Big Data Development Company for Scalable Data Platforms
Updated
Big Data Engineering Services We Offer
Big Data Infrastructure Setup
Data Integration and ETL
Data Processing and Analytics
Real-Time Streaming Analytics
Machine Learning and AI Integration
Data Governance and Security
Scalability and Performance Optimization
Cloud-Based Big Data Solutions
Big Data Visualization
Big Data Consulting and Strategy
Ongoing Support and Maintenance
Data Science Collaboration
Why Should You Hire Big Data Engineers From Us
Big Data Expertise
Our big data engineers bring hands-on experience across complex, large-scale data ecosystems. From designing distributed systems to managing high-volume data pipelines, they understand how to turn raw data into reliable, business-ready intelligence.
End-to-End Data Engineering Capabilities
We cover the complete big data lifecycle—architecture design, data ingestion, ETL pipelines, processing, analytics, visualization, and optimization. This keeps your data systems cohesive, scalable, and maintainable.
Scalable and High-Performance Solutions
Our engineers build data platforms that scale effortlessly with growing data volumes and user demand. Using distributed frameworks and cloud-native architectures, we ensure high performance, low latency, and consistent reliability.
Strong Cloud and Platform Expertise
We work extensively with cloud platforms such as AWS, Azure, and Google Cloud to deliver cost-effective, elastic big data solutions. Our engineers design systems that leverage cloud services for storage, processing, security, and analytics.
Focus on Data Security and Governance
Data protection is built into every layer. Our engineers implement strong data governance frameworks, access controls, encryption, and compliance measures to safeguard sensitive information and maintain data integrity.
Advanced Analytics and AI Readiness
We design big data platforms that support advanced analytics, machine learning, and AI initiatives. This enables predictive insights, anomaly detection, and data-driven decision-making without re-engineering your core systems later.
Real-Time and Streaming Expertise
From batch processing to real-time streaming analytics, our engineers handle time-sensitive data pipelines that support instant insights and rapid business actions.
Business-Aligned Engineering Approach
We don’t just build data systems—we align them with your business objectives. Our engineers collaborate closely with stakeholders to ensure data solutions directly support growth, efficiency, and smarter decision-making.
Transparent Communication and Collaboration
Clear communication, regular updates, and collaborative workflows are central to how we work. You stay informed at every stage, with full visibility into progress, performance, and outcomes.
Long-Term Partnership and Support
Beyond implementation, we provide ongoing support, optimization, and enhancements. As your data needs evolve, our engineers adapt and improve your data ecosystem to keep you ahead.
Benefits of Big Data Engineering
Technical Scope
Our big data engineers work with large-scale and complex datasets and with the data platforms, frameworks, and tools that support efficient processing, accurate analysis, and reliable insights from your data.
Data Architecture and Design
We design scalable and robust data architectures tailored to your business needs. From defining data pipelines to structuring storage and processing layers, our approach ensures data integrity, security, and long-term flexibility.
Advanced Analytics and Machine Learning
Big data engineering enables advanced analytics and machine learning capabilities. Our engineers help build data platforms that support predictive models, trend analysis, and intelligent decision-making, giving your business a competitive edge.
Performance Optimization
Our engineers focus on optimizing data pipelines and workflows to improve processing speed and reduce latency. By fine-tuning data ingestion, transformation, and querying, we ensure faster insights and improved system performance.
Real-Time Data Processing
We enable real-time and near-real-time data processing for time-sensitive use cases. This allows businesses to analyze streaming data, respond to events instantly, and make decisions based on the most current information.
Scalability and Elasticity
Big data solutions are designed to grow with your business. Our engineering approach ensures your data infrastructure can scale seamlessly, handle increasing data volumes, and adapt to fluctuating workloads without disruption.
Data Governance and Security
We implement strong data governance frameworks, access controls, and security measures to protect sensitive information. This ensures compliance with industry standards while maintaining data quality, consistency, and trust.
Collaboration and Integration
Our big data engineers work closely with data scientists, analysts, and business teams. They integrate data from multiple sources and ensure compatibility across systems, enabling unified analysis and smoother workflows.
