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Snowflake Migration Services

Zero Disruption, Proven Playbooks

We migrate Teradata, Redshift, Oracle, SQL Server, and Hadoop environments to Snowflake. With AI-accelerated code conversion, parallel running, and a structured cutover plan that keeps your business running throughout.

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40TB+

Migrated

50+

Projects Delivered

10 Weeks

Avg. Migration Weeks

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Migrate Your Legacy Warehouse to Snowflake

Here's How We Move You Off It.

Teradata licenses are renewing. Redshift is hitting concurrency limits. Oracle needs a DBA just to stay alive. The business case for Snowflake is approved — now comes execution.

Snowflake migrations drag on for predictable reasons: SQL that doesn't port cleanly, hidden object dependencies, cutover plans that were never tested, and validation treated as optional. Teams that haven't mapped the full scope of their Snowflake implementation — from ingestion architecture to warehouse configuration — routinely discover blockers mid-build that were visible from day one.

Aegis has delivered 50+ migrations from every major legacy platform. We compress timelines with AI-powered tooling, protect continuity with phased playbooks, and use source-specific patterns built from real delivery, not vendor documentation. If you're still evaluating whether Snowflake is the right destination for your workloads, our Snowflake consulting services can help you build the business case and platform strategy before migration begins.

Your legacy warehouse has an expiry date. Start the clock on Snowflake.
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Migration Challenges

Why Snowflake Migrations Take Longer Than Scoped And How We Prevent It?

Every Snowflake migration team underestimates the same set of problems. These are the ones that blow timelines, inflate budgets, and cause post-cutover incidents.

ChallengeWhat goes wrong?How do we fix it?
Manual SQL rewritingBTEQ scripts, PL/SQL procedures don't port automatically — manual translation at scale takes monthsAI-powered SnowConvert handles bulk conversion; engineers review what it flags
Hidden dependenciesObject inventories miss what was actually built — blockers surface mid-buildFull dependency mapping in Phase 1, before conversion starts
Cutover riskNo tested rollback plan means any production issue becomes a crisisParallel running on every migration; rollback rehearsed before cutover
Late-stage validationValidating only at the end leaves no time to fix what's brokenAutomated reconciliation runs on every migration wave, not just at cutover
Snowflake anti-patternsReplicating an on-prem model in Snowflake produces a warehouse that runs but underperformsSchema redesign where source constraints don't belong in Snowflake
Business continuity ignoredCutover planned around the migration team, not the business calendarCutovers scheduled around your operational windows — month-end, peak periods, reporting cycles
Avoid the migration bottlenecks that typically delay Snowflake projects and increase post-cutover risk.
Talk to a Snowflake Migration Specialist
Snowflake migration consultant helping businesses modernize Teradata, Oracle, Redshift, SQL Server, and Hadoop data platforms

Our Snowflake Migration Methodology

Snowflake Migrations fail when phases are rushed or skipped. Our seven-phase delivery model is sequenced to surface risk early, validate continuously, and protect your production environment throughout the cutover window.

We audit the source environment before committing to a timeline or budget.

  • Full object inventory: tables, views, stored procedures, functions, ETL jobs, scheduled scripts
  • Data volume and growth rate profiling by schema and table
  • SQL dialect complexity analysis: what converts automatically, what requires manual review
  • Dependency mapping: object-to-object, ETL-to-table, report-to-view
  • Compliance and data classification requirements identified upfront
  • Go/no-go criteria defined and agreed with stakeholders

Deliverable: Snowflake Migration assessment report — complexity score by object type, risk register, realistic timeline, and resource plan.

The target architecture is designed before any conversion begins. Our Snowflake services team brings that architectural depth into every migration engagement from Phase 2 onwards.

  • Source data profiling: data types, nullability, value distributions, anomalies, data quality issues
  • Target schema design: Snowflake-native, not a replica of the source
  • Clustering key strategy designed from query patterns, not assumed from source indexes
  • Warehouse configuration plan: workload separation, sizing, auto-suspend settings
  • RBAC design aligned to source access controls and target compliance requirements
  • Ingestion architecture for ongoing loads post-cutover

Deliverable: Target architecture design document, schema mapping specification, RBAC design, warehouse configuration plan.

AI-powered tooling handles the bulk of conversion. Human engineers handle what they can't.

