How long does a data platform migration take?

It is scope-dependent. The Migration Blueprint produces an executable plan in roughly 10 business days for a bounded environment, and a clearly defined first wave typically reaches a reconciled, production-ready workload in about 4-8 weeks. Those timings describe a first wave, not a promise to move an entire environment.

How do you preserve business logic, KPIs, and reporting accuracy?

We inventory business logic across stored procedures, ETL, and BI, rebuild it target-native where a one-to-one translation would carry old technical debt, and accept each wave on data reconciliation plus business-rule and KPI parity, with business-owner sign-off before the old path is retired.

Who owns the platform and what support is available after cutover?

You do. Ownership, access, handoff, and post-cutover support are defined in the statement of work before delivery begins. Support options include stabilization, pipeline monitoring, performance and cost optimization, enablement, and managed support.

Data platform migration for established businesses

Reach an accepted migration outcome faster, with lower delivery overhead.

Move SQL Server, SSIS, Oracle, Synapse, Redshift, and older ETL tools to Microsoft Fabric, Snowflake, or Databricks, and keep the KPIs, reports, and controls the business trusts.

See the 3ALICA Delivery Model Scope the First Migration Wave

Start with one bounded workload, not a full-program commitment.

Current environment

SQL ServerSSISSSASSSRSOracleTeradataSynapseAzure Data FactoryRedshift & GlueInformaticaTalendDataStageBigQueryOther ETL tools & flat files

3ALICA migration system

1. Inventory

2. Map

3. Convert

4. Reconcile

5. Cut over

Native tools + 3ALICA playbooks + AI-assisted production + senior gates

Senior control gates at every step

Target platform

Microsoft Fabric

Snowflake

Databricks

Amazon Redshift / S3 / Glue supported as a secondary route

15+ years

In data, DWH, BI & analytics

200+

Data, DWH & BI projects

40+

Delivery specialists

US & EU

Delivery footprint

A bounded first workload can reach reconciled production in about 4-8 weeks, scope-dependent.

Selected brands our team has worked with

Experience across data, BI, analytics, and operational systems. See approved case studies for engagement-specific evidence.

Platforms and tools we work with

Microsoft Fabric
Snowflake
Databricks
AWS
dbt
Fivetran
Airbyte
Tableau
Qlik
Power BI

We work from your chosen target architecture across Microsoft Fabric, Snowflake, Databricks and AWS, with dbt, Fivetran, Airbyte, Power BI, Tableau and Qlik in the surrounding stack, we do not push a single vendor.

What this looks like in practice

One client migration, and two shapes we see most often

Client engagement

Analytics moved to AWS with no reporting downtime

Situation: A company wanted its analytics on AWS for cost and flexibility. The daily reports the business ran on could not stop, and a straight lift-and-shift would have carried the old problems across.
What we did: We re-engineered the pipelines for Amazon Redshift and Apache Airflow, then moved 50+ schemas and pipelines with validation against the source.
What the business got: A clean cutover with no downtime, on a platform the company controls, with faster queries than before.

See the AWS migration

A pattern we see often

The SSIS package that cannot be translated one-to-one

Situation: A package built over years carries script tasks and lookups against tables nobody remembers. A converter translates the syntax and leaves the meaning behind.
What we did: We inventory what the package actually decides, rebuild that logic target-native, and reconcile its outputs against the old path before anything is retired.
What the business got: The package is retired only after its outputs match the old path across an agreed set of runs.

A pattern we see often

The stored procedure the month-end close depends on

Situation: One large SQL Server procedure produces the numbers finance signs off on, and it does not run on the target platform as written.
What we did: We agree with finance which numbers count and to what tolerance, rebuild the calculation in target-native transformations, and dual-run it across prior closes.
What the business got: Cutover waits until the agreed figures reconcile within the tolerance finance set, with the old path still running beside the new one.

The first is a client engagement with the figures from its case study. The other two are patterns from our delivery work, not named clients: the shape is real, the details are composites.

Why 3ALICA

A better delivery system, not a bigger consulting team.

Traditional migrations add people as scope grows and move through discovery, conversion, testing, and cutover with expensive handoffs. 3ALICA uses a lean senior pod, platform-native tools, reusable source-to-target playbooks, and AI-assisted production to run more repeatable work in parallel. Senior owners stay responsible from architecture through acceptance and cutover.

Traditional consulting model

Sequential discovery → staffing ramp → manual production → team handoffs → slow proof and rising delivery cost

Delivery capacity grows mainly by adding people and hours.

3ALICA delivery model

Lean senior pod + platform-native tools + reusable playbooks + AI-assisted production + senior acceptance gates

More work in parallel → faster accepted workload → lower delivery overhead

The production system does more of the repeatable work. Senior accountability remains.

