Back to the main menu
Big Data Services
With practical experience in 30+ domains, ScienceSoft provides big data development, consulting, support and maintenance services. We guarantee a safe project start with a feasibility study and a PoC as well as optimal development costs thanks to our mature processes.
Big data services are aimed at helping companies handle massive-scale data for smooth software operation and reliable analytics insights. With 11 years of experience in big data, ScienceSoft provides full-scope big data services. We also apply our experience in AI/ML, data science, business intelligence, and data visualization to maximize the value of our customers' big data initiatives.
Select Your Case
I need a solution to store and analyze large amounts of data from multiple sources
We build systems that consolidate enterprise-wide data in a centralized location optimized for analytics querying and reporting and serve as a single point of truth.
I need to leverage big data to automate business or production operations or get real-time insights
We will build software that supports thousands of requests in real time and enables continuous operations monitoring, automated action triggers, and alerts (e.g., financial fraud detection and transaction blocking, remote patient monitoring, real-time inventory optimization).
I’m need to plan/develop/upgrade an XaaS app handling data from thousands of users
We build ecomemrce, ride-sharing, streaming, dating, gaming, social media, and other apps that enable user-specific real-time recommendations, dynamic pricing, and other personalization features and preserve stable performance under any workload.
About ScienceSoft
Our customers enjoy big data solutions that are powered by high-quality data and are tailored to the specific needs of their domain.
With an MVP, our customers gain profit from their big data solutions early on and benefit from improvements that are based on user feedback.
In-house PMO to support large-scale and distributed projects. Expertise in Lean, Agile, and DevOps methodologies. PM certifications (PSM I, PMP, and more).
We choose tools and services to deliver architectures with an optimal performance-to-cost ratio in each particular case.
- We hold partnerships with Microsoft, Amazon, Oracle, and other tech leaders to keep pace with the technological advancements and the evolution of the data analytics landscape.
- An expert team of architects, developers, DataOps engineers, ISTQB-certified QA engineers, data scientists, project managers, and business analysts with 5–20 years of experience.
- A quality-first approach based on a mature ISO 9001-certified quality management system.
- ISO 27001-certified security management based on comprehensive policies and processes, advanced security technology, and skilled professionals.
- Transparent and flexible pricing.
- We collaborate with companies from 70+ countries. Some of our prominent clients include:
Select Your Service Option
Big data consulting
You will get assistance for end-to-end big data solution implementation or for separate stages of your IT initiative. You can count on us to deliver a business case (e.g., to verify solution feasibility, create a competition strategy), estimate costs and ROI, design an architecture and recommend an optimal tech stack. We also provide consulting on achieving full security and regulatory compliance and implementing ML/AI-powered capabilities.
Big data implementation
You will get a system that automatically scales up and down depending on the load, smoothly fits your existing infrastructure, and is easy to upgrade in the future. We will choose techs that will enable the required performance at an optimal price. For highly complex cases, we can start with a Proof of Concept (PoC) or an MVP. This way, you can make sure of the solution’s feasibility and interact with an intermediate version of the software, provide your feedback, and thus let us adjust the system early on.
Improvement of a big data solution
You can turn to us to fix software inefficiencies or expand it with new capabilities. Our team will audit your system and introduce the required changes or provide you with actionable recommendations on their implementation. E.g., we can customize and configure big data infrastructure techs (like Hadoop, Kafka, Spark, NiFi, Cassandra, and MongoDB) and modernize data processing pipelines to improve solution performance, add/upgrade data encryption mechanisms to eliminate security vulnerabilities, enhance containerization to improve scalability, and more.
Support and maintenance of a big data solution
We can provide you with infrastructure support, solution administration, data cleansing, and other required support and maintenance services. Depending on your choice, you can request either one-time assistance or have our team to continuously monitor your software and fix and prevent issues.
See How Big Data Can be Used in Your Industry
Healthcare
- Healthcare data analytics with insigths into patient and financial management, treatment processes and outcomes, and more.
- Remote patient monitoring.
- Smart medical devices and hospital asset tracking.
- Recommendations on care personalization.
- Data analytics for clinical trials.
Manufacturing
- Manufacturing data analytics with insights into OEE and production optimization, quality control management.
- Real-time equipment monitoring and predictive maintenance.
- Real-time asset tracking.
- SCM and fleet management analysis.
Banking
- Banking analytics with continuous monitoring of an institution’s stability indicators, operational and marketing management insights, and more.
- ML/AI-powered recommendations for customer service personalization.
- Continuous market monitoring, what-if modeling, and forecasting.
