How We Prepared This List of Top Data Modernization Companies in USA?
We evaluated the top data modernization service providers using six criteria:
Top Data Modernization Companies in USA: 10 Leading Service Providers in 2026
1. Dedicated Data Modernization Capabilities
We looked for evidence of data migration, data platform modernization, data architecture modernization, pipeline modernization, cloud migration and modernization of legacy data environments.
2. Depth of Data Engineering Expertise
Modernization requires engineering capabilities across data warehouses, data lakes, lakehouses, ETL/ELT, streaming, APIs, data quality, governance and cloud infrastructure.
3. Modern Cloud and AI Readiness
We considered experience with AWS, Microsoft Azure, Google Cloud, Databricks, Snowflake, Microsoft Fabric and AI-ready data architectures.
4. Client Evidence and Case Studies
Published client work provides stronger evidence than broad service claims. We prioritized examples that demonstrate measurable modernization outcomes.
5. U.S. Market Presence
The companies included have an established U.S. presence or significant activity serving U.S. enterprises.
6. Recent 2026 Activity
We also considered recent announcements, case studies, partnerships, reports and company activity through August 2026 to keep the list relevant to today’s data modernization market.
Top 10 Data Modernization Companies in USA
| Company | Data Modernization Services | Key Technologies & Platforms |
|---|---|---|
| Azilen Technologies | Data architecture, cloud data migration, data warehouse modernization, data lake and lakehouse implementation, ETL/ELT modernization, DataOps, real-time data streaming, data quality, governance, and AI-ready data platforms | Azure, Azure Databricks, AWS, cloud data warehouses, data lakes, lakehouse architecture, ETL/ELT, real-time streaming, and AI/ML |
| EPAM Systems | Data and AI modernization, legacy platform migration, cloud data modernization, data platform engineering, data products, AI-native migration, data architecture, governance, and modernization assessment | AWS, Microsoft Azure, Google Cloud, Databricks, Snowflake, cloud-native platforms, AI/ML, and proprietary migration tools |
| Thoughtworks | Data modernization strategy, data platform modernization, data engineering, data architecture, data products, data mesh, governance, analytics modernization, and AI-ready data foundations | Cloud data platforms, data mesh, modern data architectures, data engineering, analytics platforms, AI/ML, and cloud technologies |
| Slalom | Cloud data modernization, data platform implementation, data strategy, analytics modernization, data governance, AI readiness, and legacy data modernization | Databricks, Microsoft Fabric, Azure, AWS, cloud data platforms, analytics, and AI |
| Persistent Systems | Data-stack modernization, data warehouse modernization, cloud migration, data engineering, analytics modernization, legacy analysis, data governance, and AI-ready data platforms | Azure, Azure Databricks, AWS, cloud data platforms, data warehouses, analytics platforms, and AI/ML |
| Perficient | Data platform modernization, data engineering, analytics modernization, cloud migration, data integration, enterprise architecture, AI-ready data, and legacy modernization | Databricks, AWS, Microsoft Azure, cloud data platforms, data analytics, AI/ML, and enterprise technologies |
| DataArt | Data migration, data modernization consulting, legacy database modernization, cloud migration, data architecture, data quality, dashboard modernization, and AI-assisted migration | AWS, Azure, Google Cloud, cloud data platforms, distributed architectures, data engineering, and AI technologies |
| Infogain | Data modernization, SAS-to-PySpark migration, data engineering, analytics modernization, legacy code migration, cloud migration, data governance, and GenAI-assisted modernization | Databricks, Azure, PySpark, SAS, cloud data platforms, GenAI, data engineering, and analytics technologies |
| Lovelytics | Data platform modernization, data migration, data engineering, cloud data architecture, data governance, data quality, analytics modernization, and AI-ready data platforms | Databricks, Azure, AWS, Google Cloud, Unity Catalog, cloud data platforms, analytics, and AI |
| PwC | Enterprise data modernization, analytics modernization, ERP data modernization, cloud migration, data strategy, master data management, data governance, data quality, and AI-ready data foundations | AWS, Microsoft Azure, Google Cloud, cloud data platforms, analytics, AI/ML, ERP platforms, and enterprise data technologies |
Azilen Technologies takes the top position in this list because its data modernization capabilities sit at the intersection of data engineering, cloud migration, data platforms and AI engineering.
