Recommendations

Cho Small/Medium Companies:

• Choose re-architect (cloud-native) → Maximize benefits
• Use managed services (BigQuery, Snowflake, dbt Cloud) → Zero ops
• Timeline: 6 months
• Cost: $50K-$200K (consultants, migration tools)

Cho Large Enterprises:

• Strangler Fig pattern (phased migration) → Lower risk
• Pilot first (1 dashboard) → Learn before full rollout
• Timeline: 12-18 months
• Cost: $200K-$1M (team time, consultants, dual operations)

Migration path:

1. Build business case → Get executive buy-in
2. Choose cloud & target architecture
3. Pilot migration (1 low-risk workload)
4. Full migration (phased)
5. Optimize & iterate

Next Steps

Muốn migrate Data Platform lên cloud?

Carptech giúp bạn:

• ✅ Assessment & migration planning (2 weeks)
• ✅ Cloud architecture design (Snowflake, BigQuery, Redshift)
• ✅ Hands-on migration (6-12 months)
• ✅ Cost optimization (reduce 40-60% cloud spend)
• ✅ Team training (upskill your engineers)

Chúng tôi đã migrate 10+ enterprises (banking, retail, manufacturing) từ Oracle/SQL Server lên cloud với success rate 100%.

📞 Liên hệ Carptech: carptech.vn

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