  • SnowConvert was deployed against the full source object inventory
  • Automated conversion of SQL DDL, DML, stored procedures, views, and functions
  • Conversion report generated: objects converted automatically, objects requiring manual review, objects requiring rewrite
  • Manual review and rewrite of flagged objects by Snowflake-certified engineers
  • ETL jobs assessed: automated migration where possible, rewrite to Snowpipe/dbt/Airflow where replacement is the better path

Deliverable: Converted object library, conversion completeness report, and manual rewrite log.

Validation is built alongside conversion, not after it.

  • Automated reconciliation suite: row counts, aggregates, business metric comparisons, null rate checks
  • Statistical distribution validation for critical tables, not just row count matching
  • Performance benchmark suite built against agreed query SLAs
  • Data quality test suite covering every pipeline and transformation layer
  • Regression test suite for downstream BI reports and dashboards

Deliverable: Automated validation suite, benchmark query library, regression test pack.

Migration runs in waves. High-risk objects migrate last.

  • Wave 1: Historical and reference data — lowest risk, validates the base environment
  • Wave 2: Core transactional tables and dimensions — primary business data
  • Wave 3: Stored procedures, complex transformations, and edge-case objects
  • Parallel running begins: legacy and Snowflake environments operating simultaneously
  • Automated reconciliation runs daily across both environments
  • Issues tracked, resolved, and re-validated before proceeding to the next wave

Deliverable: Wave completion reports, reconciliation results by wave, and issue log with resolutions.

Cutover is a managed event, not a moment of uncertainty.

  • Cutover rehearsal run in the staging environment before production cutover
  • Cutover plan distributed to all stakeholders — timing, responsible parties, decision points
  • Rollback procedure documented and tested — if production cutover fails, the path back to legacy is clear and fast
  • Production cutover executed during the agreed low-traffic window
  • Hypercare team on standby for 72 hours post-cutover
  • The legacy environment is kept live in read-only mode for a defined decommission window

Deliverable: Cutover execution log, go/no-go sign-off, hypercare incident log.

The migration isn't finished when the data is moved.

  • Performance baseline established post-cutover: query times, credit consumption, warehouse utilization
  • Optimization pass on any queries or warehouses underperforming against targets
  • Governance configuration validated in production: masking policies, audit logging, lineage tracking
  • Full documentation package: architecture decision records, schema mapping, pipeline runbooks, RBAC inventory
  • Team enablement sessions: your engineers are trained on the migrated environment and ongoing operational procedures
  • 30-day post-cutover support window included

Deliverable: Performance baseline report, optimization log, full documentation package, team training, 30-day hypercare.

Data is on Snowflake. Next: build production pipelines on top of it.
Snowflake Implementation
Snowflake migration consultant helping enterprises modernize legacy data warehouses and plan cloud migration strategies

Migration Tools & Accelerators

The difference between a 10-week migration and a 6-month one often comes down to tooling. We use a combination of purpose-built migration accelerators and custom-built validators, not manual conversion at every layer.

What do we use to compress your migration timeline?

ToolWhat it doesWhere we use it
SnowConvertAI-powered automated conversion of SQL DDL/DML, stored procedures, views, functionsFirst pass on every migration — engineers handle what it flags
Snowpark Migration AcceleratorEvaluates Spark jobs, ML pipelines, and procedural ETL for native Snowflake execution via SnowparkWorkloads currently running outside the warehouse
Custom validation frameworkSource-specific reconciliation — row counts, aggregates, metric comparisons, distributionsRuns automatically on every migration wave
Snowflake zero-copy cloningIsolated test environments with no storage cost — conversion validation, parallel run schemas, rollback snapshotsThroughout every migration phase
dbtRebuilds transformation logic with version control, automated testing, and documentationWhere stored procedures are better replaced than migrated

Which Migration Approach Fits Your Situation?

Not every migration should be approached the same way. The right approach depends on your source complexity, business risk tolerance, timeline, and the extent to which the legacy data model is worth preserving.

Lift-and-shift: Fastest timeline

Migrate schema, data model, and transformation logic to Snowflake with minimal redesign. Fastest to execute — optimization handled in a subsequent phase.

