Data Migration Companies
Data migration companies move your data from a legacy system to a modern cloud warehouse, lakehouse, or database - covering discovery, schema and pipeline re-engineering, validation, cutover, and decommissioning. The firms below are rated Expert or Strong in platform-migration capability in our directory and listed alphabetically by default; compare by fit, not by position.
Data Migration Companies
75 firms · listed A-Z
Inclusion criteria: every firm below is rated Expert or Strong in platform-migration capability in our directory assessment. This is a capability cut, not a quality ranking - order is alphabetical.
| Company | Migration | Best For | Evidence |
|---|---|---|---|
| Accenture | Expert | Large enterprises running multi-cloud transformations across AWS, Azure, and GCP simultaneously, where a single integrator needs to own the full program. | Listed, not reviewed |
| Adastra | Expert | Enterprise data, cloud, and analytics work across financial services, insurance, retail, and other sectors. Ask for references from similar projects. | Reviewed |
| Aimpoint Digital | Expert | Consider Aimpoint for programs using Snowflake, Databricks, and dbt. It lists credentials for all three; ask for relevant references and the names of the proposed consultants. | Reviewed |
| Airbyte Services | Strong | Custom connector development and large-scale data replication | Listed, not reviewed |
| Algoscale | Strong | Data engineering and analytics; distributed data processing | Listed, not reviewed |
| Analytics8 | Expert | Mid-market companies needing end-to-end data solutions; data modernization projects | Listed, not reviewed |
| Atrium | Expert | Snowflake and Salesforce integration; AI-native consulting | Listed, not reviewed |
| Avenga | Strong | Regulated industries; nearshore teams; life sciences and finance | Listed, not reviewed |
| Bain & Company | Strong | Private equity firms and portfolio companies that need due-diligence analytics strategy on Snowflake. Ask which implementation work Bain will own. | Listed, not reviewed |
| BCG X | Strong | Boards and executive teams commissioning a deep-tech or AI venture build through BCG X. Confirm who owns engineering delivery alongside the strategy work. | Listed, not reviewed |
| Beyond Key | Strong | Microsoft technologies and PowerBI consulting; .NET development | Listed, not reviewed |
| BigData Boutique | Expert | Open-source big data; Elasticsearch and OpenSearch specialists | Listed, not reviewed |
| BIZTORY | Strong | Asian markets; Microsoft Azure and PowerBI specialists | Listed, not reviewed |
| BlueCloud | Strong | Mid-market companies modernizing to a cloud data stack on Databricks or Snowflake with AWS or Azure. Ask for comparable implementations and the proposed team. | Listed, not reviewed |
| Brooklyn Data Co | Strong | Companies building or maturing a dbt-centered data stack with Snowflake, Looker, and Fivetran. Brooklyn Data is now part of Velir; ask for references matched to your scope. | Listed, not reviewed |
| Capgemini | Expert | European industrial and engineering-intensive enterprises running Industry 4.0 or R&D data programs where manufacturing-domain depth and on-continent delivery are requirements. | Listed, not reviewed |
| Celebal Technologies | Expert | Microsoft Azure specialists; PowerBI and AI solutions | Listed, not reviewed |
| Cognizant | Expert | Large retailers and consumer-goods companies running GenAI modernization programs that need a large delivery bench and long-standing enterprise relationships. | Listed, not reviewed |
| Confluent | Strong | Enterprise-scale event streaming and data in motion | Listed, not reviewed |
| Continuus Technologies | Expert | Financial-services data cloud work on Snowflake, FactSet, or SimCorp. Confirm any required Snowflake partner tier. | Listed, not reviewed |
| Damco Solutions | Strong | Enterprise data modernization; Big Data solutions | Listed, not reviewed |
| Data Driven | Strong | Modern data stack implementation and analytics engineering | Listed, not reviewed |
| DataArt | Strong | Custom software development with data engineering; European nearshore | Listed, not reviewed |
| Datacoves | Strong | dbt implementation and analytics engineering workflow optimization | Listed, not reviewed |
| Datalytyx | Strong | Data governance and managed data services | Listed, not reviewed |
| DATAPAO | Expert | European companies running Databricks on Azure or AWS that need MLOps and Spark/Kafka expertise. Confirm current credentials and the proposed consultants. | Listed, not reviewed |
