Top Big Data Development Companies in 2026

Big data isn’t just a trendy term. Every business in the world is moving towards digitalization, which means covering all business processes with numbering or moving them to the cloud, which inevitably leads to the generation of massive amounts of data.

That’s where big data and SumatoSoft step in. 

Companies like ours help businesses operate these massive volumes of data and extract really valuable insights that will drive business decisions. And according to the IDC worldwide big data & analytics spending guide, worldwide spending on big data and analytics reached $330B–$350B in 2025.

In this article, we examine the top big data development companies in the world, assessing them according to predefined criteria that we will mention before the top. 

What is a Big Data Company? 

When people say “big data company”, they usually mix two very different types of businesses:

• Big data development companies – service providers that design and build big data solutions for clients.
• Data-native product companies – businesses whose main asset is the huge volume of data they collect and use themselves.

In this ranking, we focus on the first group.

Our focus is on bug data development companies. 

The second group consists of companies that generate and own massive datasets as part of their core products. They may have world-class internal data platforms, but they don’t primarily sell development services to clients.

Typical examples:

• Google – search, ads, maps, YouTube usage data.
• Meta (Facebook, Instagram, WhatsApp) – social graphs, user interactions, advertising data.
• Amazon – e-commerce, logistics, and behavior data from retail and AWS logs.
• Netflix – streaming behavior, recommendations, and content performance.
• TikTok / ByteDance – short-video engagement and content graphs.
• LinkedIn – professional graph, recruiting and job-market data.
• X (Twitter) – public conversation and real-time event data.
• Uber – rides, geo-location, supply/demand dynamics.
• Airbnb – rental demand, pricing, and travel behavior.
• Spotify – listening behavior and recommendation data.

These are often called “big data companies” because they run on data at a huge scale.
But they are not what we mean by big data development companies in this article.

Why the distinction matters

If you’re a business looking for a partner to build your data platform, you need a big data development company.

If you’re looking for tools, advertising reach, or data-rich platforms, you deal with the data-native product companies listed above.

What Big Data Development Company Must Be Able to Do (Objectively)

The target companies of this article are companies specializing in big data development. The one that designs, builds, and supports big-data-powered platforms for their Clients. 

Below are the core competencies such a company must have.

1. Translate Business Problems Into Data Solutions

Big data is a tool that needs to serve some purpose. The big data development company must understand how to apply this technology to the Client’s industry and how to reach the Client’s goals with it. Another significant aspect here is measurement: the company might be capable of converting these goals into precise requirements, such as KPIs, SLAs, quality rules, and data governance processes. 

This is especially valuable when it comes to complex systems that use the power of both the Internet of Things and big data visualization.

The output must be a concrete backlog and specifications.

2. Design Modern Data Architectures

The company chooses and justifies the right architecture: lake, warehouse, or lakehouse. It designs layers (raw data, cleaned info, modeled insights) and plans storage/compute, cloud choices, and integration patterns. They are also experts in big data visualization. 

The result is a scalable, flexible, and cost-aware blueprint, not a random collection of tools.

3. Build Reliable Data Pipelines

The data is only valuable when it’s processed correctly. The big data team should be able to build automated pipelines that ingest, transform, and deliver data between databases, APIs, files, and streams. Pipelines must run daily without manual fixes; otherwise, the platform is not production-ready.

4. Enable Analytics and Machine Learning

The company models data for BI (marts, star schemas, semantic layers) and prepares high-quality datasets for ML. It integrates BI tools and basic MLOps processes (versioning, deployment paths, monitoring) as needed. 

5. Ensure Security, Privacy, and Governance

Big data entails high security requirements, particularly when handling sensitive information. Therefore, the big data development company must be able to implement multiple security measures, including role-based access, strict data governance processes with clear ownership, lineage, and approvals, as well as support for compliance requirements such as GDPR and HIPAA, among others. 

6. Work as a Long-Term partner

Big data development projects are a long-term journey with multiple phases and a long roadmap. The big data dev company must plan a roadmap with phases: quick wins first, then larger transformations. It trains the Client’s team and hands over documentation so the platform is not a “black box”. Communication is transparent regarding costs, risks, and trade-offs, fostering a partnership rather than a one-time project.

We reviewed the big data development companies and the competencies they must possess to deliver a successful solution utilizing big data technology. The following section is the core part of this article: the list of top companies and the criteria we used to assess them. 

We also have another article on the top big data visualization companies if you are looking for these services. 

Matrices We Considered

We would like to briefly touch on the list of metrics we used to assess the providers.