  • Risk: Medium technical debt carried forward if the optimization phase isn't committed to
  • Redesign: None schema migrated as-is
  • Sources: Redshift SQL, Server DB2
  • Best for: License expiry or hardware EOL deadlines, time-pressured exits, teams with internal capacity to optimize post-migration.

Re-architect: Highest long-term ROI

Redesign the data model in Snowflake during migration. Clustering keys from real query patterns. Transformations rebuilt in dbt. Ingestion is rebuilt with Snowpipe or Kafka.

  • Risk: Lower long-term legacy constraints eliminated upfront
  • Redesign: Full purpose-built for Snowflake's architecture
  • Sources: Teradata, Oracle, Hadoop
  • Best for: Platforms with heavy on-prem customization (BTEQ, PL/SQL) where the source model reflects constraints that don't belong in Snowflake.

Phased migration: Lowest risk per wave

Migrate in domain waves, finance first, then marketing, then operations. Each wave has its own validation and cutover. Legacy runs in parallel per domain until that domain cuts over.

  • Risk: Distributed blast radius limited to one domain at a time
  • Redesign: Per-wave decision can vary by domain
  • Sources: Any large environment, Multi-domain
  • Best for: Large environments, multiple business units with different risk tolerances, or where the legacy platform is retained for some workloads post-migration.

Big bang: Single cutover event

Full migration in one cutover window. Legacy and Snowflake run in parallel for a defined validation period, then the entire environment switches over in a single managed event.

  • Risk: Highest at cutover requires thorough pre-cutover validation and a tested rollback plan
  • Redesign: Optional, depends on source complexity
  • Sources: Smaller environments, SQL Server, Redshift
  • Best for: Smaller environments where a single cutover is operationally simpler than phased waves, and teams with high confidence in their validation results.
Need help choosing the right migration strategy for your timelines, risk profile, and legacy environment?
Get a Snowflake Migration Recommendation
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Source Platforms We Migrate to Snowflake

We have delivered migrations from every major legacy data platform. Each source brings a specific set of conversion challenges. Our playbooks are source-specific — not generic migration methodology applied regardless of where you're starting.

Teradata to Snowflake

The highest-complexity migration we handle. Teradata's primary index design, BTEQ scripting, TPT jobs, and proprietary SQL extensions require systematic conversion — not just syntax replacement. SnowConvert handles the bulk of automated code translation. Teradata's workload management (TASM/TIWM) needs to be replicated as Snowflake warehouse configurations and resource monitors. We have delivered multi-terabyte Teradata migrations with parallel running windows of four to six weeks before cutover.

Key conversion challenges: BTEQ → Snowflake Tasks/Stored Procedures, Teradata-specific SQL functions, primary index → clustering key strategy, TPT jobs → Snowpipe/Kafka pipelines.

Amazon Redshift to Snowflake

Redshift migrations carry a different risk profile — the SQL dialect is closer to standard, but the architectural assumptions are not. Redshift's distribution keys and sort keys have no direct equivalent in Snowflake. Redshift Spectrum workloads need to be evaluated against Snowflake's external table and Iceberg support. Organizations migrating from Redshift are often cloud-native already — the migration is typically faster, but performance validation requires specific attention to concurrency and multi-cluster warehouse design.

Key conversion challenges: distribution/sort key → clustering key mapping, Redshift Spectrum → Snowflake external tables, COPY commands → Snowpipe, spectrum-heavy workloads → Iceberg table design.

Oracle to Snowflake

Oracle PL/SQL stored procedures are the primary complexity driver. Oracle-specific functions, packages, and procedural logic require significant rewriting for Snowflake compatibility — SnowConvert handles the initial conversion pass, but Oracle procedural complexity typically requires more human review than other platforms. Oracle's partitioning schemes need to be evaluated against Snowflake's micro-partition architecture. AWR-based performance baselines from Oracle need to be translated into Snowflake Query History equivalents.

Key conversion challenges: PL/SQL packages → Snowflake Stored Procedures/JavaScript/Python UDFs, Oracle partitioning → clustering keys, Oracle-specific functions (DECODE, CONNECT BY, MERGE syntax), DBLink dependencies.