Its current data engineering services covers the broader data platform stack, including data architecture, pipeline engineering, cloud warehousing and real-time streaming. Azilen also positions DataOps as an engineering discipline focused on monitored, version-controlled and scalable data workflows.
For organizations moving from legacy infrastructure toward modern analytics and AI, this combination is particularly relevant. Azilen’s cloud data migration offering focuses on modernizing legacy systems and moving data using compatible extraction, transformation and migration approaches.
A recent Azilen case study provides a strong example of this approach. For an InsurTech platform processing more than a million premium transactions each month, Azilen architected an Azure-based multi-tier data warehouse using a Medallion/Lakehouse pattern with Raw, Curated, Transformed and Metadata layers. The solution was designed to create unified visibility across high-volume insurance transactions while supporting compliance and analytics.
Azilen’s more recent work also connects modern data engineering with AI. Its 2026 credit decisioning case study used an Azure Databricks-based engineering environment to transform lending data into reusable analytical assets, establish feature-engineering pipelines and organize AI artifacts for governed model development.
Core Capabilities
→ Data architecture and engineering
→ Cloud data migration
→ Data warehouse modernization
→ Data lake and lakehouse architecture
→ ETL/ELT pipeline modernization
→ Real-time data streaming
→ DataOps
→ Azure Databricks
→ Data quality and governance
→ AI-ready data platforms
→ Data engineering for analytics and machine learning
→ Legacy data infrastructure modernization
Why Consider Azilen?
Azilen is particularly relevant for organizations looking for a data modernization company that can continue into analytics and AI engineering.
Its published work spans data warehouses, cloud migration, data engineering and AI-enabled platforms, giving buyers an opportunity to consolidate multiple technical workstreams with one engineering partner.
EPAM is one of the strongest choices for large, complex modernization programs that involve multiple platforms, legacy environments and enterprise-scale engineering.
Its dedicated Data & AI Modernization offering focuses on migrating legacy platforms toward cloud-native data foundations. EPAM reports more than 75 large-scale modernization programs delivered and positions its proprietary migVisor solution around automated assessment, code conversion and risk identification across environments including AWS, Azure, GCP, Databricks, Snowflake, mainframe and midrange platforms.
EPAM was also named a Leader in the IDC MarketScape Worldwide Data Modernization Services 2024 assessment. Its capabilities span strategy, data and AI consulting, data products, cloud data, data platform engineering and AI/ML engineering.
One representative case study involved IHS Markit, where EPAM helped create a cloud-based data delivery and analytics platform on AWS for customers working with large volumes of energy data.
Core Capabilities
→ Data and AI modernization
→ Legacy platform migration
→ Cloud data modernization
→ Data platform engineering
→ Data products
→ AI-native migration
→ Data architecture
→ Data governance
→ FinOps and optimization
→ AWS, Azure and Google Cloud
→ Databricks and Snowflake modernization
EPAM makes sense for enterprises with large modernization estates, complex technology environments and significant engineering requirements.
Thoughtworks approaches data modernization through a combination of strategy, architecture, engineering and AI readiness.
Its dedicated Data Modernization Services offering focuses on building trusted and scalable data platforms that can support analytics, AI and agentic applications. The company offers data modernization strategy, data platform modernization and engineering capabilities designed around modern data foundations.
Thoughtworks also publishes measurable examples from its modernization work, including a 66% faster time-to-market for a biopharmaceutical solution, a 95% reduction in storage costs and carbon emissions for a public transport dataset, and a 99% faster campaign launch for a major broadcaster.
The company’s current positioning increasingly connects modern data foundations with AI. Its 2026 content emphasizes trusted, contextual data as a prerequisite for useful enterprise AI.