• Foundation year – how long the company has been on the market.
• Geographic coverage – number of offices, delivery centers, and active project regions.
• Hourly rate – average engineer cost per hour.
• Technology stack – tools and frameworks the team uses for big data delivery.
• Services provided – specific big data services offered.
• Industries served – verticals where the company has proven experience.
• Awards/industry ratings – independent recognitions from third-party platforms. 
• Standardization of processes – presence of formal data management methods, data governance, and certifications (ISO, GDPR compliance).
• Top relevant case – the single strongest project example that demonstrates a real significant data impact for a Client similar to the reader.

#1 SumatoSoft

Foundation year: 2012

Geographical coverage: Headquarters in Boston, USA; development center in Warsaw, Poland, serving 25+ countries globally.

Hourly rate: $50-$99 / hour 

Technology stack: 

• Databases: PostgreSQL, MySQL, MongoDB.
• Data warehousing & OLAP: Amazon Redshift, Snowflake, ClickHouse, Cloudera, DataStax.
• Streaming: Apache Kafka, Apache Kudu, AWS Kinesis, Google Pub/Sub, Apache NiFi, MQTT / WebSockets.
• Monitoring: InfluxDB, Chronograf, Graphite, Prometheus, Grafana.
• Analytics & BI: Google Analytics, Power BI, Tableau, Looker, Superset, Metabase, Grafana.

Service provided:

• Data engineering
• Big Data analytics & BI
• Data integration
• Data visualization
• Predictive analytics & ML
• Big Data consulting

Industry served:

• Finance & FinTech
• Retail & E-Commerce
• Logistics & Transportation
• IoT
• Healthcare
• AdTech
• EdTech

Awards/industry ratings:

• Clutch: rated 4.8/5 across 24 reviews.
• TechReviewer: rating 5.0

Standardization of processes: 

• Certifications: ISO 9001:2015 (quality management), ISO/IEC 27001:2022 (info security) achieved March 2024.
• Data encryption, role-based access controls, incident response plans, alignment with GDPR, Privacy by Design. 

Top case: Tartle – Big Data Trading Platform (web + iOS) for an anonymous data-buy/sell marketplace. The case link. 

Verdict

The company offers competitive hourly rates for many markets, and the geographical coverage is decent, but the operations are centralized in two cities: Warsaw and Boston. The tech stack is broad, while the presence of best-practice streaming and data warehousing technologies is a big plus. The industry verticals listed match typical Big Data use cases (FinTech, retail, adtech, logistics), which align with typical enterprise needs.

#2 Accenture

Foundation year: 1989

Geographical coverage: Headquartered in Dublin, Ireland, Accenture operates in 52 countries.

Hourly rate: Undisclosed, but it can be indirectly established as $150 – $350/hour.

Technology stack:

• Cloud platforms: AWS, Azure, Google Cloud
• Data platforms: Snowflake, BigQuery, Redshift
• Distributed processing: Apache Spark, Hadoop
• Streaming: Kafka, cloud-native streaming tools
• Analytics & AI: Power BI, Tableau, AI/ML toolsets
• Governance & automation: data-governance frameworks, DataOps/CI/CD systems

Services provided:

• Data and analytics strategy
• Cloud data migration
• Modern data platform engineering
• Data architecture and governance
• Data engineering
• Advanced analytics, AI & ML
• Managed data services

Industries served:

• Technology & Media
• Financial Services
• Healthcare & Life Sciences
• Public Sector
• Consumer & Retail

Awards / industry ratings:

• Fortune’s “World’s Most Admired Companies” and TIME’s “World’s Best Companies”.
• Recognized in Kantar BrandZ’s Most Valuable Global Brands.
• Appears as a Leader in Gartner’s Data and Analytics Service Providers evaluations and similar analyst reports.

Standardization of processes:

Accenture utilizes ISO 27001 and CMMI Level 5 frameworks, applies formal data governance models, adheres to GDPR-compliant practices, and employs structured DataOps/DevOps pipelines for standardized delivery.

Top relevant case:

For UK bank NatWest, Accenture (together with AWS) led a large customer data transformation program, consolidating data from ~20 million customers into a unified platform.

Verdict:

The global big data and analytics partner Accenture operates as a top-tier company that serves large enterprises with complex multi-country programs. The company delivers massive project capacity through its broad technology expertise in cloud and AI solutions. As a downside, the company charges high prices for its services. 

#3 Delloite

Foundation year: 1845

Geographical coverage: A global network of member firms operating in 150+ countries and 700+ locations.

Hourly rate: Undisclosed, but roughly $150–$300/hour.