Continuous Learning and Innovation
Big data technologies evolve rapidly. Our engineers stay up to date with the latest tools, platforms, and best practices, continuously improving solutions and bringing innovation into your data ecosystem.
Client-Centric Approach
We prioritize your business goals and challenges. By understanding your requirements, we design and deliver big data solutions that align with your objectives, ensuring measurable value and long-term success.
Solving IT Challenges In Every Industry, Every Day.
Big data consists of extensive datasets, primarily in the characteristics of volume, variety, velocity and/or variability, that require a scalable architecture for efficient storage, manipulation and analysis, in NIST’s definition. This section draws that architecture as this page describes it, groups the twelve big data engineering services above, and sets out how the work runs, what lands at each stage, batch against streaming data, four ways to hire big data engineers, what moves the cost, and what to ask any big data firm, including us.
From big data infrastructure setup to data science collaboration, each one described above.
Depending on the seniority and mix of the team, as published on our about page.
Architecture design to optimization, as this page describes the big data lifecycle.
The repository and the cloud accounts are in your name, with no licence back to us.
On this page
- What big data is, in NIST’s terms
- How big data processing runs, stage by stage
- Big data engineering services, grouped by what they change
- How a big data engagement runs, and what lands at each stage
- Batch processing or streaming data: which suits the use case
- Four ways to hire big data engineers, compared
- What moves the cost of big data engineering
- How to compare big data engineering firms
- Big data engineering questions, answered
- More about big data engineering
- Talk to our engineers
What big data is, in NIST’s terms
NIST’s Big Data Interoperability Framework, Volume 1, Definitions (SP 1500-1r2) defines big data engineering as the discipline for engineering scalable systems for data-intensive processing. Its definition of big data turns on four characteristics, each defined in the same publication.
The same definition appears in the NIST glossary, sourced to that publication.
How big data processing runs, stage by stage
This page describes the complete big data lifecycle as architecture design, data ingestion, ETL pipelines, processing, analytics, visualization and optimization. Figure 1 draws that lifecycle as a big data architecture and places each service above at the stage its own description names.
Figure 1. A big data architecture, stage by stage, with this page’s services at each stage
The seven stages are this page’s own description of the big data lifecycle. Each service line sits at the stage its own description names; governance and security apply at every stage, as the page’s data security card says protection is built into every layer, and the three cloud platforms are the ones its cloud-based service names. Nothing in the figure is a Sthenos measurement.
Big data engineering services, grouped by what they change
The twelve services above fall into four kinds of work. Every line below uses the service’s own wording from this page.
- Big data infrastructure for high-volume storage, distributed processing and efficient data access.
- Cloud-native big data solutions on AWS, Azure and Google Cloud.
- Scalability and performance optimization as data volumes grow.
- Data integration and ETL pipelines from multiple sources into target systems.
- Data processing and analytics on large datasets.
- Real-time data ingestion and streaming analytics.
- Data governance frameworks for data quality, privacy and regulatory compliance.
- Access controls, encryption, anonymization and secure data management practices.
- Machine learning and AI integration for predictive analytics and anomaly detection.
- Big data visualization in interactive dashboards.
- Big data consulting and strategy, and data science collaboration.
- Ongoing monitoring, optimization and support.
How a big data engagement runs, and what lands at each stage
The lifecycle above is what gets built. The six stages below are how a Sthenos build runs, with what leaves each stage, as published on our custom software development page. The cadence is the one on our about page: two week sprints, and a first production release typically in three to six months.
- DiscoveryThe problem, the constraints and the success criteria are mapped before a line of code.Written scope
- Architecture and designDecides the data model, where state lives, how the system fails, how it scales, and how a reviewer will later verify its security posture.Architecture decisions in writing
- Build, in two week sprintsWork is cut into increments that end in something reviewable rather than something described.Working software every two weeks
- ReleaseHardening, testing and a controlled release, with the rollback path decided before the deployment rather than during the incident.A controlled release
- HandoverThe operational knowledge written down in a form somebody who was not on the project can act on.Repository and cloud accounts in your name
- SupportMonitoring, maintenance and a roadmap tied to business outcomes.Monitoring, maintenance and a roadmap
Batch processing or streaming data: which suits the use case
This page covers both, from batch processing to real-time streaming analytics. NIST’s framework explains the difference: use cases can be characterized by the time limits for end-to-end analytics processing, at real time, near real time, or batch, and the desired place on that continuum is a large factor in the choice of architectures and component tools.