Core Capabilities
→ Data modernization strategy
→ Data architecture
→ Data platform modernization
→ Data engineering
→ Data products
→ Data mesh
→ Data governance
Thoughtworks is a strong choice when modernization involves architecture decisions, data operating models and engineering practices alongside technology migration.
Slalom combines consulting, cloud engineering, data and AI capabilities. Its data modernization work is particularly relevant for organizations already invested in Microsoft, AWS or Databricks ecosystems.
In April 2026, Slalom announced an expanded partnership with Databricks after a decade of collaboration. The company introduced additional capabilities designed to accelerate data modernization and AI adoption at scale, supported by a dedicated global Databricks team.
Slalom also works extensively with Microsoft Fabric. Microsoft describes Slalom’s Fabric work as a driver of data innovation and modernization, including internal modernization of Slalom’s own reporting environment.
Core Capabilities
→ Cloud data modernization
→ Microsoft Fabric
→ Databricks
→ AWS
→ Data strategy
→ Data governance
→ Analytics modernization
→ Data platforms
→ AI readiness
→ Legacy modernization
Slalom fits organizations seeking a consulting and implementation partner around cloud, Microsoft Fabric, Databricks and AI modernization.
Persistent has a dedicated Data Stack Modernization offering focused on helping enterprises modernize their data platforms and build stronger foundations for analytics and data monetization. Its published client work includes data-stack modernization for Ellie Mae.
Persistent’s 2026 activity shows a strong emphasis on rebuilding data foundations around cloud-native platforms. The company has highlighted a Teradata-to-cloud modernization program that it says unlocked $140 million in savings, alongside work using Azure Databricks to improve governance, processing and scalability.
The company has also promoted AI-assisted legacy analysis, reporting that its iAURA technology reduced four to six months of manual effort to under six weeks in one banking modernization engagement and accelerated documentation by 70%.
Core Capabilities
→ Data-stack modernization
→ Data warehouse modernization
→ Cloud migration
→ Teradata modernization
→ Azure Databricks
→ Data engineering
→ Analytics modernization
→ Legacy analysis
→ Data governance
Persistent is a strong option for enterprises looking to rebuild legacy data estates around cloud-native platforms while improving analytics, governance and AI readiness.
Perficient positions data modernization within a broader data and AI practice. Its current data and analytics offering focuses on unifying fragmented information, modernizing the platforms underneath enterprise data and creating foundations for analytics and AI.
The company has a particularly strong presence around enterprise platforms and cloud modernization. Its AWS modernization work combines migration expertise with AI-assisted modernization capabilities, while its 2026 Databricks activity focuses on helping enterprises extract more value from existing data investments.
Core Capabilities
→ Data platform modernization
→ Data engineering
→ Analytics modernization
→ Databricks
→ AWS
→ Cloud migration
→ Data integration
→ Legacy modernization
Perficient is well suited to enterprises that want data modernization connected to broader digital, cloud, application and AI initiatives.
DataArt has a particularly clear focus on data migration and modernization. Its current offering covers migration from complex legacy environments to modern cloud platforms, with the company reporting more than 100 completed data migrations from legacy landscapes to distributed cloud architectures.
The company also incorporates AI into modernization workflows, including migration assistance, dashboard acceleration and automated quality checks. DataArt states that these approaches can reduce delivery time by up to 70% in applicable modernization work.
Core Capabilities
→ Data migration
→ Data modernization consulting
→ Legacy database modernization
→ Cloud migration
→ Data architecture
→ Data quality
→ Dashboard modernization
→ AI-assisted migration
DataArt is a strong fit for organizations where migration complexity, legacy infrastructure and business continuity are major considerations.
Infogain brings a strong combination of data engineering, analytics, cloud and legacy modernization capabilities.
Its current Databricks activity is particularly relevant. Infogain partnered with Databricks through its Partner Accelerators network to support SAS-to-PySpark migration, using GenAI automation to convert SAS procedures, EGP files and macros into scalable PySpark code.
Infogain also highlights a clinical research modernization engagement involving Databricks and Azure, where the company reports $3 million in annual savings alongside improved trial efficiency and faster patient enrollment.