Technology stack:

• Cloud platforms: AWS, Microsoft Azure, Google Cloud Platform, Oracle Cloud.
• Data platforms: Snowflake, Databricks, cloud data lakes and lakehouse-style architectures.
• Streaming processing: Apache Spark, Apache Beam, Spark on GKE, Dataflow.
• Data management & governance: Dataplex, Data Catalog, metadata, cataloging, and governance tooling.
• Analytics & AI / MLOps: Analytics platforms and AI/ML solutions built on top of these data foundations, with MLOps and modern data-platform patterns.

Services provided:

• Data & analytics strategy
• Data engineering & modernization
• Data management & governance
• Analytics, AI & ML
• Industry-specific analytics solutions
• Data platform operations & support

Industries served:

Financial services (including exchanges and banking), retail and e-commerce, marketing and media, telecom, manufacturing and industrial, and public sector, with generally broad cross-industry coverage.

Awards/industry ratings:

Ranked as the #1 consulting services provider worldwide by revenue in Gartner market share reports and described as part of the world’s largest professional-services network by revenue and headcount.

Standardization of processes:

Emphasizes formal data governance operating models and dedicated data management & governance capabilities for modern architectures. Operating at scale in heavily regulated industries, with global enterprise Clients, implies structured compliance frameworks (GDPR, ISO/SOC-type controls) and mature, standardized processes around data quality, governance, and risk.

Top relevant case:

For a leading financial exchange, Deloitte led the “From Blue Sky to the Cloud” data-modernization program. The firm helped establish a Chief Data Office, designed a unified data architecture, and migrated complex legacy applications and data lakes to Google Cloud.

Verdict:

Deloitte is a very strong choice for large, enterprise-grade big data and analytics programs that require both deep industry context and robust governance. Its strengths are a long market history, huge global footprint, rich cloud/data stack, and end-to-end coverage from strategy to AI. The trade-offs are premium pricing and the “big-firm” engagement style, which may feel heavy for smaller or narrowly scoped projects.

#4 IBM Consulting

Foundation year: 2021 – current IBM Consulting brand; IBM itself has a century-long history

Geographical coverage:

Global provider with presence in around 170+ countries via the IBM parent group and major consulting delivery centers across the Americas, EMEA, and APAC.

Hourly rate: Undisclosed, but data-related roles are estimated at roughly $200–$400/hour

Technology stack:

• Cloud & data platforms: AWS, Microsoft, Salesforce, Snowflake; modern and hybrid data platforms, data lakes and lakehouses.
• Distributed & streaming processing: Big-data and streaming/real-time processing frameworks (e.g., Spark-based pipelines and similar technologies).
• Data fabric & governance: Data transformation, modern data platforms, data fabric concepts, and governed data foundations.
• AI / ML: AI/ML frameworks including IBM watsonx components for data governance and AI-driven analytics, plus broader analytics/AI stacks on top of cloud data platforms.

Services provided:

• Data strategy
• Data architecture & modernization
• Analytics & AI
• Data & AI governance
• End-to-end Big Data services

Industries served:

Banking and financial services, manufacturing, government and public sector, retail and e-commerce, telecom/ICT, technology, and healthcare.

Awards / industry ratings:

Positioned as a leader in AI-led services by independent research (e.g., HFS Research) and benefiting from IBM’s long-standing recognition by major analyst firms such as Gartner and IDC.

Standardization of processes:

Emphasis on responsible AI and formal governance frameworks, combined with work in highly regulated industries, strong internal governance and compliance practices (GDPR, ISO-style controls) and mature, standardized processes for data management, security, and quality.

Top relevant case:

For Virgin Money, IBM Consulting developed an AI-powered banking assistant that uses data and analytics to improve customer engagement.

Verdict:

IBM Consulting is a high-end, global partner for Big Data development and modernization – well suited to large enterprises with complex, multi-country data estates and strong regulatory constraints. Its strengths are global footprint, broad technology alignment (cloud, data, AI), deep industry experience, and governance/scale capabilities.

#5 TCS

Foundation year: 1968

Geographical coverage: Headquartered in Mumbai, India, operates in 150 locations across 46 countries with 500+ offices and a workforce of about 590k–600k+ employees

Hourly rate:

Indicative ranges: ~$25–$49/hour for offshore/standard delivery and up to $50–$99/hour for specialised or onshore roles, depending on region, seniority, and engagement type.

