Big Data Services

Turn Your Data into a Valuable Business Asset!

Enter Big Data

With more than a decade of experience, Romexsoft can deliver actionable insights for you to take respective business decisions and move your company forward. Our big data services will help you stay ahead of the competition, take away the guesswork out of your consumer-driven decisions, propel your revenue growth and improve the overall operations efficiency.

Only 23% of enterprises manage to make use of just the three quarters of all the Big Data at their disposal. You can scrutinize and analyze your customers, revenues, and operations by hand all you want. But if you lack systems to operationalize all that raw data, and your current tools can’t uncover the full picture – there is a great chance you won’t be able to reach your business goals as quickly as your competitors, who do take advantage of the big data insights. We at Romexsoft could help your organization to make data-driven decisions at a moment’s notice.

Reach out to a Big Data Consultant

Services We Offer

Big Data Analytics

Execute your analytical workloads at petabyte scale

Stream Processing

Build scalable fault-tolerant streaming applications

Data Visualization

See-through your data, Build dashboards, Visualize your KPIs to help your business

Optimization & Support

Reduce your end-of-month check on infrastructure

Data Integration

Collect data from various data sources

  • Batch processing
  • Real-time streaming data processing

Process structured data

  • Process your streaming data incrementally and continuously to compute business decisions real-time
  • Process multiple streams of data in parallel

Big Data Warehousing

Build cost-effective and scalable Big Data warehousing

Platform Design and Strategy

  • Organize your data around your needs
  • Identify various sources of structured and unstructured data
  • Define valuable business cases
  • Implement analytics frameworks
  • Implement custom dashboards and alerts

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Big Data and Analitics Use Cases

What is 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.
big data as a service

Big Data Analytics as a Service for Business Intelligence

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 multiple sources. You simply share the access to your data – and we can start analyzing it instantly.
  • Spark’s encapsulation of powerful AI algorithms that run their Machine Learning module allows distributed data processing, and functions seamlessly with real-time data 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.

big data solution providers

Why do you need Big Data Analytics for your business?

  • Helps your organization to make data-driven decisions
  • Builds a unified analytics platform
  • Integrates all your data sources into a single pipeline and storage layer
  • Grows your current data warehouse beyond its limits
  • Saves money on infrastructure in your end-of-the-month bill
big data analytics

Our Big Data Expertise for Industries

We offer DSaaS Solutions and Big Data consulting services for the following Industries:

  • IoT
  • E-commerce
  • Finance and Insurance
  • Healthcare
  • Media & Broadcasting – News Portals included
  • Telecommunications
  • Travel and Transportation
  • Manufacturing
  • AdTech
  • Renewables
  • And more
big data analytics solution

Tools & Frameworks

Open-source stack

  • Hadoop – the baseline of the BigData world, HDFS
  • Spark – state-of-the-art unified analytics platform
  • Kafka – industry-standard messaging & data integration platform
  • ElasticSearch – scalable full-text search engine
  • Zookeeper – synchronization point for all the “zoo” of BigData
  • Presto – centralizes your analytical workloads
  • MemSQL – real-time in-memory relational database
  • Tableau – industry standard for BI
  • Storm – an old one, but hell of a stable streaming framework

AWS stack

  • S3 – Amazon Simple Storage Service
  • EMR – managing Hadoop & Spark workloads
  • Redshift – lightning-fast analytics at the petabyte-scale
  • Athena – the entry point to your big-data warehouse
  • Glue – managed ETL service
  • QuickSight – visualizes your data while seamlessly integrating with other AWS native services
  • Kinesis – AWS native data streaming

Big Data Services FAQ

What are Big Data analytics services?

Big Data solutions are applied to make sense of different kinds of data gathered from various data sources, including structured, semi-structured, and unstructured data, enabling businesses to put this data into action and make decisions based on empirical evidence. Big Data analytics companies provide big data analytics consulting, offering solutions for designing infrastructure to aggregate and analyze data sets. They encompass building solutions to collect data from different sources, data integration, visualization, warehousing, solutions to streamline data workflows.

What does Big Data Analytics include?

Big Data analytics includes a review of data needs and the designing of an appropriate Big 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 Big Data processing to cover the demands of specific businesses.

What are the types of Big Data analytics solutions?

There are four types of Big 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.

What is the difference between Big Data and Data Science?

Big Data and Data Science 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 & modeling techniques to process them efficiently. Data Science 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, artificial intelligence, etc. We can say that the Data Science concept has originated from Big Data.

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