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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 – ScienceSoft
Big Data Services – ScienceSoft

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 process thousands of requests in real-time

We will build low-latency software that will handle constantly arriving and often unstructured data (e.g., texts, images, audio, videos).

Solution examples:

  • Social media analytics solutions.
  • IoT systems for remote monitoring and control.
  • XaaS (e.g., streaming services, dating apps).

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I have large amounts of enterprise data that should be stored and analyzed

We will help aggregate your data generated by disparate data sources (e.g., financial and transactional data, customer demographics) and drive analytical insights.

Solution examples:

  • BI and reporting apps.
  • Data management platforms.
  • ERPs.

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About ScienceSoft

  • During 34 years in data analytics and data science, we have been satisfying companies’ diverse analytical needs (including the need for advanced analytics), which makes us fully understand the transformation you’re undergoing.
  • 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
  • In-house PMO to support large-scale and distributed projects. Expertise in Lean, Agile, and DevOps methodologies. Established project management practices to drive projects to their goals regardless of time and budget constraints.
  • 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:

ScienceSoft's Big Data Services

Big data consulting

  • Big data implementation/evolution strategies and detailed roadmaps.
  • Recommendations on data quality management.
  • Solution architecture design + an optimal technology stack.
  • User adoption strategies.
  • A proof of concept (for complex projects).
Big data consulting services

Big data implementation

  • Big data solution architecture design.
  • Solution development (a data lake, DWH, ETL/ELT setup, data analysis (SQL and NoSQL), reporting and dashboarding).
  • Setup of big data governance procedures (data quality, security, etc.)
  • Big data testing and QA.
  • Software modernization, evolution, redevelopment.
Big data implementation services

Big data support and maintenance

  • Big data solution infrastructure setup and support.
  • Solution administration.
    • Software updating.
    • Adding new users and handling permissions.
  • Big data management.
    • Data cleaning.
    • Data backup and recovery.
  • Solution health checks, performance monitoring, and troubleshooting.

Advanced big data analytics services

Our Selected Big Data Projects

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.

The Benefits of ScienceSoft’s Big Data Services

Industry-centric approach

With practical experience in 30+ domains, we speak your language, understand your unique challenges, and offer pragmatic solutions that fit your processes.

Optimized costs

We use our DevOps and Agile expertise to build efficient development processes, apply feasible test automation, and rightsize cloud resources to reduce cloud fees.

High degree of automation

We set up automated data governance and reporting procedures to eliminate manual work for your IT and BI teams and reduce the risk of human errors.

User-friendly UI

Enjoy the complete clarity of your big data dashboards: we build easy-to-read reports and responsive interfaces that easily adapt to users’ needs (e.g., sleek visuals for C-level presentations, in-depth data exploration for analysts).

Clean data for reliable insights

We establish robust big data quality management processes that ensure your data is always accurate, consistent, and complete to serve as a trustworthy source for analytics.

95%+ AI/ML model accuracy

We combine best-fit algorithms and create tailored data sets for model training, apply cross-validation to fine-tune hyperparameters, and enable self-learning for ML engines to deliver consistently accurate AI output.

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.


*What is your industry?

*How would you describe your current/target big data solution?

*What kind of help are you looking for regarding your big data initiative?

*What is your current/expected data volume?

*What growth of data volume do you expect during the next 12 months?

What is the nature of the data sources of your current/target big data solution?

Are you planning to implement/upgrade real-time processing capabilities?

Are you planning to add/upgrade AI/ML capabilities (e.g., for forecasting, intelligent optimization recommendations)?

*When are you ready to start your big data initiative?

Your contact data

Preferred way of communication:

We will not share your information with third parties or use it in marketing campaigns. Check our Privacy Policy for more details.

Our team is on it!

ScienceSoft's experts will study your case and get back to you with the details within 24 hours.

Our team is on it!