Technology stack:

• Data platforms & analytics: Databricks, Snowflake, AWS, Azure, GCP
• Data & analytics foundations: TCS Datom™ framework, platform-agnostic analytics foundations
• Integration, governance & MDM: Informatica integration, Informatica MDM, governance and democratization patterns
• AI / ML & GenAI: TCS AI WisdomNext™, ignio™, partner AI/GenAI solutions

Services provided:

• Data-estate assessment and strategy
• Data-platform modernisation
• Data engineering & integration
• Analytics & AI
• GenAI & agentic AI
• Data governance & managed analytics

Industries served:

Banking & capital markets, insurance and broader BFSI, retail & consumer packaged goods, communications/media/information services, manufacturing and high tech, energy & utilities, healthcare, life sciences, public services, travel & logistics.

Awards / industry ratings:

Recognised as a Leader in Healthcare Data, Analytics and AI Services and as a Leader in Retail & Consumer Packaged Goods services by independent analyst evaluations, and consistently listed among the top global IT and consulting brands.

Standardization of processes:

TCS applies its Datom™ framework, leverages long-standing MDM and integration partnerships, operates in regulated sectors, and adheres to GDPR and ISO standards. 

Top relevant case:

TCS developed a a Unified Device Data Platform for Microsoft. They consolidated multiple devices under one network and directed all the marketing data into an analytics pipeline.  So multiple data sources under one system and extensive business intelligence under the hood. 

Verdict:

TCS operates as a major big data and analytics partner, serving enterprise clients through its worldwide presence, established cloud and data partnerships, and proprietary Datom methodology for data estate transformation. The company excels at helping clients transform their complex multi-system environments into AI-ready platforms for the BFSI retail and manufacturing sectors.

#6 Infosys

Foundation year: 1981

Geographical coverage: Headquartered in Bangalore, India, a global presence in 50+ countries, and delivering capabilities across remote regions: India, Europe, with a network of delivery centers across India, Europe, the Americas, and the Middle East

Hourly rate:~$25–$100/hour

Technology stack:

• Cloud platforms: AWS, Azure, Google Cloud
• Big data technologies: Hadoop, Spark
• Cloud data lakes: AWS S3, Glue, Lambda, event-driven pipelines, Athena
• Enterprise data platforms: Infosys Data & Analytics Platform
• ML & analytics: prediction, risk scoring, root-cause analysis, decision support.

Services provided:

• Data platform modernization
• Advanced analytics & ML
• Data sharing & compliance solutions

Industries served:

The whole finance sector, including banking, healthcare, the public sector, and industrial and manufacturing sectors. 

Awards / industry ratings:

Recognised as one of the leading global IT services providers, and winning large, data-driven transformation deals. The organisation and its public services arm are appraised at CMMI Maturity Level 5, the highest level.

Standardization of processes:

Multiple reputable certifications: ISO 9001, ISO 27001, ISO 20000, ISO 22301, ISO 27701 and others) and CMMI Level 5 maturity.

Top relevant case:

For a leading American bank, Infosys built a modernized big data platform on AWS. The solution created a metadata-driven, AWS-native data lake ingesting data from ~86 source systems and 4,000 feeds (files, mainframe, databases), migrated around 200 data users and 13 live consumer applications.

Verdict:

Those companies that want to modernize their legacy system will mostly benefit from this provider. The wide range of cloud-related expertise (AWS, Azure, GCP) and high process standardization with multiple certifications and level 5 CMMI makes Infosys a reliable big data development company. 

#7 Capgemini

Foundation year: 1967

Geographical coverage: Headquartered in Paris, France, and operates in 40+ countries with around 350,000 employees.

Hourly rate: $50–$100/hour

Technology stack:

• Cloud data platforms: Snowflake, AWS, Azure, GCP. 
• Data lakes: AWS S3, Azure Data Lake Storage
• DevOps / DataOps: Docker, Kubernetes, CI/CD for data  
• ETL/ELT tooling: Talend, Informatica, custom Apache Spark pipelines
• Cloud platforms: AWS, Azure, Google Cloud

Services provided:

• Full-stack data engineering
• Real-time streaming analytics
• AI/ML integration
• Governance and cost optimisation
• Industry-specific accelerators

Industries served:

Automotive, consumer products, retail, finance, telecom, and public sector. 

Awards / industry ratings:

Recognised as a Leader by Gartner for data and analytics services, winner of SAP Innovation Awards, and a Forrester Wave Leader in analytics/AI consulting.

Standardization of processes:

Capgemini relies on formalised delivery frameworks backed by ISO 9001 and ISO 27001 certifications and CMMI Level 5, combined with GDPR-focused practices. 

Top relevant case:

For a global automaker, Capgemini built a data lake and IoT analytics platform to process telemetry from millions of connected cars. 