Batch
The slower end of NIST’s continuum- NIST places batch at one end of the time limits for end-to-end analytics processing, with real time and near real time at the other.
- NIST notes that inexpensive servers are appropriate for slower, batch-speed big data applications, but do not provide good performance for applications requiring low-latency processing.
- Named on this page beside streaming: from batch processing to real-time streaming analytics.
Streaming
Data in motion- Velocity is a measure of the rate of data flow, and high-velocity systems have traditionally been described as streaming data.
- Data in motion is processed and analyzed in real time or near real time, and must be handled in a very different way than data at rest.
- On this page: real-time streaming analytics, to analyze and respond to data as it is generated.
This page names no specific processing engine or data streaming platform, so the choice is set out here by category rather than by product. Two open-source projects do appear by name in the ETL engine case study on this site, delivered by NeoSOFT: Apache Hadoop, which describes itself as a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models, and Apache Airflow, a platform created by the community to programmatically author, schedule and monitor workflows.
Four ways to hire big data engineers, compared
Sthenos sells big data engineering in the four commercial shapes published on our software development services page. The table restates that page’s comparison.
| Model | How it is priced | Where it fits | Where it goes wrong |
|---|---|---|---|
| Fixed price | One number for a defined scope | The specification is genuinely settled and unlikely to move | Every change becomes a negotiation, and the buffer is priced in whether you use it or not |
| Time and materials | Rate multiplied by hours actually worked | Scope will evolve | No ceiling unless you set one deliberately |
| Dedicated team | Monthly, per named engineer | Continuous product work over quarters, not a single project | You are paying for capacity even in a slow month |
| Staff augmentation | Per person, into your existing team | You have the plan and the management, and need hands | You own the outcome, so weak internal direction shows up fast |
Whichever shape you choose, settle the commercial terms in writing before the statement of work is signed: a ceiling on spend, the notice period, who owns the repository, what happens to the cloud accounts, and what counts as done.
What moves the cost of big data engineering
Two published figures sit under every engagement model, both on our about page. The drivers under them carry no number, because a number without your scope attached to it is decoration.
- How settled the scope is. A settled scope can be quoted as a fixed price. An unsettled one priced as though it were settled is where the buffer, and the argument, comes from.
- The seniority and mix of the team. This is the stated reason the rate band has a range at all.
- How many systems it has to talk to. Every integration adds a counterparty whose availability, data quality and release calendar you do not control.
- The control framework the contract names. Work scoped against a named framework carries evidence, review and documentation the same build would not otherwise need.
- Data migration. Reconciling the data, proving it moved correctly, and deciding what to do with the records that do not fit the new model.
- What happens after launch. Get deployment and maintenance priced up front, rather than sold back afterwards.
How to compare big data engineering firms
These questions apply to any big data consultants or data visualization company on your shortlist, Sthenos included. The first two rest on this page’s own services and NIST’s framework; the rest are published on our software development and custom software development pages for any vendor.
- Ask which parts of the pipeline are batch and which are streaming, and why. NIST treats that choice as a large factor in the architecture and the tools.
- Ask how access controls, encryption and anonymization are applied. They are the controls this page’s governance service names.
- Ask what they have integrated with, by name and version. Experience with a platform and a completed integration against it are different claims.
- Ask what happens when the estimate is wrong. The answer tells you how the relationship will actually work.
- Ask who reviews the code, and whether that person can veto a merge.
- Ask what documentation is a contractual deliverable, not what they promise to write.
- Ask which certifications are audited and which are self declared, then ask for the report date. Our about page lists what Sthenos does not hold, including a SOC 2 report and an ISO 27001 certificate.