SQL Server to Snowflake

SQL Server migrations are typically the lowest-complexity source conversion but carry integration risk — SQL Server is often tightly coupled with SSIS packages, SSRS reports, and Power BI datasets that all need to be re-pointed or rebuilt. T-SQL conversion to Snowflake SQL is largely automated. The greater effort is usually dependency mapping across the Microsoft toolchain and validating BI layer compatibility post-cutover.

Key conversion challenges: T-SQL → Snowflake SQL, SSIS packages → Snowpipe/Fivetran/dbt pipelines, SSRS reports → cloud BI tool re-pointing, linked server dependencies.

Hadoop / on-premises Data Lakes to Snowflake

Hadoop migrations — HDFS, Hive, HBase, Spark — involve moving both the data and the processing logic. Hive SQL converts reasonably well to Snowflake SQL. Spark jobs need to be evaluated: some translate cleanly to Snowpark, and dbt transformations inside Snowflake better replace others. HDFS storage moves to cloud object storage (S3, Azure Blob, GCS) with Snowflake external stages. Schema-on-read patterns need to be replaced with governed, structured schemas in Snowflake.

Key conversion challenges: Hive DDL → Snowflake DDL, Spark jobs → Snowpark or dbt, HDFS → cloud object storage + Snowflake external stages, Hive partitioning → clustering keys, schema-on-read → governed table design.

Other Sources

We have also delivered migrations from IBM Db2, Greenplum, Netezza, Cloudera, and Sybase. If your source platform isn't listed, contact us — we assess any legacy environment and scope a migration plan specific to your platform's conversion requirements.

What Happens After Snowflake Migration — The First 30 Days

Moving the data is the beginning, not the end. The three to four weeks after cutover determine whether the migration delivers the performance and cost profile the business was promised.

Week 1

Performance baseline: Query execution times, credit consumption, and warehouse utilization measured against the targets agreed in Phase 1. Any shortfall gets a root cause: clustering keys, warehouse sizing, or query rewrites. Optimization pass included — not sold separately.

Weeks 1–2

Governance validation: Masking policies, row-level security, and audit logging validated under real production access patterns. Staging behavior and production behavior differ. We don't hand over until both matches.

Days 1–30

Ingestion monitoring: Pipelines monitored for latency, failure, and volume anomalies while your team builds operational familiarity with the new environment. Alerting is configured and handed over before the 30-day window closes.

On Completion

Legacy decommission plan, a decommission checklist, and a recommended shutdown timeline based on post-cutover stability. Running the legacy environment indefinitely is a cost, not a safety net.

For teams that want to continue building capability on the migrated platform: new pipelines, BI layers, ML workloads. Our Snowflake services team picks up where the migration ends, without the handoff risk of switching to a new delivery partner.

Migration Compliance and Domain Knowledge by Vertical

Snowflake migration solution complexity is not just technical. Compliance requirements, data residency rules, and audit obligations vary by industry, and they affect migration design, parallel running windows, and cutover procedures. Our Snowflake migration engineers bring vertical-specific knowledge, not just platform expertise.

IndustryCompliance angleKey migration consideration
Retail & CPGPII data residency across regionsCutover timed around peak trading windows — promotions, seasonal peaks, fiscal year-end
Financial ServicesSOC 2 Type II, Basel III, PCI-DSS, FedRAMPAudit trail continuity across legacy and Snowflake for the full parallel running period
HealthcareHIPAA throughoutPHI masking validated before any patient data migrates; BAA with Snowflake required first
ManufacturingISO 27001, GDPRIoT/sensor data at full production volumes — no sampled subset performance testing
Technology & SaaSSOC 2 Type IIRow-level security validated per tenant before cutover — one misconfiguration is a breach
Public Sector & EducationFedRAMP, FERPA, CJISCloud region constrained by compliance; ATO processes may extend the timeline.
Media & EntertainmentGDPRBehavioral, segmentation, and ad targeting data encoded with retention and consent rules in Snowflake schema
CybersecurityHigh-volume telemetryQuery performance validated at full production data volumes — security analysts can't absorb regression

Snowflake Migration Case Studies

40TB Teradata → Snowflake | Global Retail Enterprise

  • 1,400 tables, 340 stored procedures, 200+ TPT jobs
  • 78% of objects converted automatically by SnowConvert; 22% manually reviewed
  • Five migration waves over 8 weeks; parallel running throughout
  • Cutover: Saturday night, zero production disruption
  • Result: Query time halved. Snowflake spend 34% below Teradata TCO in month one.