- Transactions monitoring with alerting on risk-incurring events (e.g., identity theft, money-laundering activity).
Lending
- Lending analytics to provide insights into loan underwriting, loan servicing, and portfolio performance.
- Borrower creditworthiness evaluation.
- AI-powered planning and execution of debt collection.
- Loan demand and profitability forecasts.
- Real-time tracking of portfolio transactions.
- Lending fraud detection and alerting.
Investment
- Investment analytics to track portfolio performance, get insights into operational processes, and more.
- Financial returns forecasts.
- ML/AI-driven stock buying recommendations and investment prescriptions.
- Continuous monitoring of market, liquidity, and credit risks.
- Identification of pump and dump schemes, insider trading, HFT manipulation, and other investment fraud.
Insurance
- Insurance analytics to provide insights into sales, customer, claim, and finance management.
- Insurance automation, including claim processing and underwriting.
- Predictions of insurance events and what-if modeling for the required business processes.
- ML/AI-powered prescriptions (e.g., on claim denial or approval, optimal insurance prices).
- Detection of fraudulent transactions and their automated blocking.
Retail and ecommerce
- Retail analytics and ecommerce analytics with identification of customer behavior patterns and preferences, insights into financial management, and more.
- Real-time personalized recommendations for online customers.
- Merchandising analytics and face recognition for personalization in physical stores.
- Dynamic price optimization.
- Multi-echelon inventory optimization.
- Inventory counting with computer vision.
Transportation & logistics
- Data analytics for informed planning of operational capacity, customer delivery schedules, personel shift schedules, and more.
- Predictive fleet maintenance with recommendations on optimal actions.
- Real-time tracking of vehicle and cargo condition.
- Dynamic route optimization based on weather conditions and traffic load.
Telecommunications
- Customer analytics, including CLV calculation, churn prediction, and behavior modeling.
- Optimization of network infrastructure performance.
- Predicitve asset maintenance.
- Billing fraud and edenitty theft identification.
- Large-scale OSS/BSS, VoIP, and IPTV systems.
Oil & gas
- Big data for oil & gas exploration management.
- Estimated ultimate recovery forecasting and reservoir simulation.
- Remote asset monitoring (e.g., of drilling equipment, storage tanks).
- Preventive asset maintenance.
- Real-time production monitoring and optimization.
- Refinery quality control.
- SCM analytics.
- Energy and utilities data analytics to get insights into utility network performance, customer resource usage patterns, financial management, SCM operations, and more.
- Automated network load balancing.
- Resource demand forecasting.
- Predictive asset maintenance.
- Continuous sustainability monitoring.
- Detection of meter manipulation fraud.
- Smart thermostats.
- Real estate data analytics and visualization.
- Customer-property management.
- Property value forecasts.
- Sensor-powered predictive property maintenance.
- AI-powered personalization of marketing content in real time.
- Continuous monitoring of construction projects progress.
Professional services
- Analytics solutions for professional services companies to optimize project, customer, finace, marketing, and HR management.
- Continuous project health monitoring with forecasts and recommendations for resource allocation.
- NLP-powered customer sentiment analysis.
- Custom service quoting.
- ML/AI-powered recommendations for service personalization.
Education
- Analytics solutions for educational institutions to track enrollment, student performance, financial management, etc.
- Forecasting students’ performance and enrollment numbers.
- AI-powered personalized learning assistants.
- Facilities monitoring with insights into usage patterns and maintenance requests.
- Analyzing data from surveillance cameras.
Media and entertainment
- Analytics for M&E companies with insigths into content performance, finance management, and other business aspects.
- Personalized content recommendations generated in real time.
- Content performance forecasts.
- Detection of pirate content.
- GenAI capabilities for creating and optimizing content.
- Large-scale video and audio streaming, gaming, dating, and other entertainment platforms.
Automotive
- Real-time production monitoring and optimization.
- Continous quality control.
- Insights into driver bahvior and road condition.
- Predicitive maintenance for vehicles and manufacturing equipment.
- Connected car services and autonomous vehicles.
- ADAS.
Agriculture
- Precision farming.
- Real-time livestock monitoring.
- Environmental conditions tracking.
- Crop yield optimization.
- Disease and pest management.
- SCM analytics.
Public services
- Smart city solutions.
- Traffic management optimization.
- Energy, resource, and waste management.
- Predictive maintenance of city infrastructure assets.
- Optimization of public transportation routes and schedules.
- Data-driven crime prevention.
Travel and hospitality
- Predicting demand for travel, hotel, and other services.
- Real-time personalized recommendations for customers on destinations, lodging options, events, etc.