Big data engineering questions, answered
How do we hire data engineers through Sthenos?
Through one of the four shapes above. On our software development services page, staff augmentation adds engineers per person into your existing team, for when you have the plan and the management and need hands, and a dedicated team is priced monthly, per named engineer, for continuous product work over quarters. Our team augmentation page covers the first in detail.
What is big data, in NIST’s definition?
NIST SP 1500-1r2 defines big data as extensive datasets, primarily in the characteristics of volume, variety, velocity and/or variability, that require a scalable architecture for efficient storage, manipulation and analysis. It defines big data engineering as the discipline for engineering scalable systems for data-intensive processing.
What is the difference between batch and streaming data?
NIST describes a continuum of time limits for end-to-end analytics processing, from real time and near real time to batch. Streaming data is data in motion, processed and analyzed in real time or near real time, and it must be handled in a very different way than data at rest, which is persisted data. This page covers both, from batch processing to real-time streaming analytics.
Which cloud platforms does this page cover?
AWS, Azure and Google Cloud, the three named in the cloud-based big data solutions service above. Each has its own page on this site: AWS, Azure and Google Cloud.
How much does big data engineering cost?
Sthenos rates are $150 to $250 per hour, depending on the seniority and mix of the team, and a typical project runs $50,000 to $200,000. Where the scope is clear we quote a fixed price for a defined outcome, with a costed roadmap before any build starts.
Who owns the code you write?
You do, entirely, from the first commit. The repository and the cloud accounts are in your name, with no licence back to us and no dependency on us continuing to exist.
How long does a big data project take?
Sthenos works in two week sprints, so working software arrives every two weeks and a first production release typically lands in three to six months. A date more precise than that comes out of discovery, because it depends on scope rather than on goodwill.
More about big data engineering
Services. Big data and analytics, data analytics services, data science, AI and machine learning, cloud computing, Python development, team augmentation.
Standards and documentation, at the source. NIST SP 1500-1r2, Big Data Interoperability Framework: Definitions, the NIST glossary entry for big data, Apache Hadoop, the Apache Airflow documentation.
Related work and checklists. The ETL engine case study, delivered by NeoSOFT, the production readiness checklist, the production readiness audit, and what custom software costs.
Ready to talk? Tell us which sources the data comes from, which parts of it have to be read in real time and which cloud you run on, and we will tell you which parts of the work are known and which are discovery. Talk to our engineers.
Selected Project Examples
Unlock the Power of Analytics and Robotic Process Automation (RPA) with a Comprehensive Platform
A comprehensive Analytics and Robotic Process Automation (RPA) platform designed to deliver actionable insights while automating high-volume manual processes. The solution combined advanced analytics, intelligent data management, and RPA capabilities to streamline operations and accelerate decision-making.
Data engineers, analytics specialists, and RPA experts leveraged Artificial Intelligence (AI), business intelligence, and domain expertise to help stakeholders gain real-time visibility into operations, reduce operational overhead, and improve overall business performance.
A Solution That Transfers Customer and Transaction Data to SAP C4C for Enhanced Big Data Analysis and Improved Customer Services
Detailed requirement analysis and close work with stakeholders to fully understand business goals and data flow requirements. The solution involved rigorous testing, validation, and deployment planning to ensure reliability at scale.
Java schedulers, RESTful APIs, and message-based integrations enabled seamless data movement from multiple sources into SAP C4C. This streamlined analytics workflows, improved customer data accessibility, and enhanced service quality by ensuring timely and accurate insights across customer touchpoints.
Harness the Power of Big Data: A Robust Solution That Delivers Valuable Insights for Budget Forecasting, Sales Analysis, and Profit Reporting
A scalable big data solution capable of processing large and diverse datasets while maintaining data quality and performance. The integrated data engine consolidated information from multiple sources to support budget forecasting, sales performance analysis, and profit reporting.
The work included building ingestion pipelines, developing processing and consumption layers, and implementing validation checks at every stage. The result was a reliable analytics platform that empowered business teams with accurate insights, improved forecasting accuracy, and data-driven financial decision-making.