| Dataroots | Strong | AI-driven data engineering and MLOps implementation | Listed, not reviewed |
| Dateonic | Expert | Teams building or scaling a Databricks or MLflow-based ML platform on AWS, Azure, or GCP. Ask for matching project references and named specialists. | Listed, not reviewed |
| dbt Labs Services | Strong | Teams migrating existing analytics code to dbt, standardizing dbt practices, or training analytics engineers with the creators of dbt. Confirm the proposed instructors. | Listed, not reviewed |
| Deloitte | Expert | Regulated-industry enterprises (healthcare systems, banks, insurers) that need C-suite advisory, compliance framing, and Big Four sign-off alongside the technical delivery. | Listed, not reviewed |
| Devoteam | Expert | European enterprises; cloud and cybersecurity specialists | Listed, not reviewed |
| DS Stream | Strong | AI and data analytics for global brands; GenAI solutions | Listed, not reviewed |
| Entrans | Expert | End-to-end data engineering; data lakehouse implementations | Listed, not reviewed |
| EY | Expert | Global compliance, audit-ready data platforms, and finance transformation | Listed, not reviewed |
| Fivetran Services | Expert | Fivetran implementation, connector work, and assisted transformations, including data modeling, SQL, and dbt. | Reviewed |
| Fractal Analytics | Strong | Enterprise AI and decision intelligence for large enterprises | Listed, not reviewed |
| Hakkoda | Expert | Healthcare and financial-services teams building Snowflake data platforms where compliance experience matters. Ask for references that match your requirements. | Listed, not reviewed |
| Hashmap | Expert | Enterprises needing cloud migrations and IoT data solutions | Listed, not reviewed |
| HCLTech | Expert | Large-scale migrations off older systems and managed services outsourcing | Listed, not reviewed |
| Improving | Strong | Software consultancy with data engineering; Agile delivery | Listed, not reviewed |
| Indium Software | Expert | Product engineering with data modernization; Digital assurance | Listed, not reviewed |
| Infostrux | Expert | Data teams adopting Data Vault methodology on Snowflake with dbt. Ask for Data Vault 2.0 references and the proposed consultants. | Listed, not reviewed |
| Infosys | Expert | Global enterprises; offshore development model; large-scale implementations | Listed, not reviewed |
| Innowise | Strong | Full-cycle software development with data engineering; Eastern Europe | Listed, not reviewed |
| Intellias | Strong | Automotive, fintech, and large-scale engineering projects | Listed, not reviewed |
| InterWorks | Expert | BI and analytics deployments; Tableau and Snowflake specialists | Listed, not reviewed |
| iTechArt | Strong | VC-backed startups and rapidly scaling tech firms | Listed, not reviewed |
| Itransition | Strong | Mid-market companies; full-cycle software development with data engineering | Listed, not reviewed |
| Kanerika Inc | Strong | Intelligent automation and data analytics; Microsoft Azure specialists | Listed, not reviewed |
| KPMG | Expert | Risk management, regulatory reporting, and finance back-office data | Listed, not reviewed |
| Lovelytics | Expert | Companies seeking Snowflake-to-Databricks migration; cloud data platform specialists | Listed, not reviewed |
| LTM | Expert | Snowflake migrations for large enterprises | Listed, not reviewed |
| Mantel Group | Expert | Australia and New Zealand enterprises considering Databricks or Snowflake work, including regulated-industry programs. Verify required partner credentials and domain references. | Listed, not reviewed |
| McKinsey & Company | Strong | Large-scale digital transformation and strategy-led AI initiatives | Listed, not reviewed |
| Mphasis | Expert | Banking and capital-markets firms running structured data modernization programs on Snowflake where financial-services domain expertise is a baseline requirement. | Listed, not reviewed |
| N-iX | Expert | European nearshore development; enterprise clients | Listed, not reviewed |
| Perficient | Expert | Digital transformation; enterprise data and analytics | Listed, not reviewed |
| phData | Expert | Consider phData for Snowflake data engineering, migrations, and SAP-to-Snowflake analytics. Snowflake confirms its Elite tier; check references for your source systems and agree on the work before hiring. | Reviewed |
| Pingahla | Strong | Data engineering and analytics for startups and mid-market | Listed, not reviewed |
| ProCogia | Expert | Data consultancy and bioinformatics; enterprise data mesh | Listed, not reviewed |