- Real-time pricing optimization.
- Dynamic customer-specific marketing content adjustment.
Construction
- Predicting potential delays and risks in construction projects.
- Continuous monitoring of construction project health with alerts on budget and deadline deviations.
- Remote equipment monitoring and control.
- Preventive asset maintenance.
- Supply chain optimization.
Marketing and advertising
- Real-time personalization of marketing offers and materials.
- Marketing channels attribution modelling.
- A/B testing of marketing campaigns.
- Churn risk prediction and prevention.
- GenAI solutions to create and optimize marketing content.
Our Selected Big Data Projects
13 results for:
Our Customers Say
We needed a proficient big data consultancy to deploy a Hadoop lab for us and to support us on the way to its successful and fast adoption.
ScienceSoft's team proved their mastery in a vast range of big data technologies we required: Hadoop Distributed File System, Hadoop MapReduce, Apache Hive, Apache Ambari, Apache Oozie, Apache Spark, Apache ZooKeeper are just a couple of names. Whenever a question arose, we got it answered almost instantly.
We commissioned ScienceSoft to audit and upgrade our partially developed AI-based software for clay pigeon shooting tracking.
As a result, the system could track a flying target in a real-life outdoor environment and faultlessly detect shooter’s performance. We are satisfied with our cooperation with ScienceSoft and their skilled development team, which smoothly fit into our project. In case of further system evolution, we’ll continue our collaboration and won’t hesitate to recommend ScienceSoft as a reliable development partner.
ScienceSoft has delivered cutting-edge solutions to complex problems bringing in innovative ideas and developments.
ScienceSoft follows specifications very rigidly, requiring clear communication about intended functionality. My final comment about ScienceSoft reflects their dedication to handle any problem that occurs as a result of hardware or software issues; simply put, they will go the extra mile to support their customers regardless of the time of day these issues arise.
That’s What Your Solution Will Be Like
We don’t know yet what solution you would like to develop, but we can definitely say it will be:
Future-proof
You will get a flexible and efficient big data system that is easy to scale and evolve in the long run. We will provide you with exhaustive software documentation to streamline software maintenance and are ready to stay with you for long-term solution support or train your internal team.
Secure
Relying on our ISO-27001-certified security management system and 21 years of experience in cybersecurity, we can establish reliable protection of your big data solution and ensure its full compliance with any required regulations.
Rigorously tested
We develop a tailored QA strategy to ensure smooth software operation and its unfailing performance even under high data load. We also implement a feasible share of test automation, which helps us to reduce testing costs by up to 20%.
Estimate the Cost of Big Data Services
Please answer a few simple questions to let our experts understand your project specifics and give you a tailored pricing estimation.
Our team is on it!
ScienceSoft's experts will study your case and get back to you with the details within 24 hours.
ScienceSoft USA Corporation Is a 3-Year Champion in the Financial Times Rating
Three years in a row (2022–2024), the Financial Times has included ScienceSoft USA Corporation in the list of 500 fastest-growing American companies. This is the result of our dedication to driving project success despite any constraints and disruptions.
Big Data Deployment: Cloud or On-Premises?
Nowadays, cloud deployment is the default option for big data: it’s cheaper and easier to set up, scale, and maintain. But let’s say you operate in a strictly regulated field and have a massive list of privacy requirements — if you need complete control over your data, you’d want to own the physical servers. And on the contrary, some app infrastructures are just too large or dynamic to maintain on your own. If you have unpredictable load spikes or a rapidly growing user base, it’s much safer — both financially and operationally — to let Microsoft or Amazon handle them. There are dozens of other essential factors that differ even between the largest cloud vendors (like data availability, processing speed, and redundancy), so the final choice will always depend on your particular needs.
Technical Components of a Big Data Solution We Cover
- A bus layer or aggregation layer collects data from various sources, handles event sequencing, timestamping, and routing.
What are the sources of big data?
- Internal big data sources: customer-facing apps, ecommerce platforms, enterprise systems like CRM, ERP, EHR.
- External big data sources: data from stock exchanges, banks, and credit companies, weather-forecasting services, online marketplaces, web tracking tools, GPS systems and traffic cameras, social media platforms, etc.
HIDE
- A data lake stores collected raw data of all types.
What are the types of big data?
There are three main types of big data:
- Structured data: it can be easily organized in tables, e.g., customer demographics data, financial transactions, and sales. Such data is easy to sort for further queries via BI tools.