Big Data Use Cases ScienceSoft Covers

Industry-neutral big data use cases

Big data warehousing

  • Storing data about business processes, finances, resources, customers, etc. for analytical querying and reporting.
  • Corporate performance analytics.
  • Revenue, cost and investment analytics.
  • Predicting, forecasting, planning (performance, revenue, capacity, etc.) with all interdependencies.
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Operational analytics

  • Collecting, processing and storing large volumes of operational data (transactional data, production process data, asset data, employee data, plans, etc.)
  • Detecting deviations and undesirable patterns in a company’s operations (production processes, product distribution, etc.)
  • Recognizing bottlenecks (equipment failure, resource unavailability, etc.), conducting cause-effect analysis.
  • Forecasting (demand, capacity, inventory, etc.)
  • What-if scenario modeling and operational risk management.
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Industry-specific big data use cases


  • Analyzing manufacturing data (equipment year, model, sensor data, error messages, engine temperature, etc.) to predict equipment failures and the remaining useful time in real time.
  • Real-time monitoring of production processes, production equipment data, materials usage, etc., to identify factors leading to production time increase and delays for production optimization.
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  • Capturing, storing, and analyzing patient-related data (doctor notes, medical images, EHR/EMR data, R&D results, etc.).
  • Real-time patient monitoring and alerting on trends and patterns requiring the doctor’s attention.
  • Personalized care plans recommendations.
  • Mining claims data to identify fraudulent activity.
  • Forecasting the supply, demand, supplier risks, etc., to enable healthcare supply chain optimization and planning.
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Financial services

  • Analyzing integrated transactional data, interaction events, customer behavior in real time, identifying complex AML transactions, creating advanced risk models, etc., to identify potential fraud and fraud patterns.
  • Consolidating and analyzing data on assets and liabilities and conducting credit risk assessment, liquidity risk assessment, counterparty risk analysis, etc., to mitigate financial risks.
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Transportation and logistics

  • Tracking and analyzing real-time sensor data (cargo state, location, etc.) to make the delivery process fully transparent and ensure high-quality delivery of sensitive goods.
  • Analyzing driver behavior, maintenance needs, weather data, traffic data, fuel consumption data, etc., in real time to enable dynamic route optimization.
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Retail and ecommerce

  • Analyzing customer demographic data, data from mobile apps, in-store purchases, etc. to identify customer paths and behavior to optimize merchandizing, provide personalized product recommendations, discounts, etc.
  • Forecasting customer demand, analyzing the key attributes of past and current products/services and commercial success of their offerings, and using ML-driven recommendations to create new products/services.
  • Consolidating and analyzing data from social media, web visits, call logs, and more to personalize customer support, launch tailored customer retention campaigns, etc.
  • Analyzing customer transactions, spend patterns, predicting future customer actions with ML models to assess customer lifetime value, target marketing and sales offers to your best customers, etc.
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  • Analyzing log and sensor data from different types of equipment in real time and putting analytics results into operations to facilitate predictive equipment maintenance.
  • Analyzing drilling and production process data, data generated from seismic monitors, etc., to identify new oil deposits.
  • Analyzing sensor and historical production data and building ML-based predictive models to measure well production and understand the usage rate.
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  • Analyzing the network usage trends and patterns and using sophisticated models to forecast areas with excess capacity and optimize the network capacity.
  • Analyzing overall customer satisfaction, identifying customer churn patterns, and recommending the most relevant products/services to increase customer retention.
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Professional services

  • Customer segmentation to offer personalized services, enable automated customer-agent matching, and ensure effective ad targeting.
  • Financial data analytics to identify optimal service pricing strategies (based on competitors’ prices, market dynamics, historical data), detect revenue leakage, and improve profitability.
  • Operational data analytics to optimize resource allocation, improve employee performance and process efficiency, and boost service quality.
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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.

Head of Data Analytics Department, ScienceSoft

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

Want to Harness Big Data for Your Business Needs?

ScienceSoft helps companies fetch big data from a variety of sources, consolidate and analyze it to get valuable insights from previously untapped data assets.

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:

  1. 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.
  2. 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.
  3. 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!


Please tell us a bit more about your needs

Answer at least 3 questions to get results.

Go to questions

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.

Talk to the team

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!

Discuss my IT initiative