| PwC | Expert | Busines-led transformation and finance function modernization | Listed, not reviewed |
| Saviant Consulting | Strong | Microsoft Azure specialists; Industrial IoT and smart machines | Listed, not reviewed |
| ScienceSoft | Strong | Healthcare and financial services; compliance-focused data solutions | Listed, not reviewed |
| Sigmoid | Strong | Consider Sigmoid for ML engineering and data platform work across Snowflake, Databricks, and the major clouds. Confirm target-platform references and a current quote. | Listed, not reviewed |
| Simform | Strong | Consider Simform when application development and cloud data infrastructure need to be delivered together across AWS, Azure, GCP, Databricks, and Snowflake. Confirm workstream ownership. | Listed, not reviewed |
| Slalom | Expert | Consider Slalom for enterprise digital transformation, including AWS and GenAI programs. Ask for comparable implementations and the proposed cloud and data engineering team. | Listed, not reviewed |
| Solita | Expert | Nordic organizations considering Snowflake or broader data transformation. Verify required partner credentials and request references for the target platforms. | Listed, not reviewed |
| STX Next | Expert | European nearshore data engineering for fintech, manufacturing, or logistics. Ask for relevant project references and verify any required AWS or Snowflake credentials. | Listed, not reviewed |
| Tata Consultancy Services (TCS) | Expert | Multinational enterprises considering multi-year data platform transformation with an offshore delivery component. Confirm regional coverage and staffing commitments in the proposal. | Listed, not reviewed |
| Tech Mahindra | Strong | Telecom operators and large manufacturers running multi-year data platform programs where offshore delivery economics and domain-specific process knowledge are primary selection criteria. | Listed, not reviewed |
| Thoughtworks | Expert | Organizations adopting data mesh and modern data architecture. Ask Thoughtworks for comparable implementations and a delivery plan suited to the organization's operating model. | Listed, not reviewed |
| Tiger Analytics | Strong | Consider Tiger Analytics for retail and CPG analytics, AI/ML, and GenAI programs. Ask for relevant implementations and named specialists. | Listed, not reviewed |
| Tredence | Expert | Consider Tredence for retail and CPG analytics or GenAI programs. Request comparable implementations and evidence for any accelerator savings it cites. | Listed, not reviewed |
| Wipro | Expert | Large-scale global enterprises; offshore delivery model | Listed, not reviewed |
| XenonStack | Expert | Agentic AI systems; real-time analytics; platform engineering | Listed, not reviewed |
Shortlist data migration firms Matched to your platform, source systems, and budget in about 60 seconds.
Compare firms with proven platform-migration experience - cloud, data warehouse, and database moves - and learn how migration projects are scoped, sequenced, priced, and de-risked before you commit.
Based on 86 profiled firms
- 75 firms
- 87% rated Expert/Strong at migration
- 42 firms
- rated "Expert" in platform migration
- 3-18 mo
- typical migration timeline
Rates vary by delivery model; see the rates guide.
Firm counts and percentages are from DataEngineeringCompanies.com's analysis of 75 migration-capable firms profiled in our directory; the timeline is an editorial planning range.
Big Bang vs Trickle vs Hybrid: Which Migration Approach
The single most consequential decision in a migration is the cutover approach - it sets your downtime, risk, and budget. Most enterprise programs end up hybrid: move low-risk reporting marts big-bang to build operational muscle, then phase the production-critical flows with a parallel run.
| Approach | How it works | Downtime | Risk | Best for |
|---|---|---|---|---|
| Big Bang | Move everything in one defined cutover window | High (planned outage) | High | Smaller estates, tolerant of a weekend outage |
| Trickle (phased) | Migrate in increments; both systems run in parallel | Near zero | Low | 24/7 systems that cannot take an outage |
| Hybrid | Big-bang low-risk workloads, phase the critical ones | Low | Medium | Most enterprise data-platform migrations |
-
Phase 1 - Discovery & assessment
Inventory every source, ETL job, BI report, and downstream consumer; capture row counts, refresh cadence, and business owner. Score each workload as rehost, refactor, or rebuild. Typical investment: $25,000-$75,000 over 2-4 weeks. Skipping this is why Gartner estimates most migrations overspend their budgets by 20-50%.