Redshift → Snowflake | B2B SaaS Analytics Platform

  • 180 tables, Redshift Spectrum workloads, existing dbt layer
  • 91% SQL conversion automated; Spectrum migrated to external tables
  • 3-week parallel run; multi-cluster data warehouse configured for auto-scaling
  • Result: Concurrent query performance is 3x better under peak load. Manual cluster management eliminated.

Oracle → Snowflake | Regional Financial Services Firm

  • 420 stored procedures; 61% auto-converted, 39% required manual PL/SQL rewrite
  • CONNECT BY, DBMS packages, MERGE syntax rewritten in 3 weeks
  • Tag-based column masking applied to all PII and regulatory fields
  • Result: Cutover met financial year-end deadline. First regulatory report passed internal audit with no findings.
40TB moved. Zero disruption. Your migration could be next.
Book a 30-Minute Consultation
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Why Organizations Choose Aegis for Snowflake Migration?

  • Source-specific playbooks:
    Teradata, Oracle, Redshift, SQL Server, and Hadoop each have different conversion challenges, ETL redesign requirements, and cutover risk profiles. Our playbooks are built from 50+ real migrations, not adapted from generic methodology.
  • AI conversion + human oversight:
    SnowConvert handles the automated pass. Certified engineers handle what it flags. Neither alone is sufficient at scale.
  • Parallel running on every engagement:
    Legacy and Snowflake run simultaneously, validated daily, before decommission. We don't do big bang cutovers without a tested rollback.
  • Validation at every wave, not just cutover:
    Automated reconciliation runs throughout execution. Issues are caught and fixed at the layer where they were introduced.
  • We stay through decommission:
    The engagement ends when the legacy platform is off, your team is trained, and you own the environment. Not when the data moves.
Plan your Snowflake migration with a team that has handled complex warehouse modernisation across Teradata, Oracle, Redshift, SQL Server, and Hadoop.
Talk to a Snowflake Migration Expert
Snowflake migration expert helping businesses migrate data warehouses

Our Team

Yash Shah - Snowflake Implementation Consultant
Yash Shah

Snowflake Implementation Consultant

Harsh Savani- Director & Head of Operations
Harsh Savani

Director & Head of Operations

Rajen Raiyarela - Delivery Head, Aligning Team-Outcomes
Rajen Raiyarela

Delivery Head, Aligning Team-Outcomes

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TABLE OF CONTENTS

Migrate Your Legacy

Migration Challenges

Our Methodology

Tools & Accelerators

Approach Fits Your Situation

Source Platforms

The First 30 Days

Migration Compliance

Snowflake Migration Case Studies

Why Choose Us?

FAQ

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FAQs

SQL Server or Redshift with limited procedural complexity: 6–8 weeks. Large Teradata environments with hundreds of BTEQ scripts: 10–14 weeks. The timeline is set in Phase 1 based on the actual object inventory — not a generic estimate.

Costs range from $30K for small migrations to $2M+ for enterprise. Key drivers include data volume, source system complexity, custom logic rewriting, and whether you use an in-house or partner team.

Typically, 2–4 months for small warehouses and 9–18 months for large enterprises. Teradata's proprietary SQL dialect and BTEQ scripts make it one of the more time-intensive migration paths available.

Timelines inflate due to complex stored procedures, poor data lineage, and scope creep. Compress them using automated conversion tools, wave-based migration, parallel workstreams, and a dedicated cross-functional squad focused solely on migration.

Run Snowflake in parallel with your legacy system, use CDC tools for continuous data replication, validate parity, then cut over during a low-traffic window with an instant rollback plan ready.

Yes. Tools like SnowConvert automate much of the PL/SQL translation. Complex logic like cursors and exception handling needs manual rewriting, but most Oracle workloads migrate successfully with proper planning and testing.

Access controls and masking policies must be rebuilt natively in Snowflake using Role-Based Access Control and Dynamic Data Masking. It's an opportunity to modernize and simplify your security model significantly.

Post-migration, Snowflake can be managed by your internal data engineering team, a managed services partner, or a hybrid model. Ongoing tasks include cost optimization, performance tuning, user management, and pipeline monitoring.