- Unstructured data can't be organized into any logical structure until it is processed with complex technologies like AI, ML, natural language processing (NLP), and optical character recognition (OCR). The examples of unstructured data include texts, images, videos, and audio recordings. E.g., a company can apply NLP to customer social media posts to understand the sentiment towards the service.
- Semi-structured data is in between the two previous types. On the one hand, its elements can be assigned to certain fields or tags, but on the other hand, these elements are not always ready for querying or analytics. An example of semi-structured data can be an email with a subject line and a message body, where the line and the text will go to the correspondingly tagged fields and later be processed with techniques required for unstructured data.
HIDE
- A batch processing layer extracts data from the data storage in a scheduled manner (entails the latency from minutes to hours) and transforms it into analyzable formats to be further processed by the analytics layer.
- A stream processing layer captures real-time data and handles real-time in-memory processing (entails latency from milliseconds to seconds).
- A serving component (a data warehouse) stores processed data.
- A big data governance layer handles data auditing, security, quality, cataloging, metadata management, etc.
Big Data Technologies We Use
Here’s the list of technologies most frequently used in our big data projects. Click on the icon to find out more about our experience in a particular technology.
Our Big Data Customers Are Also Interested In
ScienceSoft combines big data expertise with decades-long experience in other advanced technologies to deliver end-to-end big data applications that bring maximum value to their users.
Building highly accurate ML models that identify hidden patterns in big data, provide reliable forecasts, power complex neural networks, and automate complex business algorithms.
Developing personalization engines, natural language processing systems, computer vision, and other AI-powered solutions that maintain stable performance under any data load.
Providing strategic and technological guidance in wrangling, exploring, and applying data, we employ reliable statistical methods, establish robust data quality management processes, and help avoid issues related to inaccurate data and false predictions.
Integrating large volumes of high-velocity data into scalable, fault-tolerant analytics solutions that provide trustworthy insights to any number of users.
Creating easy-to-navigate, customizable reports and dashboards that are tailored to the needs of specific business users and provide a clear and concentrated view of data insights that matter most.
Proficient in Azure, AWS, and GCP, we build cloud big data solutions from scratch and migrate legacy workloads to the cloud to achieve better scalability, cost-efficiency, and availability of our customers’ data.
Frequent Questions About Big Data Services, Answered
How much does big data implementation cost?
Big data implementation costs may vary from $200,000 to $3,000,000 for a mid-sized organization. The pricing depends on such factors as the number of data sources, data volume and complexity, data processing specifics (batch, real-time, or both), requirements for security and compliance, deployment model.
What are the types of big data?
There are three main types of big data:
- Structured data: it can be easily organized in tables, e.g., customer demographics data, financial transactions, and sales. Such data is easy to sort for further queries via BI tools.
- Unstructured data can't be organized into any logical structure until it is processed with complex technologies like AI, ML, natural language processing (NLP), and optical character recognition (OCR). The examples of unstructured data include texts, images, videos, and audio recordings. E.g., a company can apply NLP to customer social media posts to understand the sentiment towards the service.
- Semi-structured data is in between the two previous types. On the one hand, its elements can be assigned to certain fields or tags, but on the other hand, these elements are not always ready for querying or analytics. An example of semi-structured data can be an email with a subject line and a message body, where the line and the text will go to the correspondingly tagged fields and later be processed with techniques required for unstructured data.
What are the sources of big data?
Internal big data sources: customer-facing apps, ecommerce platforms, enterprise systems like CRM, ERP, EHR.
External big data sources: data from stock exchanges, banks, and credit companies, weather-forecasting services, online marketplaces, web tracking tools, GPS systems and traffic cameras, social media platforms, etc.
Is your data big?
The big data term is tricky, as it is seemingly limited to data volume. Your data can deserve the status due to many other factors. Take our simple quiz to find out!
Do you need to process unstructured data (e.g., texts, images, videos, audios)?
Does your data arrive constantly, at short intervals — up to every 10 minutes?
Should your data be processed as soon as it arrives?
Does your solution feature real-time functionality (e.g., immediate notifications to users, fraud detection alerts, automated IoT action triggers)?
Does your business experience constant data and user volume growth?
Please tell us a bit more about your needs
Answer at least 3 questions to get results.
Looks like big data technologies will be a true value driver for you
It's likely that your solution will significantly benefit from big data techs. Tell ScienceSoft's experts about your needs and goals, and we'll be glad to help you with your IT initiative.
Looks like your data is not "big" yet
Looks like traditional technologies will suffice to enable efficient data management in your case. However, you have landed on a big data page, which makes us assume you are at some step of a demanding IT initiative and are looking for expert knowledge and assistance. ScienceSoft will be glad to help — just drop us a line!