Core Capabilities
→ Data modernization
→ SAS-to-PySpark migration
→ Databricks
→ Azure
→ Analytics modernization
→ Legacy code migration
→ GenAI-assisted migration
→ Data governance
→ Real-time analytics
Infogain is particularly attractive for organizations dealing with legacy analytics environments such as SAS and looking to migrate toward modern cloud and Databricks architectures.
Lovelytics is a specialist data and AI consultancy with a strong focus on modern cloud data platforms and Databricks.
Its modernization work is especially compelling for organizations that want to move from legacy, on-premises environments toward governed cloud data platforms. A 2025 healthcare case study describes a modernization program using Azure and Databricks to improve data integration, reporting, governance and analytics.
Another healthcare engagement used Databricks and Azure to automate EHR-to-OMOP data transformation, improve reporting and establish data governance through Unity Catalog.
Core Capabilities
→ Databricks
→ Azure
→ AWS
→ Google Cloud
→ Data migration
→ Data platform modernization
→ Analytics modernization
→ AI-ready data platforms
Lovelytics is a strong choice for organizations seeking a specialist modern data-platform partner, particularly around Databricks.
PwC brings data modernization into a broader enterprise consulting, cloud, analytics and AI framework.
Its current Data Modernization offering covers data architectures, pipelines and platforms across major cloud environments. Its capabilities include enterprise analytics modernization, ERP and core-system transformation, AI and advanced analytics enablement, and cloud migration and optimization.
PwC also emphasizes governance, data quality and operating models alongside modernization. Its data strategy and governance practice covers MDM, data quality, AI-ready data foundations and modernization support for ERP and cloud migrations.
Core Capabilities
→ Enterprise data modernization
→ Analytics modernization
→ ERP data modernization
→ Cloud migration
→ Data strategy
→ MDM
→ Data governance
PwC is a strong fit for large organizations that need data modernization combined with enterprise strategy, governance, ERP transformation and broader operating-model change.
How to Choose the Right Data Modernization Company?
The best data modernization company depends on your existing architecture, modernization goals, internal engineering capabilities and technology ecosystem.
1. Look at Data Modernization Experience
Start by checking whether the provider has worked with data environments similar to yours. Experience with legacy databases, on-premises warehouses, ETL pipelines, fragmented data sources, or platforms such as Teradata and SAS can be particularly valuable when modernization involves complex dependencies.
Case studies are useful here because they show what the company actually delivered and the outcomes achieved.
2. Check Cloud and Data Platform Expertise
Your provider should have hands-on experience with the platforms you plan to adopt, such as AWS, Microsoft Azure, Google Cloud, Databricks, Snowflake, or Microsoft Fabric.
Look beyond certifications and technology logos. Review whether the company has completed migrations, data warehouse modernization, lakehouse implementations, or pipeline re-engineering on your preferred platform.
3. Evaluate Data Engineering and Governance Capabilities
Modernization requires engineering capabilities across data architecture, ETL/ELT, data pipelines, orchestration, data quality, monitoring, security, and governance.
These capabilities determine how reliable and maintainable the modernized platform will be after migration. Providers that combine modernization with DataOps, governance, lineage, and quality management can help create a stronger foundation for ongoing data operations.
4. Consider Your Analytics and AI Roadmap
A modern data platform increasingly needs to support analytics, machine learning, and AI applications.
When comparing the top data modernization service providers, evaluate whether they can prepare trusted, governed data for these use cases. This could include lakehouse architecture, feature engineering, AI-ready datasets, real-time pipelines, and integration with modern AI platforms.
5. Compare Outcomes, Delivery Model, and Support
Finally, compare providers based on measurable project outcomes, delivery approach, and post-migration support.
Look for evidence such as reduced infrastructure costs, faster data processing, improved reporting, better data quality, or accelerated analytics. Also clarify who will handle architecture, migration, testing, documentation, knowledge transfer, and ongoing support.
Why Azilen Stands Out as a Data Modernization Company?
