Big Data Services
Our Big Data Services help businesses collect, store, process, and analyze complex data sets that standard data-processing tools cannot easily handle. We assist organizations with distributed storage, real-time processing, and ML to extract valuable insights from their data.
Big Data Services We Provide
We provide Big Data Services that cover planning, system setup, infrastructure management, and data analytics. These services support your business in using its data more effectively to make informed decisions, improve operations, and stay competitive.
Big Data Consulting
Our experts align each step, from big data strategy to infrastructure and analytics, with your business goals and technical needs. We help to choose the right tools and architecture to support scalable, efficient big data solutions.
Big Data Analytics
We turn raw data into meaningful insights by applying statistical analysis, data mining, business intelligence and advanced pattern recognition techniques to uncover hidden trends and forecast outcomes.
Data Processing
Ingesting data from various sources and transforming it through batch and stream processing enables real-time insights, faster workflows, and more efficient use of large-scale, high-volume datasets.
Data Visualization
Turning large-scale data into interactive dashboards and visual reports allows teams to explore trends, monitor metrics, and communicate insights clearly across the organization.
Data Storage Solutions
Storing large volumes of structured and unstructured data in scalable environments such as data lakes and warehouses ensures secure, efficient access for processing, analysis, and long-term use.
Data Governance and Security
Ensuring proper governance and security involves setting access controls, maintaining data quality, and complying with regulations to protect sensitive information and support trusted analytics.
Big Data Development
Building custom software tailored for big data workloads enables efficient data collection, processing, and analysis while integrating seamlessly with existing systems and supporting specific business needs.
Data Science and ML
Applying data science and machine learning techniques helps uncover patterns, build predictive models, and extract insights from large, complex datasets. These models enable automation, forecasting, and efficiency.
Big Data Integration and Engineering
Combining data from diverse sources and building robust pipelines ensures consistent, reliable, and scalable data flow across systems, laying the foundation for accurate analysis and reporting.
Benefits of Using Big Data
Today, companies deal with large and complex data from numerous sources. Managing and using this data efficiently requires the right tools, systems, and support. The points below explain how a big data solution can assist in that process.
Have a Talk with Our Big Data Expert
We can help you to gather big data from multiple sources, unify it, and extract valuable insights from previously unused data assets.
Explore our case studies where Big Data played a supporting role in broader cloud and insight-based projects.
Why Choose Romexsoft as Your Big Data Services Company
Our team delivers comprehensive big data services with deep expertise in scalable cloud-based architectures, we ensure your data is structured, accessible, and ready to drive informed decisions.
Customer-first Culture
Every engagement is shaped by your goals, timelines, and feedback, ensuring a partnership built on trust and measurable results.
Scalable, Cloud-Native Solutions
We specialize in designing cloud-native architectures that scale with your data and business needs.
End-to-End Expertise
Our team combines expertise in data engineering, analytics, and cloud infrastructure to deliver secure, efficient projects aligned with your business goals.
Industries We Support with Big Data Services
We offer BSaaS solutions and Big Data consulting for the following industries.
Our Big Data Implementation Process
Implementing a Big Data solution requires a structured, step-by-step approach to ensure reliability, scalability, and business value. Our process covers every stage, from defining the right strategy to operationalizing insights, so you can turn unstructured data into actionable outcomes with confidence.
The process begins with a deep dive into your business objectives, existing infrastructure, and data challenges. We work closely with stakeholders to define a tailored big data strategy, select suitable technologies, and outline a roadmap that aligns technical execution with business outcomes. This phase sets the foundation for a scalable, efficient, and value-driven data environment.
We collect data from diverse sources – internal systems (e.g., CRM, ERP), external APIs, logs, IoT devices, and more. Our team implements secure, high-throughput ingestion pipelines that support both batch and real-time data streams. This ensures that incoming data is captured reliably, with minimal latency and proper formatting, while maintaining consistent data management practices for downstream processing.