AI and platform tools assist	Senior 3ALICA owners decide and approve
Estate inventory and dependency extraction	What to migrate, modernize, or retire
Code / schema analysis and baseline conversion	Unsupported logic and target-native redesign
Documentation and test generation	Security, recovery, and acceptance criteria
Technical reconciliation evidence	Business-rule, report, and KPI parity
Repeatable deployment mechanics	Coexistence, rollback, cutover, and decommissioning

Faster because repeatable work runs in parallel, not because controls are skipped.

When this fits

Your target is chosen. We get the first workload accepted.

Data platform migration moves warehouse data, pipelines, stored logic, semantic models, and reporting from an older or fragmented environment to a modern platform while preserving the business outputs people rely on.

Fabric, Snowflake, or Databricks is selected, but accountable migration ownership is missing.
Finance, operations, or leadership reporting must reconcile before the old path can retire.
The first production workload must prove the timeline, quality, and economics before the full program is approved.

From the stack you need to retire to the platform you already chose.

Microsoft environment → Microsoft Fabric

From

SQL Server, SSIS, SSAS, SSRS, Azure Synapse Analytics, Azure Data Factory, Power BI dependencies

Fabric Warehouse, Fabric Lakehouse, OneLake, Data Factory in Microsoft Fabric, Power BI, governed semantic models

Best fit for a Microsoft-heavy environment with clear standardization and retirement triggers.

Older DWH / ETL → Snowflake

From

SQL Server, Oracle Database, Teradata, Amazon Redshift, Informatica, Talend, IBM DataStage, custom ETL

Snowflake, dbt, Fivetran or Airbyte where selected, governed BI and semantic models

Best fit when Snowflake is chosen but pipelines, modeling, reconciliation and retirement still have to be built and verified.

Fragmented or older analytics → Databricks

From

SQL Server / Synapse, Oracle / Teradata, Redshift / AWS Glue, BigQuery, selected Snowflake workloads

Delta Lake, Unity Catalog, Databricks Workflows, dbt, Databricks SQL, governed Lakehouse patterns

Best fit for fragmented pipeline and reporting environments moving to a Lakehouse operating model.

Common source-to-target patterns

SQL Server / SSIS / SSAS → Snowflake
SQL Server / SSIS / Synapse → Microsoft Fabric
Oracle / Teradata → Snowflake or Databricks
Redshift / Glue or BigQuery → Databricks
Informatica, Talend or DataStage → target-native ingestion, transformation and orchestration
Older BI & semantic models → Power BI or a governed semantic layer

AWS remains a supported cross-cloud or secondary target route. We work from your chosen target architecture rather than pushing one vendor.

Engagement path

Start with one accepted workload. Scale after the numbers match.

Blueprint

Migration Blueprint: Scope, Parity and Cutover

~10 business days for a bounded environment, scope-dependent

Source, workload and pipeline inventory
Dependency and source-to-target map
Critical KPI / report parity scope
Migration-wave plan and target architecture decisions
Reconciliation, cutover, rollback, risk, effort range and first-wave SOW

First wave

First Reconciled Migration Wave

~4-8 weeks for one bounded workload, scope-dependent

Production-ready and reconciled against the current source path
Accepted against explicit business and technical criteria
Documented and handed over
Scope such as one domain, one data mart, one SSIS package group, one older database or one reporting domain

Scale

Scale, Cut Over & Support

After the numbers match

Additional controlled waves with coexistence and dual-run where needed
Production cutover and retirement of the old platform
Stabilization, monitoring, cost and performance optimization
Documentation, enablement, and optional managed support

A blueprint, a first workload, and the full migration are three different commitments. The 4-8 week estimate applies to a bounded first workload, not an entire environment.

Request a migration scoping call Ask a senior data architect

The people who own the outcome

Senior accountability does not end after discovery.

The people who shape the architecture stay close to the exceptions, parity decisions, and cutover. The senior team that signs the scope is the team that delivers it.

Alex Gonchar

Co-founder / CEO

Founder and operator focused on Data, AI, operational systems, US GTM, and productized delivery.

Vladimir Gorshunov

Co-founder / CTO

Technical leader focused on BI, DWH, Data platforms, modernization, AI, software delivery, and production-grade systems.

Meet the 3ALICA team Read verified client feedback on Clutch View case studies

Verified client feedback covers BI, Big Data, software development, and AI delivery. Review engagement-specific evidence before applying any client result to a migration claim.

Your cloud accounts, code, documentation, and runbooks remain yours. Ownership, access, handoff, and post-cutover support are defined in the statement of work before delivery begins.

Before the scoping call

Related capabilities

Need to improve the warehouse without re-platforming? See Data Warehouse Modernization. Moving applications, databases or infrastructure rather than the data platform? See Legacy Modernization.

Data engineering capability Case studies Partner delivery model

Scope the first wave

Know the source, the target, and the workload that cannot break? Let's scope the first accepted wave.

Share the source platform, target platform, and the workload that cannot break. A senior data architect will review the fit and propose the smallest useful first step.

Email us directly

sales@3alica.com