-
Phase 2 - Schema & pipeline re-engineering
A migration is rarely a like-for-like copy. Source schemas are re-modelled for the target platform (for example, Redshift to Snowflake, or Snowflake to Databricks Lakehouse), and ingestion/transformation pipelines are rebuilt in the target's tooling. This is typically the largest cost line.
-
Phase 3 - Parallel run & validation
Run the new platform alongside the old one for at least one full reporting cycle. Reconcile row counts, control totals, and business-critical metrics before anyone trusts the new numbers. This validation period - not the data copy - is what protects against silent data loss and broken downstream reports.
-
Phase 4 - Cutover & decommission
Switch consumers to the new platform, keep a tested rollback path until sign-off, then decommission the legacy system to stop paying for two stacks. Document architecture and runbooks so the engagement can wind down without a knowledge cliff. Lingering dual-running is a common hidden cost - budget for a hard decommission date.
Types of Data Migration
"Data migration" covers four distinct project types, each with different tooling and risk. Match the firm's track record to the move you actually need.
-
Cloud migration
On-prem or data-center workloads to AWS, Azure, or GCP. See AWS, Azure, and GCP partner guides.
-
Data-warehouse migration
Legacy warehouse to a modern platform - for example a Snowflake to Databricks migration, or onto Snowflake.
-
Database migration
Transactional database moves (Oracle, SQL Server, Postgres) with schema conversion and replication-based cutover.
-
Platform re-architecture
Consolidating tools and re-modelling for a lakehouse, often to unlock AI/ML workloads.
Frequently Asked Questions
-
What does a data migration company do?
A data migration company plans and executes the move of data from a source system to a target platform - cloud warehouse, lakehouse, or database. The work spans discovery and inventory, schema and pipeline re-engineering, choosing a migration approach (big bang, trickle, or hybrid), parallel-run validation, cutover, and decommissioning the legacy system. The goal is a controlled transition with no data loss and minimal downtime, not just copying tables.
-
How much does data migration cost?
Rates vary widely by delivery model and seniority; see the rates guide and request written quotes. A scoped data-warehouse migration typically runs $75,000-$300,000; a multi-source cloud migration with pipeline re-engineering runs $150,000-$750,000+. Cost is driven by data volume, number of source systems, transformation complexity, and tolerable downtime during cutover.
-
What is the difference between big bang and trickle migration?
Big bang migrates everything in a single defined window - faster and cheaper, but higher risk and requires downtime. Trickle (phased) migration moves data in increments while both systems run in parallel - lower risk and near-zero downtime, but longer and more expensive. Hybrid moves low-risk reporting workloads big-bang and phases the production-critical flows; most enterprise migrations are hybrid.
-
How long does a data migration take?
A single data-warehouse migration typically takes 3-6 months; a multi-source enterprise cloud migration with pipeline re-engineering takes 6-18 months. The timeline is set by the number of source systems, downstream dependencies, data-quality remediation, and the parallel-run validation period - not by raw data volume alone.
-
How do you reduce risk in a data migration?
Inventory every source, pipeline, and downstream consumer before moving anything; profile and remediate data quality at source; run the new platform in parallel for at least one full reporting cycle and reconcile row counts and business totals; sequence low-risk workloads first; and keep a tested rollback path until cutover sign-off. Skipping parallel-run reconciliation is the most common cause of post-migration trust failures.
Deep-Dive Guides
In-depth research articles supporting this hub.
Find a Data Migration Partner
Use our matching wizard to find firms with proven cloud, warehouse, and database migration experience for your source systems and target platform.
Want the broader picture first? The top data engineering companies in our independent 2026 directory are profiled by rate, capability, and engagement fit.
Compare Migration Firms