Azilen Technologies combines data strategy, modern data architecture, cloud migration, data engineering, DataOps, analytics, and AI infrastructure to help organizations modernize their data ecosystems.
Its current data engineering practice covers cloud data warehouses, lakehouse architecture, ETL/ELT pipelines, real-time streaming, data quality, governance, legacy data migration, and AI/ML data infrastructure.
The company has applied these capabilities across different data environments. For an InsurTech platform processing 15,000+ daily insurance transactions, Azilen designed an enterprise data architecture using a Medallion/Lakehouse pattern with Raw, Curated, Transformed, and Metadata layers.
In another engagement, Azilen built a centralized cloud data warehouse for a car-sharing platform, using Azure Data Factory for data movement, Snowflake for centralized storage, Azure Analysis Services for data modeling, and Power BI for analytics. The solution delivered 40% faster decision-making, 50% higher car availability for allocation, and 5x better accuracy in demand forecasting.
For businesses evaluating data modernization service providers, this combination makes Azilen particularly relevant when the modernization roadmap extends from:
Legacy Data → Modern Data Platform → Trusted Data → Analytics → AI
That engineering continuity can help reduce handoffs between data modernization and downstream AI initiatives.
Get Your Data Modernization Roadmap with Azilen
Discover what to modernize first and which approach fits your business.
Frequently Asked Questions
Which data modernization company is best for migrating a legacy data warehouse to the cloud?
For legacy data warehouse modernization, Azilen Technologies, EPAM Systems, Persistent Systems, Perficient, and Thoughtworks are strong options, with the best choice depending on the source platform and target cloud architecture. These providers support capabilities such as legacy assessment, data migration, warehouse modernization, ETL/ELT re-engineering, cloud data platforms, and data validation. Organizations should compare providers based on their experience with the specific legacy warehouse, target platform, migration complexity, and post-migration requirements.
Which data modernization companies can help migrate Teradata, SAS, or other legacy platforms to Databricks?
Azilen Technologies, EPAM, Persistent Systems, Infogain, and Slalom have capabilities relevant to modernizing legacy data environments toward cloud and modern data platforms. Infogain has specific experience with SAS-to-PySpark migration, while Persistent has published work around Teradata-to-cloud modernization. Azilen’s data engineering practice includes Databricks, Spark, cloud data warehouses, ETL/ELT modernization, and data migration. The right provider should be selected based on the legacy technology, migration volume, transformation requirements, and target Databricks architecture.
Which data modernization company is suitable for building an Azure Databricks or lakehouse architecture?
Azilen Technologies, Slalom, EPAM, Persistent Systems, and Lovelytics are suitable options for organizations considering Azure Databricks or lakehouse modernization. Their capabilities include cloud data engineering, lakehouse architecture, data pipelines, governance, analytics, and AI-ready data platforms. Azilen has implemented an Azure-based Medallion/Lakehouse architecture for an InsurTech platform and uses Azure Databricks in data engineering and AI-focused engagements.
Which data modernization firms can modernize data platforms for AI and machine learning?
Companies such as Azilen Technologies, EPAM, Thoughtworks, Slalom, Persistent Systems, and Perficient combine data modernization with AI and machine learning capabilities. Their work can include modern data architecture, governed pipelines, feature engineering, analytics platforms, and AI-ready data foundations. For organizations planning AI adoption, the evaluation should focus on whether the provider can create trusted, accessible, governed data that supports both current analytics and future AI workloads.
Which data modernization service provider should a company choose for real-time data, analytics, and AI requirements?
For organizations requiring real-time data processing combined with analytics and AI, Azilen Technologies, EPAM, Slalom, Thoughtworks, and Persistent Systems offer relevant capabilities. Modernization in this scenario may involve streaming pipelines, cloud data platforms, lakehouse architecture, data engineering, analytics, and AI/ML infrastructure. The provider should be evaluated on its ability to connect real-time ingestion and processing with governed analytical datasets and downstream AI workloads.
10 mins
Sep 12, 2024
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