Based on your performance, scalability, and access requirements, we design a robust storage architecture. This may include centralized data repository for raw and semi-structured data, and data storages for structured, analytics-ready datasets. Our big data solutions ensure high availability, cost-efficiency, and seamless integration with processing and analytics layers.
Using state-of-the-art frameworks like Spark, we process large-scale data in both batch and stream modes. The goal is to clean, enrich, and transform collected data into usable formats that support real-time insights and historical analysis. Processing workflows are optimized for speed, fault tolerance, and scalability.
We apply advanced analytics techniques, including statistical modeling, data mining, and ML, to extract actionable insights from the processed data. This step helps uncover patterns, predict trends, and support use cases such as customer segmentation, anomaly detection, and forecasting—driving measurable business value.
To make insights accessible and understandable, we build interactive dashboards, reports, and visual analytics tools. These visualizations are tailored to your users and use cases, enabling teams to monitor KPIs, explore trends, and share findings effectively across departments.
We deploy models and data workflows into production environments and set up automated monitoring systems. These include alerts, performance tracking, and feedback loops to continuously assess data quality, model accuracy, and system reliabilitys companies to gather big data froensuring long-term success and adaptability of your big data solution.
Big Data Technologies We Use
Data Warehouse and Distributed Storage
Big Data Databases
Data Streaming and Stream Processing
Machine Learning
Frequently Asked Questions About Big Data
Earlier in the day, companies like Google, Yahoo, Facebook, and LinkedIn discovered the need of developing specific approaches for working with data at a bigger scale, which gave birth to particular programming models and toolsets. Nowadays, almost all of the middle- to big-sized businesses can benefit from applying Big Data methodologies.
The classical definition of Big Data is emphasized in the so-called 3 Vs of Big Data:
- Volume – when you have a big amount of data to store and/or process – on the Tera/Peta/..-byte scale.
- Velocity – when the speed of processing and sub-second latency from ingestion to serving matters.
- Variety – when you have a lot of metadata to manage and govern – imagine a relational database with thousands of tables of thousands of columns to catalog, and manage accesses to.
Data analytics includes a review of data needs and the designing of an appropriate data analytics workflow accordingly. Concrete approaches are defined depending on your business focus and current situation. Roughly speaking, there are 4 interconnected and codependent stages involved in Big Data processing: aggregation, analysis, processing, and distribution. For each of these steps, certain tools & techniques are applied. Big Data experts build infrastructures and models for data processing to cover the demands of specific businesses.
There are four types of data analytics: descriptive, diagnostic, predictive, and prescriptive.
- Descriptive analytics is the most common and basic type, and is used to identify and “describe” general trends and patterns. It is mostly applied for the analysis of the company’s operational performance, translating these insights into reports or other readable forms.
- Diagnostic analytics is a more advanced type, which comes to play when you want to investigate the reasons for certain trends and behaviors. Predictive analytics, based on the data insights received from the historical and present data, forecasts future trends and behaviours.
- Prescriptive analytics relies on descriptive and predictive analytics results and based on them the best future decision for your business can be suggested. It can efficiently help you to find the right solution to the problem.
They are interconnected, yet they are not equal in meaning. By its concept, Big Data designates all data types, characterized by volume, variety and velocity, which are extracted from different sources and require special systems and modeling techniques to process them efficiently. AI-powered analytics is, in turn, a set of scientific activities applied to process big data for specific business goals, which requires expertise in a number of fields, like mathematics, computer science, statistics, AI, etc. We can say that its concept has originated from Big Data.
Apache Spark is a powerful and robust cluster computing tool, which its users access to:
- Parallel data processing on multiple computers in clusters, meaning skyrocket delivery speed.
- Working with any type of data storage – from file systems and SQL databases to various real-time streams coming from different sources. You simply share the access to your data – and we can start analyzing it instantly.
- Spark's encapsulation of powerful artificial intelligence algorithms that run their ML module allows distributed data processing, and functions seamlessly with real-time operations.
If you have piles of data, our DSaaS team will prepare it for the analysis, run the algorithms and present their findings whenever you need them.
Explore key concepts, technologies, and use cases that show how big data and analytics.