Verdict:

The company Capgemini serves as the leading data engineering partner for organizations that build cloud-native data ecosystems with AI/ML integration. The company provides multiple technology solutions and operates throughout Europe while maintaining adaptable offshore facilities, and shows expertise in data management for the automotive and financial services industries.

#8 Cognizant

Foundation year: 1994

Geographical coverage: Headquartered in Teaneck, New Jersey, Cognizant operates in 40+ countries.

Hourly rate: $80–$300/hour

Technology stack:

• Data engineering & processing: Apache Spark, Kafka, batch and streaming pipelines
• Cloud big data platforms: Azure Data Lake, Azure Synapse, AWS Lake Formation, Amazon Redshift, Google Cloud big-data services
• Governance & operations: Cognizant Data & Intelligence Toolkit, CI/CD workflows, Terraform, CloudFormation
• Analytics & ML enablement: feature-engineering pipelines, DataOps/MLOps

Services provided:

• Full-stack data engineering: pipelines, lakes, warehouses
• Real-time streaming analytics
• AI/ML integration (feature pipelines, production ML, MLOps)
• Data science-driven architectures
• Governance & optimization – cost control, storage optimization, data quality
• Industry-specific accelerators for faster implementation

Industries served:

Healthcare & life sciences, banking & insurance, retail & e-commerce, manufacturing, and technology/digital platforms.

Awards / industry ratings:

Recognized as a Leader in data & analytics services by Gartner and Everest Group, listed in the HFS Top 10 for AI services, repeatedly included among the “Most Admired Companies,” and holding key cloud designations such as AWS Data & Analytics Competency and Azure Expert MSP.

Standardization of processes:

Holds CMMI Level 5 maturity and ISO 27001 certifications, with HIPAA-compliant processes for healthcare and strong GDPR alignment via structured audit logging, access control, and privacy governance. 

Top relevant case:

For a major healthcare provider, the big data development company built a cloud-based platform for electronic health records and billing. Powered by IoT technology, the platform has embedded real-time predictive models for patient health assessment.  

Verdict:

Cognizan offers deep industry specialization, mature DataOps/DevOps practices, and solid expertise. Though their services might be premium-priced, their offering might be extremely valuable for industries they are specialised in.

#9 Wipro

Foundation year: 1945

Geographical coverage: Headquartered in Bangalore, India, with operations in 66 countries. 

Hourly rate: ~$30–$80/hour

Technology stack:

• Big data & streaming / IoT: Spark, Flink, Kafka, streaming and IoT platforms.
• Orchestration & DataOps: Airflow.
• Cloud & infrastructure: AWS, Azure, GCP, hybrid Hadoop–cloud, containers, serverless
• Platforms & accelerators: Wipro Data Discovery Platform (DDP), IoT and analytics accelerators.

Services provided:

• End-to-end data engineering
• IoT & streaming platforms
• Rapid data-lake setup via DDP
• Cloud big data infrastructure
• Data governance & privacy
• AI & MLOps
• DataOps

Industries served:

Banking & capital markets; energy & utilities; consumer goods; manufacturing / Industry 4.0; government and public sector.

Awards / industry ratings:

Recognized as a Leader in Gartner’s data & analytics evaluations, an ISG leader in IoT analytics, and an Informatica Partner of the Year.

Standardization of processes:

Assessed at CMMI Level 5 and certified to ISO 27001 / ISO 9001

Top relevant case:

Wipro helped Marelli build a cloud-based cabin digital twin that accelerates vehicle software development by enabling virtual simulation, validation, and over-the-air updates, cutting prototyping costs and development time while supporting the shift to software-defined vehicles.

Verdict:

Wipro is a strong candidate when the priority is IoT- and streaming-heavy big data platforms backed by mature, cost-efficient global delivery. Its strengths are asset-based accelerators (DDP), deep experience with utilities, manufacturing and banking, and a robust governance/security posture (CMMI5, ISO, privacy-by-design). It is less focused on high-touch strategy than Big Four consultancies, but for organisations wanting large-scale, real-time, IoT-driven data solutions at competitive blended rates, Wipro is a very compelling partner.

Afterwards

This article showcases the variety of big data development companies. However, we observe an apparent lateral displacement toward the enterprise segment when companies offer high specialist rates, backed by strong expertise and well-established processes. 

However, we believe that our criteria help to find a proper vendor for any particular business case, be it IoT platforms, multi-cloud solutions, legacy system modernization, or any other. If you still have doubts about what company to choose, we encourage you to contact us – we will examine your case and find the most suitable big data development company for your needs.